JP4296026B2

JP4296026B2 - Product demand forecasting system, product sales volume adjustment system

Info

Publication number: JP4296026B2
Application number: JP2003125753A
Authority: JP
Inventors: 弘行高橋; 信昭小塚; 宏輔石郷岡
Original assignee: Nomura Research Institute Ltd
Current assignee: Nomura Research Institute Ltd
Priority date: 2003-04-30
Filing date: 2003-04-30
Publication date: 2009-07-15
Anticipated expiration: 2023-04-30
Also published as: JP2004334326A

Description

【０００１】
【発明の属する技術分野】
本発明は、小売店が販売する商品の需要を予測するための技術に関する。
【０００２】
【従来の技術】
小売店の多くにはＰＯＳ（Point of Sales）システムが普及し、これを通じて集めた商品の売上データが様々に活用されている。その中で、売上データに基づいて商品の需要を予測して、効率的に発注を行う自動発注システムがある。
【０００３】
例えば、特許文献１には、商品の需要の変動要因を考慮して需要予測をし、予測結果に基づいて商品を自動発注するシステムが記載されている。
【０００４】
【特許文献１】
特開平１−２２４８６４号公報
【０００５】
【発明が解決しようとする課題】
しかしながら、特許文献１に記載されている自動発注システムが行っている需要予測は、過去データの単なる平均であったり、店長等の在庫管理者のノウハウに基づいた補正ルール等に基づいて補正を行ったりしている。つまり、変動要因がどの程度売上数を変動させるかが、店長の経験とカンに基づいて決定されている。
【０００６】
また、需要予測結果に基づく発注数も、店長等のノウハウに基づいた診断ルール等に基づいて修正を行って、決定している。つまり、システムが算出した発注数を店長の経験とカンで修正している。
【０００７】
このように、従来の自動発注システムでは、結局のところノウハウを有する者がいないと需要予測の精度を上げることができないし、さらには、適正な発注数を決定することもできない。
【０００８】
本発明は、ノウハウがなくても商品の需要予測ができるようにするための技術を提供することである。
【０００９】
本発明の別の目的は、商品の売上数を変動させる要因が売上数の変動に与える影響を定量化することである。
【００１０】
本発明のさらに別の目的は、商品の売上実績から変動要因による売上の変動の影響を取り除くことである。
【００１１】
【課題を解決するための手段】
本発明の一つの実施形態に従う商品の需要予測システムは、複数商品について、過去の商品別の売上数を示す情報を記憶した記憶手段と、前記記憶手段に記憶された前記商品別の売上数を示す情報に基づいて、前記売上数を変動させる変動要因が、前記売上数の変動に寄与する寄与度を商品ごとに算出する手段と、前記算出された商品別の寄与度に基づいて、各商品の売上数から前記変動要因による変動を取り除いて、前記商品別の売上数を調整する手段と、前記算出された商品別の寄与度および商品別の調整後の売上数に基づいて、将来の商品別売上数を予測する手段と、を備える。
【００１２】
好適な実施形態では、前記算出手段は、複数の前記変動要因について商品別要因別の寄与度を算出し、前記調整手段は、前記商品別要因別の寄与度に基づいて、各商品の売上数から各変動要因ごとの変動を取り除き、前記予測手段は、前記商品別要因別の寄与度および商品別の調整後の売上数に基づいて、将来の商品別売上数を予測する。
【００１３】
好適な実施形態では、前記変動要因は、商品の売価、商品を販売した日の天気、商品を販売した店舗数、商品を販売した日が休日であるか、および、商品を販売した曜日のうちのいずれか一つを含む。
【００１４】
好適な実施形態では、前記予測手段は、将来の予測対象日の商品別売上数を、前記予測対象日の販売条件に基づいて予測する。さらに、前記販売条件は、各店舗の商品別の予定売価、前記予測対象日の予測される天気、前記予測対象日が休日であるかどうか、および前記予測対象日の曜日のうちの一つ以上を含んでもよい。
【００１５】
好適な実施形態では、前記予測手段が事前に予測した予測対象日の商品別売上数と、前記予測対象日の実績の商品別売上数との比較結果に基づいて、前記寄与度を補正する補正手段をさらに備える。
【００１６】
本発明の一つの実施形態に従う商品の売上数調整システムは、一の商品の売価および前記売価での売上数を記憶した記憶手段と、前記売価および前記売上数に基づいて、前記売価が前記売上数の増減に与える影響を取り除いた売価調整済み売上数を算出する手段と、を備える。
【００１７】
好適な実施形態では、前記算出手段は、前記売価および前記売上数を統計的に処理して、売価と売上数との関係を求め、前記売価と売上数との関係に基づいて、前記各売価での売上数を、基準となる売価での売上数に換算する。さらに、前記売価と売上数との関係は指数関数で表され、前記指数関数で表される関係は、最小二乗法を用いた回帰計算により算出してもよい。
【００１８】
本発明の一つの実施形態に従う商品の売上数調整システムは、一の商品を販売した日の天気とその日の前記商品の売上数とを対応付けて記憶した記憶手段と、前記天気および前記売上数に基づいて、前記天気が前記売上数の増減に与える影響を取り除いた天気調整済み売上数を算出する手段と、を備える。
【００１９】
好適な実施形態では、前記算出手段は、前記天気および前記売上数を統計的に処理して、天気と売上数との関係を求め、前記天気と売上数との関係に基づいて、前記各天気での売上数を、基準となる天気での売上数に換算する。さらに、前記天気と売上数との関係は１次関数で表され、前記１次関数で表される関係は、最小二乗法を用いた回帰計算により算出してもよい。
【００２０】
本発明の一つの実施形態に従う商品の売上数調整システムは、複数の店舗における、一の商品の日別店舗別の売上数を記憶した記憶手段と、前記売上数に基づいて、各店舗の店舗別売上比率を求め、休業店舗がある日は、前記店舗別売上比率を用いて休業店舗の売上数を補償して、店舗調整済み日別売上数を算出する手段と、を備える。
【００２１】
本発明の一つの実施形態に従う商品の売上数調整システムは、一の商品の平日の売上数および休日の売上数を記憶した記憶手段と、前記平日の売上数および休日の売上数を比較して、休日の売上数を調整する手段と、を備える。
【００２２】
好適な実施形態では、前記調整手段は、平日に対する休日の売上数比率を求め、前記売上比率を用いて休日の売上数を調整する。
【００２３】
本システムの一つの実施形態に従う商品の売上数調整システムは、一の商品の日別の売上数および各日の曜日とを対応付けて記憶した記憶手段と、１週間の売上数の平均値と前記日別売上数とを比較して曜日別特性を算出し、前記曜日別特性に基づいて前記日別売上数を調整する手段と、を備える。
【００２４】
本システムの一つの実施形態に従う商品の売上数調整システムは、所定期間内の複数店舗における一の商品の売上実績を、日別、店舗別に記憶した記憶手段と、前記記憶手段から前記商品の売上数および前記商品の売価を取得し、前記売価が前記売上数の増減に与える影響を取り除いた、日別店舗別の第１の調整済み売上数を算出する売価調整手段と、前記記憶手段から前記商品を販売した日の天気を取得し、第１の調整済み売上数に基づいて、天気が前記商品の売上数の増減に与える影響を取り除いた、日別店舗別の第２の調整済み売上数を算出する天気調整手段と、前記記憶手段を参照して、営業店舗と休業店舗とを日別に識別し、第２の調整済み売上数に基づいて、店舗の休業が前記商品の売上数の増減に与える影響を取り除いた、日別の第３の調整済み売上数を算出する店舗調整手段と、前記記憶手段を参照して、休日と平日とを日別に識別し、休日の第３の調整済み売上数と平日の第３の調整済み売上数とを比較して、休日が前記商品の売上数の増減に与える影響を取り除いた、日別の第４の調整済み売上数を算出する休日調整手段と、前記記憶手段を参照して、各日の曜日を識別し、曜日別の第４の調整済み売上数に基づいて、各曜日が前記商品の売上数の増減に与える影響を取り除いた、日別の第５の調整済み売上数を算出する曜日調整手段と、を備える。
【００２５】
本発明の一つの実施形態に従う商品の需要予測システムは、一の商品の売上数から前記売上数の変動要因による変動の影響を除去した調整済み売上数を、過去の複数の日について記憶する手段と、前記過去の複数の日の調整済み売上数に基づいて、将来の調整済み売上数を予測する手段と、前記将来の調整済み売上数に前記変動要因による変動の影響を付加して、将来の売上数の予測値を算出する手段と、を備える。
【００２６】
好適な実施形態では、前記変動要因が前記売上数の変動に与える影響の度合いを示す寄与度を記憶する手段と、将来の販売条件を記憶する手段と、をさらに備え、前記算出手段は、前記将来の調整済み売上数、前記寄与度および前記販売条件に基づいて将来の売上数の予測値を算出する。
【００２７】
【発明の実施の形態】
以下、本発明を適用した実施形態について、図面を用いて説明する。
【００２８】
本発明の一実施形態に係る在庫管理システムは、複数の小売店における複数の商品の売上実績データに基づいて、小売店ごとに各商品の需要を予測する需要予測システム１と、この需要予測に基づいて各小売店に対して発注数量の提案を行う発注提案システム２と、小売店からの発注指示を受けて実際に商品の発注を行う発注システム３と、各小売店に設置されていて、発注提案を受け付けて発注要求を行う小売店システム４とを備える。
【００２９】
需要予測システム１、発注提案システム２、発注システム３および小売店システム４は、いずれも例えば汎用的なコンピュータシステムにより構成され、以下に説明する各システム１、２、３、４内の個々の構成要素または機能は、例えば、コンピュータプログラムを実行することにより実現される。
【００３０】
まず、需要予測システム１について、図２を用いて説明する。
【００３１】
需要予測システム１では、売上数調整部１１が売上データベース５に蓄積されている過去の店舗別、日別、商品別の売上実績データを取得し、各商品の売上数を変動させる様々な変動要因が売上数の変動へ寄与する度合いを、商品別に係数として求め、係数テーブル１２へ格納する。売上数調整部１１は、さらに、求めた各係数を用いて、それぞれの変動要因による売上数の増減の影響を取り除いた商品別の調整済み売上数を求める。調整済み売上数は、調整済み売上データベース１８に記憶される。ここで、各係数がそれぞれの変動要因を正確に反映していないこともあるので、係数補正部１４が調整済み売上数を分析し、必要に応じて係数を補正する。また、基準値予測部１３が、過去の調整済み売上数を用いて、将来の調整済み売上数に相当する基準値を予測する。そして、予測値算出部１５が、上述の処理で求められた基準値および補正後の係数を用いて、各商品別の売上数を予測する。
【００３２】
以下、需要予測システム１における個別の処理について詳細に説明する。
【００３３】
売上数調整部１１は、売上データベース５から売上実績データを取得して、売価および販売促進（以下、販促という）の効果を取り除く売価・販促調整（Ｓ１）、天気の影響を取り除く天気調整（Ｓ２）、休業している店舗の影響を取り除く店調整（Ｓ３）、休日（土曜、日曜および祝祭日）であることによる影響を取り除く休日調整（Ｓ４）および曜日別の影響を取り除く曜日調整（Ｓ５）を、この順序で行う。なお、これらの各調整処理は、順序を変更して行うことも可能であるし、一つ以上の調整処理を省略することもできる。
【００３４】
売上データベース５は、日別、商品別の各小売店での売上数などの情報を記憶する。例えば、売上データベース５は、図３に示すように、データ項目として商品を識別する商品コード５０１と、店を識別するための店コード５０２と、年月日５０３と、曜日５０４と、休日であるかどうかを示す休日フラグ５０５と、天気を示す天気コード５０６と、店のある地域を示す地域コード５０７と、店の規模を示す規模コード５０８と、商品の基本となる売値である基本売価５０９と、実際に商品が販売されたときの売価５１０と、その売価での売上数量５１１と、販促を行っていたかどうかを示す販促フラグ５１２とを有する。
【００３５】
天気コード５０６は、年月日５０３が示す日の、店コード５０２で示す店舗がある場所の天気を示すコードである。天気コード５０６は、例えば、「晴れ」、「晴れのち曇り」、「晴れのち一時曇り」などの１日の天気に対してコードが割り当てられている。
【００３６】
売上データベース５には、過去１２０日分および将来８日分のデータが記憶されている。過去のデータについては、すべて実績データが記憶されている。将来のデータについては、天気コード５０６は天気予報に基づいて設定され、売価５１０および販促フラグ５１２は各店が予定している内容が設定される。
【００３７】
次に、売上調整部１１が行うステップＳ１〜Ｓ５までの処理の詳細について説明する。
【００３８】
まず、売価・販促調整Ｓ１について、図４〜図７を用いて説明する。
【００３９】
売上数調整部１１は、売上データベース５の過去の全実績データを前処理して、売価集計データベース５１を生成する（Ｓ１１）。この前処理では、例えば、売上データベース５の所定のデータ項目について、商品コード、販促フラグおよび売上比率をキーにして集計する。売価集計データベース５１は、例えば、図５（ａ）に示すように、データ項目として、商品コード５１０１と、販促フラグ５１０２と、売価比率５１０３と、売上数５１０４と、度数５１０５とを有する。
【００４０】
ここで、売価比率５１０３とは、基本売価５０９に対する実際の売価５１０の比率であり、売価５１０が基本売価５０９と同じときに１となる。例えば、基本売価が１００円の商品を７０円で販売すると、売価比率は０．７である。
【００４１】
売上数５１０４は、売価比率５１０３ごとの過去１２０日間の全店舗の売り上げ数の平均である。
【００４２】
度数５１０５は、すべての店舗で、過去１２０日間に各売価比率５１０３で販売された延べ店舗数を示す。例えば、過去１２０日間に、２０店舗が商品Ｂを売価比率 “１”で販売した日数が１０日あったとすれば、売価比率５１０３が“１”の度数５１０５は“２００”とカウントされる。
【００４３】
図４に戻り説明を続ける。ステップＳ１１の前処理が終わると、売上数調整部１１は、売価集計データベース５１に記憶されたデータを統計処理して、売価と売上数との関係を求める。ここでは、各商品ごとに回帰分析を行い、回帰曲線を算出する（Ｓ１２）。回帰曲線算出の処理イメージを、図６を用いて説明する。ここでは、売上比率と売上数との関係を示す曲線を最小二乗法を用いて算出する。なお、ここでは計算を容易にするために売価を正規化した売価比率を用いているが、売価を用いて行っても良い。
【００４４】
まず、売価集計データベース５１に記憶された、販促を行っていないときのデータ、すなわち販促フラグ５１０２が立っていないデータについて、売価比率５１０３をｘ軸、売上数５１０４をｙ軸にとったｘ−ｙ平面上へプロット（■）する。このとき、各プロットは度数５１０５に応じた重み付けがなされる。ここで、式（１）に示す回帰曲線と、重み付けされたプロット群とを用いて、最小二乗法によりｂ₀およびｂ₁を算出する。
ｙ＝ｂ₀ｅｘｐ（−ｂ₁ｘ） …（１）
【００４５】
図４に戻り、ステップＳ１２で求めた回帰曲線を用いて、売価係数および販促係数を算出する（Ｓ１３）。
【００４６】
販促係数は、以下のようにして算出する、すなわち、販促を行っていないときの売価比率と売上数との関係を示す式（１）の両辺の対数をとり、式（２）を得る。
ｌｏｇ_eｙ＝ｌｏｇ_eｂ₀−ｂ₁ｘ …（２）
【００４７】
そして、Ｘ軸に売価比率、Ｙ軸にｌｏｇ_e（売上数）をとりグラフ化すると、図７に示す直線Ａが得られる。このＸ−Ｙ平面上に、売価集計データベース５１に記憶された、販促を行ったときのデータ、つまり販促フラグ５１０２が立っているときの売価比率５１０３と売上数５１０４との関係をプロット（□）する。
【００４８】
ここで、販促を行ったときの売価比率とｌｏｇ_e（売上数）との間にも、式（２）と同じ傾き（−ｂ₁）の直線関係があると仮定して、販促を行ったときの直線を示す式（３）を算出する。
ｌｏｇ_eｙ＝ｌｏｇ_eｋｂ₀−ｂ₁ｘ …（３）
ここで、切片ｌｏｇ_eｋｂ₀のｋを販促係数とする。
【００４９】
次に、売価係数の算出法について、図６を参照して説明する。まず、売価比率の中で度数が最も高いものを求める。つまり、売価比率５１０３をキーにして度数５１０５を集計したとき、最も大きな度数となる売価比率５１０３（以下、基準売価比率と呼ぶ）を求める。そして、基準売価比率を式（１）に代入し、そのときの売上数（以下、基準売上数と呼ぶ）を求める。そして、各売価比率の売価係数は、各売価比率の売上数の基準売上数に対する比率で表す。
【００５０】
例えば、本実施形態では、様々な売価比率のときの売上数を式（１）を使って求め、それぞれの売上数と基準売上数との比を、それぞれの売価係数として算出する。
【００５１】
図６を用いて、さらに具体的に説明する。図６の例では、基準売価比率は０．９であり、そのときの売上数（基準売上数）が１００であったとする。このとき、売価比率が０．９のときの売価係数は“１”である。さらに、売価比率が０．８のときの売上数が１１０であったとすると、そのときの売価係数は“１．１”となる。
【００５２】
本実施形態では、売価集計データベース５１に格納されているすべての売価比率５１０３について、上述の手順で売価係数を算出する。また、売上データベース５には、未来のデータ（予定の売価等）を記憶することが可能であり、未来にしか存在しない売価が売上データベース５にあるときには、これについても売価係数を算出することができる。
【００５３】
ここで求められた売価係数および販促係数は、売価・販促係数テーブル１２１に格納される。売価・販促係数テーブル１２１の例を図５（ｃ）に示す。すなわち、売価・販促係数テーブル１２１のデータ項目は、商品コード１２１１、売価１２１２、売価比率１２１３、売価係数１２１４および販促係数１２１５である。
【０００５４】
そして、売価係数および販促係数を用いて、それぞれの商品について売上数の売価・販促調整を行う（Ｓ１４）。例えば、売上データベース５に格納されている過去の全実績データの売上数５１１を、商品コード５０１および基本売価５０９と売価５１０とにより定まる売価比率に対応する売価係数１２１４で除算することにより、売価調整を行う。この売価調整は、各売価のときの売上数を基準売価での売上数に換算することに相当する。つまり、実際の商品の売上数は売価に大きく依存するが、この売価調整を行うことにより、売価変動に起因する売上数の増減効果を取り除くことができる。
【００５５】
また、販促フラグ５１２が立っているデータについては、上述の売価調整行った後に、さらに販促係数１２１５で除算して販促調整を行う。売価と同様に、売上数は販促を行ったかどうかで大きく変動するため、この販促調整により、販促を行わなかったときの売上数に換算して、販促による売上数の増減効果を取り除くことができる。
【００５６】
この結果、売上データベース５に記憶されている過去の全実績データについて、売価および販促調整が行われる。ここで得た売価調整済みの売上数、ならびに、売価および販促調整済みの売上数を調整済み売上数Ａと呼ぶ。
【００５７】
調整済み売上数Ａは、調整済み売上数Ａデータベース５２に記憶される。調整済み売上数Ａデータベース５２は、図５（ｂ）に示すように、商品コード５２１と、店コード５２２と、年月日５２３と、曜日５２４と、休日フラグ５２５と、天気コード５２６と、地域コード５２７と、規模コード５２８と、調整済み売上数Ａ５２９とをデータ項目として有する。
【００５８】
次に、天気調整Ｓ２について、図８〜図１０を用いて説明する。
【００５９】
ここでは、売価および販促調整済みの調整済み売上数Ａを用いて天気調整を行う。そこで、売上数調整部１１は、調整済み売上数Ａデータベース５２に記憶されている全データを前処理して、天気集計データベース５３を生成する（Ｓ２１）。この前処理では、例えば、調整済み売上数Ａデータベース５２の所定の項目について、商品コード５２１および天気コード５２６をキーにして集計する。
【００６０】
天気集計データベース５３は、例えば、図９（ａ）に示すように、商品コード５３１と、天気コード５３２と、天気比率５３３と、調整済み売上数Ａ５３４と、度数５３５とをデータ項目として有する。
【００６１】
ここで、天気比率５３３とは、太陽が地平線より上にある時間帯に、実際に太陽が出ていた時間の割合を、天気コードに基づいて０から１までを８段階に区分して表したものである。ここでは、例えば、天気コードが晴れを示す場合は天気比率を「１」、天気コードが晴れのち曇りまたは曇りのち晴れを示す場合は天気比率を「０．５」、天気コードが雨を示す場合は天気比率を「０」などとする。すなわち、天気によって太陽が出ていたと思われる大まかな時間は定まるので、天気コードが決まれば天気比率は一意に定まる。
【００６２】
調整済み売上数Ａ５３４は、各天気比率５３３のときの、過去１２０日の全店舗の調整済み売上数Ａの平均である。
【００６３】
度数５３５は、すべての店舗で、過去１２０日間に各天気比率５３３で販売された延べ店舗数を示す。例えば、過去１２０日間に、１０店舗が商品Ｄを天気比率“０．５”のときに販売した日数が２０日あったとすれば、天気比率５３３が“０．５”の度数５３５は“２００”とカウントされる。
【００６４】
再び図８を参照すると、売上数調整部１１は、天気集計データベース５３に記憶されたデータを統計処理して、天気と売上数との関係を求める。ここでは、天気集計データベース５３のデータを用いて各商品ごとに回帰分析を行い、回帰曲線を算出する（Ｓ２２）。この回帰分析処理について、図１０を用いて説明する。図１０は、ｘ軸に天気比率、ｙ軸に売上数をとったｘ−ｙ平面上で、天気比率と売上数との関係を示す直線を最小二乗法を用いて算出するときの処理イメージを示す。
【００６５】
図１０に示すｘ−ｙ平面上に、天気集計データベース５３のデータを、度数に応じた重み付けをしてプロットし、このプロット群を用いて式（４）のｃ₀およびｃ₁を最小二乗法を用いて算出する。
ｙ＝ｃ₀＋ｃ₁ｘ …（４）
【００６６】
図８に戻り、ステップＳ２２で求めた式（４）の直線を用いて、各商品について天気係数を算出する（Ｓ２３）。天気係数は、例えば、売価係数を算出するときと同様に、最も度数が高い天気比率（以下、基準点天気比率と呼ぶ）を求め、そのときの調整済み売上数Ａ（以下、基準調整済み売上数Ａと呼ぶ）を式（４）を用いて求め、この基準調整済み売上数Ａを基準にして定める。例えば、すべての天気比率に対応する調整済み売上数Ａを式（４）で求め、基準調整済み売上数Ａと、他の７段階の天気比率のときの調整済み売上数Ａとの比を求め、これをそれぞれの天気比率のときの天気係数とする。
【００６７】
図１０を用いて具体的に説明する。図１０の例では、最も度数が高い天気比率が０．６２５であり、そのときの調整済み売上数Ａ（基準調整済み売上数Ａ）が１００であったとする。このとき、天気比率が０．６２５のときの天気係数は“１”である。さらに、天気比率が０．５のときの調整済み売上数Ａが９０であったとすると、そのときの天気係数は０．９となる。
【００６８】
本実施形態では、天気集計データベース５３に格納されているすべての天気比率５３３について天気係数を算出する。また、売上データベース５には、未来のデータ（予定の売価等）を記憶することが可能であり、未来にしか存在しない天気比率が売上データベース５にあるときには、これについても天気係数を算出することができる。
【００６９】
ステップＳ２２で算出された天気係数は、天気係数テーブル１２２に格納される。天気係数テーブル１２２は、図９（ｃ）に示すように、商品コード１２２１と、天気コード１２２２と、天気係数１２２３とをデータ項目として備える。
【００７０】
次に、天気係数を用いて各商品の売上数の天気調整を行う。すなわち、天気調整は、調整済み売上数Ａデータベース５２の調整済み売上数Ａ５２９を、それぞれの天気コード５２６と対応する天気係数１２２３で除算して行われる。つまり、商品の売上数は天気により変動するが、この天気調整を行うことにより、天気が売上数の売上数の増減に与える影響が除去される。ここで求まった売上数は、売価および販促に加えて、さらに天気の影響も除去されたことになり、この売上数を調整済み売上数Ｂと呼ぶ。
【００７１】
調整済み売上数Ｂは、調整済み売上数Ｂデータベース５４に格納される。調整済み売上数Ｂデータベース５４のデータ項目は、例えば、図９（ｂ）に示すように、商品コード５４１と、店コード５４２と、年月日５４３と、曜日５４４と、休日フラグ５４５と、地域コード５４６と、規模コード５４７と、調整済み売上数Ｂ５４８とを有する。
【００７２】
次に、店調整Ｓ３について、図１１〜図１２を用いて説明する。
【００７３】
ここでは、売価、販促および天気調整済みの調整済み売上数Ｂを用いて店調整を行う。そこで、調整済み売上数Ｂデータベース５４に記憶されているデータを前処理して、店集計データベース５５を生成する（Ｓ３１）。この前処理では、例えば、調整済み売上数Ｂデータベース５４の商品コードと店コードとをキーにして集計する。店集計データベース５５は、例えば、図１２（ａ）に示すように、商品コード５５１と、店コード５５２と、地域コード５３３と、規模コード５５４と、調整済み売上数Ｂ５５５とをデータ項目として有する。
【００７４】
前処理後、売上調整処理部１１は、店集計データベース５５のデータを用いて、商品別に、店舗別の売上比率を示す店係数を算出する（Ｓ３２）。店係数の算出は、例えば、以下の式（５）を用いて、店ごと商品ごとに行う。
【００７５】
【数１】

【００７６】
また、売上調整処理部１１は、各店ごとに店係数を算出するとともに、それぞれの店を、各店の特性に応じて複数のクラスタのいずれかに分類する。例えば、各店の存在する地域を示す地域コード５５３、各店の規模を示す規模コード５５４、あるいは休日の売上金額などに応じてクラスタリングを行うことができる。これ以降のＳ３の処理およびＳ４、Ｓ５の処理は、クラスタ単位で行うこともできる。
【００７７】
ここで算出された店係数およびクラスタリング結果は、店係数テーブル１２３に格納される。店係数テーブル１２３は、図１２（ｃ）に示すように、商品コード１２３１と、店コード１２３２と、店係数１２３３と、クラスタ番号１２３４とを有する。
【００７８】
次に、ここで求めた店係数を用いて、調整済み売上数Ｂに対して商品別、日別に店調整を行う（Ｓ３３）。ここでは、休業した店があると、その日は全体の売上数が減少するため、店の休業による売上数の減少を補償する。この店調整により、調整済み売上数Ｂからさらに店舗の休業による売上変動が除去される。店調整後の売上数を調整済み売上数Ｃと呼ぶ。それぞれの日の調整済み売上数Ｃは、次の式（６）で得られる。
【００７９】
【数２】

【００８０】
なお、式（６）の右辺の分母の営業している店舗の店係数合計は、以下のようにして求める。すなわち、もともと売上データベース５には休業している店のデータが存在しないので、調整済み売上数Ｂデータベース５４の調整済み売上数Ｂ５４８を日別に集計する際、店コード５４２を抽出しておき、その店コード５４２の店係数１２３３を集計する。
【００８１】
これにより、調整済み売上数Ｃの算出対象となっている日に休業している店があったとしても、その店が休業でなかった場合に予想される売上数が求まる。
【００８２】
調整済み売上数Ｃは、調整済み売上数Ｃデータベース５６に格納される。調整済み売上数Ｃデータベース５６のデータ項目は、例えば、図１２（ｂ）に示すように、商品コード５６１と、クラスタ番号５６２と、年月日５６３と、曜日５６４と、休日フラグ５６５と、店係数合計５６６と、調整済み売上数Ｃ５６７とを有する。
【００８３】
次に、休日調整Ｓ４について、図１３〜図１４を用いて説明する。
【００８４】
ここでは、売価、販促、天気および店調整済みの調整済み売上数Ｃを用いて休日要因の調整を行う。そこで、調整済み売上数Ｃデータベース５６に記憶されているデータを用いて、商品別に休日係数を算出する（Ｓ４１）。休日係数は、過去１２０日にあるすべての休日（土曜、日曜および国民の祝日）について、以下の式（７）で算出する。
【００８５】
【数３】

平日とは休日以外の日を指す。
【００８６】
ここで算出した休日係数は、休日係数テーブル１２４へ格納される。休日係数テーブル１２４は、例えば、図１４（ａ）に示すように、商品コード１２４１と、クラスタ番号１２４２と、休日係数１２４３と、休日フラグ１２４４とを有する。
【００８７】
そして、休日フラグ５６５が立っている調整済み売上数Ｃ５６７に対して、休日係数を用いて商品別に休日調整を行う（Ｓ４２）。休日調整は、例えば、各休日の調整済み売上数Ｃを、休日係数で除算することによって行う。この休日調整により、調整済み売上数Ｃからさらに休日であることによる売上変動が除去される。休日調整後の売上数を調整済み売上数Ｄと呼ぶ。
【００８８】
調整済み売上数Ｄは、調整済み売上数Ｄデータベース５７に格納される。調整済み売上数Ｄデータベース５７のデータ項目は、例えば、図１４（ｂ）に示すように、商品コード５７１と、クラスタ番号５７２と、年月日５７３と、曜日５７４と、休日フラグ５７５と、店係数合計５７６と、調整済み売上数Ｄ５７７とを有する。
【００８９】
次に、曜日調整Ｓ５について、図１５〜図１７を用いて説明する。
【００９０】
ここでは、売価、販促、天気、店および休日調整済みの調整済み売上数Ｄを用いて曜日調整を行う。そこで、調整済み売上数Ｄデータベース５７に記憶されているデータを用いて、各曜日の売上の特性を示す曜日係数を算出する（Ｓ５１）。曜日係数は、以下のようにして算出する。
【００９１】
まず、売上データベース５に実績データが記憶されている過去１２０日のうち、最も過去の３日と最近の３日を除く１１４日分の調整済み売上数Ｄを対象に、移動平均をかけて曜日別の特性を除去する。
【００９２】
移動平均は、例えば、図１６に示すように、移動平均算出対象日およびその日の前後各３日分（対象となる日を含めて７日分）の調整済み売上数Ｄを用いて行う。つまり、この７日の調整済み売上数Ｄの平均値が、算出対象日の移動平均値となる。そして、対象となる日の調整済み売上数Ｄを、この移動平均値で除算して得た値を、曜日ごとに平均して曜日係数を算出する。
【００９３】
ここで求まった曜日係数は、曜日係数テーブル１２５に格納される。曜日係数テーブル１２５は、例えば、図１７（ａ）に示すように、商品コード１２５１と、クラスタ番号１２５２と、曜日１２５３と、曜日係数１２５４とを有する。
【００９４】
図１５に戻り、調整済み売上数Ｄに対して、それぞれの曜日に応じた曜日係数を用いて曜日調整を行う（Ｓ５２）。例えば、曜日調整は、調整済み売上数Ｄデータベースに記憶されている調整済み売上数Ｄ５７７を、それと対応する曜日５７４の曜日係数１２５４で除算して行われる。この結果、調整済み売上数Ｄからさらに、曜日による売上変動が除去される。ここで、曜日による売上変動が除去された売上数が、すべての売上変動要因を除去した調整済みの売上数である。そして、この調整済み売上数が調整済み売上データベース１８に格納される。
【００９５】
調整済み売上データベース１８は、図１７（ｂ）に示すように、商品コード１８１と、クラスタ番号１８２と、年月日１８３と、曜日１８４と、休日フラグ１８５と、店係数合計１８６と、調整済み売上数１８７とを、データ項目として有する。
【００９６】
以上に説明したように、売上数調整部１１は、売上データベース５に記憶されている実際の売上数（以下、調整前の売上数と呼ぶことがある）などのデータを用いて、様々な変動要因が売上数の変動に寄与する度合いをそれぞれの係数として求め、その係数を用いて各変動要因による売上数の変動を除去した調整済み売上数を求める。ここで求まった調整済み売上数および各係数は、以下のように利用される。
【００９７】
図２を参照して、基準値予測部１３について説明する。
【００９８】
基準値予測部１３は、調整済み売上データベース１８に記憶されている商品別の調整済みの売上数を用いて、調整済み売上数に対応する将来の売上数（以下、基準値と呼ぶ）をすべての商品について予測する。
【００９９】
基準値予測部１３は、調整済み売上数は、変動要因の影響が十分に除去されているかどうかの検定を行って、離れ値を除去する検定処理（Ｓ６）と、検定処理後の調整済み売上数に対して指数平滑化法を用いて基準値を算出する指数平滑処理（Ｓ７）とを行う。
【０１００】
まず、検定処理Ｓ６について説明する。
【０１０１】
基準値予測部１３は、商品ごとに、過去の調整後の売上数についてヒストグラムをとり、このヒストグラムが正規分布に従うかどうを、式（８）に示す歪度を計算して判定する。
【０１０２】
【数４】

ｎはサンプルとなった調整済み売上数の数（日数）、ｘ_iは調整済み売上数、ｘ_avはｘ_iの平均値、Ｖはｘ_iの分散である。
【０１０３】
完全に正規分布に従う場合は歪度が０となるので、歪度が０を含む所定の範囲内である商品に関しては、変動要因が十分に除去されているものとして扱う。これに対して、歪度が所定のしきい値の範囲外となる商品は、売上調整部１１が行った調整では、変動要因が十分に除去されていないものと考えられる。従って、基準値予測部１３は、歪度が所定のしきい値の範囲外となる商品を抽出する。
【０１０４】
ここで抽出された商品の調整済み売上数データを用いて、いわゆる箱ヒゲ図を作成する。箱ヒゲ図の作成方法の一例は、図１８を用いて説明する。
【０１０５】
一日の調整済み売上数データを値の小さい順に並べ、中央値を求めた後、２５％点および７５％点を求める。そして、２５％点から７５％点までを箱で表す。そして、中央値から箱の下端までの距離の１／２の長さのヒゲを箱の下端に付け、中央値から箱の上端までの距離の１／２の長さのヒゲを箱の上端に付ける。そして、上下のヒゲのさらに外側にある調整済み売上数データを離れ値として除去する。あるいは、離れ値は、離れ値以外の調整済み売上数データを用いて計算した平均値と置換してもよい。
【０１０６】
次に、指数平滑処理Ｓ７について説明する。
【０１０７】
検定処理で離れ値が除去された調整済み売上数データに対して、商品別に指数平滑化法を適用して、各商品の基準値を算出する。指数平滑化法による予測値算出式を以下に示す。
ｆ_t+1＝α・ｘ_t＋（１−α）ｆ_t …（９）
ｆ_t+1は翌日の予測値、ｘ_tは対象商品の当日の調整済み売上数、ｆ_tは対象商品の当日の予測値を示す。αは０から１までの値をとる。
【０１０８】
まず、調整済み売上データベース１８に記憶されている過去の調整済み売上数データを式（９）に適用して、最適なαを算出する。例えば、様々なα（例えば、０から１まで０．０１刻み）に対して、調整済み売上数をｘ_tに設定して翌日の予測値ｆ_t+1を算出する。このとき、予測値ｆ_tの初期値は０とする。そして、予測値ｆ_t+1と調整済み売上数ｘ_t+1との誤差の二乗を、過去１２０日分のデータに付いて合計し、その合計が最小となるようなαを最適なαとする。
【０１０９】
最適なαが定まると、このαを用いて翌日の基準値を算出する。すなわち、ｘ_tに本日の調整済み売上数、ｆ_tに最適なαのときの本日の予測値を代入して、翌日の基準値を求める。
【０１１０】
なお、この基準値は予測値算出部１５が将来の売上数を予測するときに用いられるが、その際、翌日以降の将来の日の基準値は、翌日の基準値と同一であるとする。
【０１１１】
次に、係数補正部１４について説明する。係数補正部１４は、本システムの予測モデルの予測精度を商品別に検証するモデル適合度判定（Ｓ８）と、モデル適合度判定の結果に基づいて係数を補正する補正処理（Ｓ９）を行う。
【０１１２】
ここで、次に説明する予測値算出部１５は、算出した売上数の予測値を事前予測値記憶部１９に格納する。つまり、事前予測値記憶部１９には、過去の商品別の売上数の予測値が蓄積されている。
【０１１３】
ステップＳ８のモデル適合度判定では、各商品について、この蓄積された事前予測値と、売上データベース５に記憶されている調整前の売上数の実績データとの誤差を求める。そして、この誤差を分析して、本システムの予測モデルの精度を判定し、予測精度が低く、本システムの予測モデルが実績データと適合していない商品を抽出する。例えば、係数補正部１４は、各商品の過去の所定日数分の事前予測値と調整前の売上数との誤差の分布を求める。この誤差の分布は正規分布に従うと考えられるので、正規分布から大きくはずれる商品、あるいは正規分布に近い誤差分布を有するが、標準偏差が所定のしきい値よりも大きい商品などを抽出する。
【０１１４】
ステップＳ９の係数補正処理では、モデルへの適合度が低いとして抽出された商品に関するデータを用いて各係数を補正する。例えば、最適化アルゴリズムを用いて、抽出された商品の事前予測値と調整前の売上数との誤差が最小になるような係数を算出する。算出された係数を用いて係数テーブル１２を更新し、係数を補正する。
【０１１５】
予測値算出部１５は、係数テーブル１２に蓄積された各係数と、基準値予測部１３が予測した各商品の基準値と、売上データベース５に格納されている予定の売価などの販売条件とを用いて、将来の売上数の予測値を算出する。例えば、以下の式を用いて、各店舗の日別商品別の売上数の予測値を算出する。
売上数の予測値＝基準値×売価係数×販促係数×天気係数×店係数×休日係数×曜日係数 …（１０）
【０１１６】
売価係数は、予測対象日に予定している売価に対応する売価係数である。販促係数は、予測対象日に販促が予定されていない場合は１とする。天気係数は、予測対象日の予想される天気（天気予報）と対応する天気係数である。店係数は、予測対象店舗の店係数である。曜日係数は、予測対象日の曜日と対応する曜日係数である。休日係数は、予測対象日が平日である場合は１である。
【０１１７】
ここまでに説明した需要予測システム１が算出した各商品の売上数の予測値、すなわち、商品別の需要予測値を用いて、発注提案システム２が各小売店に対して発注数量の提案を行う。
【０１１８】
発注提案システム２は、需要予測システム１が算出した需要予測値および各小売店の在庫状況に基づいて、各小売店に対して発注提案を行う。
【０１１９】
発注提案システム２の処理手順について、図１９を用いて説明する。発注提案システム２は、需要予測システム１から商品別の売上数予測値を取得し、各小売店の在庫状況を記憶した在庫データベース６から、各小売店の商品別の在庫数および発注済みで今後納品が予定されている納品数を取得する。
【０１２０】
発注提案システム２は、小売店システム４から店舗別、商品別の基準在庫数を取得する。ここで、基準在庫数とは、各小売店がそれぞれ定める在庫数であり、例えば、店の陳列棚に商品を陳列したときに、ボリューム感があり顧客に対して訴求する効果があると思われる在庫数であってもよい。この場合、基準在庫数は、陳列スペースの混み具合、またはすき具合に基づいて定めることができる。陳列スペースの混み具合またはすき具合は、例えば、陳列スペースに陳列可能な商品数に対する、陳列されている商品数の割合を占有率として用いてもよい。この場合、基準在庫数は、商品毎に予め定められている占有率に基づいて定まる。
【０１２１】
また、各小売店システム４発注提案システム２に対して、それぞれの小売店における商品別の基準在庫数を報告する（Ｓ１１１）。
【０１２２】
そして、発注提案システム２は、店舗別商品別の発注提案数を算出する（Ｓ１０１）。このときの算出式の一例が式（１１）である。
発注提案数＝売上数予測値（３日分）−在庫数−納品数＋基準在庫数 …（１１）
【０１２３】
そして、発注提案システム２は、この発注提案数を各小売店システム４へ送信して、発注提案を行う（Ｓ１０２）。
【０１２４】
各小売店システム４は、この発注提案を受信し、店舗の担当者が発注提案数を確認して、発注依頼をする（Ｓ１１２）。このとき、発注数を変更することもできる。
【０１２５】
発注提案システム２は、小売店システム４からの発注依頼を受けて、発注システム３に対して正式な発注を行う（Ｓ１０３）。
【０１２６】
なお、基準在庫数は、品切れ防止のために必要な安全在庫数と置き換えられても良い。
【０１２７】
上述した本発明の実施形態は、本発明の説明のための例示であり、本発明の範囲をそれらの実施形態にのみ限定する趣旨ではない。当業者は、本発明の要旨を逸脱することなしに、他の様々な態様で本発明を実施することができる。
【０１２８】
例えば、上述した需要予測システム、発注提案システム及び発注システムは、それら３つを纏めて一つのコンピュータマシンで集中的に実現することもできるし、それぞれ一つのコンピュータマシンに分けて実現することもできるし、それぞれ複数のコンピュータマシンで分散的に実現することもできる。
【図面の簡単な説明】
【図１】本発明を適用した一実施形態に係る在庫管理システムの構成を示す図である。
【図２】本実施形態に係る需要予測システムの構成を示す図である。
【図３】売上データベース５のデータ構造の一例を示す図である。
【図４】売価・販促調整の手順を示すフローチャートである。
【図５】（ａ）は売価集計データベース５１、（ｂ）は調整済み売上数Ａデータベース５２、および（ｃ）売価・販促係数テーブル１２１の構成の一例を示す図である。
【図６】売上数と売価比率との関係を示す図である。
【図７】売上数と売価比率との関係を示す図である。
【図８】天気調整の手順を示すフローチャートである。
【図９】（ａ）は天気集計データベース５３、（ｂ）は調整済み売上数Ｂデータベース５４、および（ｃ）天気係数テーブル１２２の構成の一例を示す図である。
【図１０】調整済み売上数Ａと天気比率との関係を示す図である。
【図１１】店調整の手順を示すフローチャートである。
【図１２】（ａ）は店集計データベース５５、（ｂ）は調整済み売上数Ｃデータベース５６、および（ｃ）店係数テーブル１２３の構成の一例を示す図である。
【図１３】休日調整の手順を示すフローチャートである。
【図１４】（ａ）は休日係数テーブル１２４、および（ｂ）は調整済み売上数Ｄデータベース５７の構成の一例を示す図である。
【図１５】曜日調整の手順を示すフローチャートである。
【図１６】移動平均値の算出手順を説明するための図である。
【図１７】（ａ）は曜日係数テーブル１２５、および（ｂ）は調整済み売上数データベース１８の構成の一例を示す図である。
【図１８】検定処理手順を説明するための図である。
【図１９】本実施形態に係る発注提案システムの構成を示す図である。
【符号の説明】
１…需要予測システム、２…発注提案システム、３…発注システム、４…小売店システム、５…売上データベース、６…在庫データベース、１１…売上数調整部、１２…係数テーブル、１３…基準値予測部、１４…係数補正部、１５…予測値算出部、１８…調整済み売上データベース、１９…事前予測値データベース、Ｓ１…売価・販促調整処理、Ｓ２…天気調整処理、Ｓ３…店調整処理、Ｓ４…休日調整処理、Ｓ５…曜日調整処理、Ｓ６…検定処理、Ｓ７…指数平滑処理、Ｓ８…モデル適合度判定処理、Ｓ９…補正処理。[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a technique for predicting demand for goods sold by a retail store.
[0002]
[Prior art]
Many retail stores have a POS (Point of Sales) system in widespread use, and sales data of products collected through this system are used in various ways. Among them, there is an automatic ordering system that predicts the demand for goods based on sales data and places an order efficiently.
[0003]
For example, Patent Document 1 describes a system that performs demand prediction in consideration of a demand fluctuation factor of a product and automatically orders a product based on a prediction result.
[0004]
[Patent Document 1]
JP-A-1-224864
[0005]
[Problems to be solved by the invention]
However, the demand forecast performed by the automatic ordering system described in Patent Document 1 is a simple average of past data, or a correction based on a correction rule based on the know-how of an inventory manager such as a store manager. It is. In other words, how much the fluctuation factor causes the sales volume to fluctuate is determined based on the store manager's experience and kang.
[0006]
Further, the number of orders based on the demand forecast result is also determined by correcting based on diagnostic rules based on know-how of the store manager or the like. In other words, the number of orders calculated by the system is corrected with the experience of the store manager and Kang.
[0007]
As described above, in the conventional automatic ordering system, the accuracy of demand prediction cannot be improved unless there is a person who has the know-how after all, and furthermore, the appropriate number of orders cannot be determined.
[0008]
This invention is providing the technique for enabling the demand prediction of goods even if there is no know-how.
[0009]
Another object of the present invention is to quantify the effect of factors that change the number of items sold on the change in the number of items sold.
[0010]
Yet another object of the present invention is to remove the influence of sales fluctuations due to fluctuation factors from the sales performance of products.
[0011]
[Means for Solving the Problems]
The product demand prediction system according to one embodiment of the present invention includes a storage unit that stores information indicating the number of sales for each past product for a plurality of products, and a sales number for each product stored in the storage unit. Based on the information shown, the variable factor that fluctuates the sales number calculates a contribution for each product that contributes to the fluctuation of the sales number, and each product based on the calculated contribution by product The means for adjusting the number of sales for each product by removing the fluctuation due to the fluctuation factor from the number of sales of the product, and the future product based on the calculated contribution for each product and the adjusted number of sales for each product Means for predicting the number of other sales.
[0012]
In a preferred embodiment, the calculation means calculates a contribution for each factor for each of the plurality of variation factors, and the adjustment means calculates the number of sales of each product based on the contribution for each factor for each product. The prediction means predicts the future number of sales for each product based on the contribution for each factor for each product and the adjusted number of sales for each product.
[0013]
In a preferred embodiment, the fluctuating factors are the selling price of the product, the weather of the day when the product was sold, the number of stores where the product was sold, whether the day when the product was sold is a holiday, and the day of the week when the product was sold Including any one of
[0014]
In a preferred embodiment, the prediction means predicts the number of sales by product in the future prediction target date based on the sales conditions of the prediction target date. Further, the sales condition is one or more of a planned selling price for each product of each store, a forecasted weather for the forecast target day, whether the forecast target day is a holiday, and a day of the week for the forecast target day. May be included.
[0015]
In a preferred embodiment, the correction for correcting the contribution degree based on a comparison result between the number of sales by product of the prediction target date predicted by the prediction unit in advance and the number of sales by product of the result of the prediction target day. Means are further provided.
[0016]
According to one embodiment of the present invention, a sales volume adjustment system for a product includes storage means for storing a selling price of one product and the sales price at the selling price, and the selling price is calculated based on the selling price and the sales volume. And a means for calculating the number of sales adjusted for sales price after removing the influence on the increase or decrease of the number.
[0017]
In a preferred embodiment, the calculation means statistically processes the selling price and the number of sales to obtain a relationship between the selling price and the number of sales, and based on the relationship between the selling price and the number of sales, each selling price. The number of sales at is converted into the number of sales at the base selling price. Further, the relationship between the selling price and the number of sales may be represented by an exponential function, and the relationship represented by the exponential function may be calculated by regression calculation using a least square method.
[0018]
The product sales number adjustment system according to one embodiment of the present invention includes a storage unit that stores the weather on the day when one product is sold and the number of sales of the product on that day in association with each other, and the weather and the number of sales. And calculating means for adjusting the number of sales after removing the influence of the weather on the increase / decrease in the number of sales.
[0019]
In a preferred embodiment, the calculating means statistically processes the weather and the number of sales to obtain a relationship between the weather and the number of sales, and based on the relationship between the weather and the sales number, The number of sales at is converted into the number of sales at the standard weather. Furthermore, the relationship between the weather and the number of sales may be expressed by a linear function, and the relationship expressed by the linear function may be calculated by regression calculation using a least square method.
[0020]
According to one embodiment of the present invention, a sales number adjustment system for a product includes a storage unit that stores the number of sales by day for each product in a plurality of stores, and stores of each store based on the number of sales. And calculating means for calculating the number of daily sales adjusted for the store by compensating the number of sales at the closed shop using the sales ratio for each store.
[0021]
According to one embodiment of the present invention, a product sales number adjustment system compares a storage means storing a weekday sales number and a holiday sales number of one product with the weekday sales number and a holiday sales number. And means for adjusting the number of sales on holidays.
[0022]
In a preferred embodiment, the adjusting means obtains a sales ratio of holidays on weekdays, and adjusts the sales numbers on holidays using the sales ratio.
[0023]
The system for adjusting the number of sales of a product according to one embodiment of the present system includes a storage unit that stores the number of sales of a single product for each day and the day of the week in association with each other, an average value of the number of sales for one week, Means for comparing the daily sales figures with each other to calculate day-by-day characteristics and adjusting the daily sales figures based on the day-by-day characteristics.
[0024]
The system for adjusting the number of sales of a product according to one embodiment of the present system includes a storage unit that stores the sales results of one product at a plurality of stores within a predetermined period by day and store, and the sales of the product from the storage unit. Sales price adjustment means for obtaining the number and the selling price of the product, and calculating the first adjusted sales number for each store, by removing the influence of the selling price on the increase or decrease of the sales number, and the storage means from the storage means The second adjusted number of sales for each store by acquiring the weather on the day the product was sold and removing the effect of the weather on the increase or decrease in the number of sales of the product based on the first adjusted number of sales The weather adjustment means for calculating the store and the storage means, the sales store and the closed shop are identified on a daily basis, and based on the second adjusted number of sales, the store closure is an increase or decrease in the number of sales of the product The day after removing the effect on The store adjustment means for calculating the third adjusted sales number of the store and the storage means are used to identify holidays and weekdays by day, and the third adjusted sales number of holidays and the third adjustment on weekdays. A holiday adjusting means for calculating a fourth adjusted sales number for each day, which removes the influence of holidays on the increase or decrease in the number of sales of the product, and the storage means. The fifth adjusted number of sales per day, identifying the day of the week and removing the effect of each day on the increase or decrease in sales of the product based on the fourth adjusted number of sales per day of the week A day of the week adjusting means for calculating
[0025]
The product demand prediction system according to one embodiment of the present invention stores means for a plurality of past days, the adjusted number of sales obtained by removing the influence of fluctuation due to the fluctuation factor of the number of sales from the number of sales of one product. And a means for predicting future adjusted sales based on the adjusted number of sales in a plurality of days in the past, and adding the influence of fluctuations due to the variable factors to the future adjusted sales. Means for calculating a predicted value of the number of sales.
[0026]
In a preferred embodiment, the apparatus further comprises means for storing a contribution indicating the degree of influence of the fluctuation factor on the change in the number of sales, and means for storing future sales conditions, wherein the calculation means includes the calculation means A predicted value of the future sales number is calculated based on the future adjusted sales number, the contribution degree, and the sales conditions.
[0027]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments to which the present invention is applied will be described with reference to the drawings.
[0028]
An inventory management system according to an embodiment of the present invention includes a demand prediction system 1 that predicts the demand of each product for each retail store based on sales performance data of a plurality of products at a plurality of retail stores, and the demand prediction. An ordering proposal system 2 for proposing an order quantity to each retail store based on the ordering system 3 for actually placing an order for a product in response to an ordering instruction from the retail store, A retail store system 4 that accepts an order proposal and makes an order request.
[0029]
The demand prediction system 1, the order proposal system 2, the order system 3 and the retail store system 4 are all configured by, for example, a general-purpose computer system, and are individually configured in the

systems

1, 2, 3, 4 described below. The element or function is realized by executing a computer program, for example.
[0030]
First, the demand prediction system 1 is demonstrated using FIG.
[0031]
In the demand prediction system 1, the sales number adjustment unit 11 obtains sales performance data for each past store, each day, and each product stored in the sales database 5, and various variable factors that cause the number of sales of each product to fluctuate. Is determined as a coefficient for each product and stored in the coefficient table 12. Further, the sales number adjustment unit 11 uses the obtained coefficients to obtain the adjusted sales number for each product from which the influence of the increase / decrease in the sales number due to the respective fluctuation factors is removed. The adjusted sales number is stored in the adjusted sales database 18. Here, since each coefficient may not accurately reflect each variation factor, the coefficient correction unit 14 analyzes the adjusted number of sales and corrects the coefficient as necessary. Further, the reference value prediction unit 13 predicts a reference value corresponding to the number of adjusted sales in the future using the number of adjusted sales in the past. Then, the predicted value calculation unit 15 predicts the number of sales for each product by using the reference value obtained by the above-described processing and the corrected coefficient.
[0032]
Hereinafter, individual processing in the demand prediction system 1 will be described in detail.
[0033]
The sales number adjustment unit 11 acquires sales performance data from the sales database 5, and sells and promotes adjustments (S1) to remove the effects of selling prices and sales promotions (hereinafter referred to as sales promotions), and weather adjustments to remove the influence of weather (S2). ), Store adjustment to remove the effects of closed stores (S3), holiday adjustment to remove the effects of holidays (Saturday, Sunday and public holidays) (S4), and day of the week adjustment to remove the effects of each day of the week (S5) Do in this order. Each of these adjustment processes can be performed by changing the order, and one or more adjustment processes can be omitted.
[0034]
The sales database 5 stores information such as the number of sales at each retail store for each day and each product. For example, as shown in FIG. 3, the sales database 5 includes a product code 501 for identifying a product as a data item, a store code 502 for identifying a store, a date 503, a day of the week 504, and holidays. A holiday flag 505 indicating whether or not, a weather code 506 indicating the weather, a region code 507 indicating the region where the store is located, a scale code 508 indicating the size of the store, and a basic selling price 509 which is the basic selling price of the product The sales price 510 when the product is actually sold, the sales quantity 511 at the sales price, and the sales promotion flag 512 indicating whether or not the sales promotion has been performed.
[0035]
The weather code 506 is a code indicating the weather of the place where the store indicated by the store code 502 exists on the day indicated by the date 503. For example, a code is assigned to the weather code 506 for one-day weather such as “sunny”, “sunny and cloudy”, and “sunny and temporarily cloudy”.
[0036]
The sales database 5 stores data for the past 120 days and for the future 8 days. As for past data, all the actual data is stored. For future data, the weather code 506 is set based on the weather forecast, and the sales price 510 and the sales promotion flag 512 are set to the contents scheduled by each store.
[0037]
Next, details of the processing from steps S1 to S5 performed by the sales adjustment unit 11 will be described.
[0038]
First, the selling price / sales promotion adjustment S1 will be described with reference to FIGS.
[0039]
The sales number adjustment unit 11 preprocesses all past performance data in the sales database 5 and generates a sales price tabulation database 51 (S11). In this preprocessing, for example, predetermined data items in the sales database 5 are totaled using the product code, the sales promotion flag, and the sales ratio as keys. For example, as shown in FIG. 5A, the selling price total database 51 includes, as data items, a product code 5101, a sales promotion flag 5102, a selling price ratio 5103, a sales number 5104, and a frequency 5105.
[0040]
Here, the selling price ratio 5103 is a ratio of the actual selling price 510 to the basic selling price 509, and becomes 1 when the selling price 510 is the same as the basic selling price 509. For example, if a product with a basic selling price of 100 yen is sold at 70 yen, the selling price ratio is 0.7.
[0041]
The number of sales 5104 is an average of the number of sales of all stores in the past 120 days for each selling price ratio 5103.
[0042]
The frequency 5105 indicates the total number of stores sold at each selling price ratio 5103 in the past 120 days at all stores. For example, if there are 10 days in which the 20 stores have sold the product B at the selling price ratio “1” in the past 120 days, the frequency 5105 with the selling price ratio 5103 of “1” is counted as “200”.
[0043]
Returning to FIG. When the preprocessing of step S11 is completed, the sales number adjustment unit 11 performs statistical processing on the data stored in the selling price aggregation database 51 to obtain the relationship between the selling price and the sales number. Here, regression analysis is performed for each product, and a regression curve is calculated (S12). A processing image for calculating the regression curve will be described with reference to FIG. Here, a curve indicating the relationship between the sales ratio and the number of sales is calculated using the least square method. Here, in order to facilitate the calculation, the selling price ratio obtained by normalizing the selling price is used. However, the selling price may be used.
[0044]
First, with respect to data stored in the sales price aggregation database 51 when sales promotion is not performed, that is, data for which the sales promotion flag 5102 is not set, xy with the sales price ratio 5103 as the x axis and the sales number 5104 as the y axis. Plot (■) on the plane. At this time, each plot is weighted according to the frequency 5105. Here, using the regression curve shown in Equation (1) and the weighted plot group, b is calculated by the least square method. ₀ And b ₁ Is calculated.
y = b ₀ exp (-b ₁ x) ... (1)
[0045]
Returning to FIG. 4, the selling price coefficient and the sales promotion coefficient are calculated using the regression curve obtained in step S12 (S13).
[0046]
The sales promotion coefficient is calculated as follows, that is, taking the logarithm of both sides of the formula (1) indicating the relationship between the selling price ratio and the number of sales when the sales promotion is not performed, the formula (2) is obtained.
log _e y = log _e b ₀ -B ₁ x (2)
[0047]
And the selling price ratio on the X axis and log on the Y axis _e When the number of sales is taken and graphed, a straight line A shown in FIG. 7 is obtained. On this XY plane, the data when the sales promotion is stored, that is, the relationship between the sales price ratio 5103 when the sales promotion flag 5102 is set and the number of sales 5104 stored in the sales price totaling database 51 are plotted (□). To do.
[0048]
Here, the selling price ratio and log at the time of sales promotion _e The same slope (−b) as in formula (2) between (number of sales) ₁ Assuming that there is a linear relationship of), an equation (3) indicating a straight line when the sales promotion is performed is calculated.
log _e y = log _e kb ₀ -B ₁ x (3)
Where intercept log _e kb ₀ K is a sales promotion coefficient.
[0049]
Next, a method for calculating the selling price coefficient will be described with reference to FIG. First, find the highest selling price ratio. That is, when the frequency 5105 is tabulated using the selling price ratio 5103 as a key, the selling price ratio 5103 (hereinafter referred to as a reference selling price ratio) having the largest frequency is obtained. Then, the reference selling price ratio is substituted into formula (1), and the number of sales at that time (hereinafter referred to as the reference sales number) is obtained. The selling price coefficient of each selling price ratio is represented by the ratio of the sales volume of each selling price ratio to the reference sales volume.
[0050]
For example, in the present embodiment, the number of sales at various selling price ratios is obtained using Equation (1), and the ratio between the number of sales and the reference number of sales is calculated as each selling price coefficient.
[0051]
A more specific description will be given with reference to FIG. In the example of FIG. 6, it is assumed that the standard selling price ratio is 0.9 and the number of sales at that time (standard sales number) is 100. At this time, the selling price coefficient when the selling price ratio is 0.9 is “1”. Further, if the sales number is 110 when the selling price ratio is 0.8, the selling price coefficient at that time is “1.1”.
[0052]
In the present embodiment, the selling price coefficient is calculated for all selling price ratios 5103 stored in the selling price totaling database 51 by the above-described procedure. Further, the sales database 5 can store future data (scheduled selling price, etc.). When the selling database 5 has a selling price that exists only in the future, the selling price coefficient can also be calculated for this. it can.
[0053]
The sales price coefficient and the sales promotion coefficient obtained here are stored in the sales price / sales promotion coefficient table 121. An example of the selling price / promotion coefficient table 121 is shown in FIG. That is, the data items of the sales price / promotion coefficient table 121 are a product code 1211, a sales price 1212, a sales price ratio 1213, a sales price coefficient 1214, and a sales promotion coefficient 1215.
[00054]
Then, using the selling price coefficient and the sales promotion coefficient, the selling price / sales promotion of the number of sales is adjusted for each product (S14). For example, the sales price adjustment is performed by dividing the number of sales 511 of all past performance data stored in the sales database 5 by the selling price coefficient 1214 corresponding to the selling price ratio determined by the product code 501, the basic selling price 509, and the selling price 510. I do. This selling price adjustment is equivalent to converting the number of sales at each selling price into the number of sales at the standard selling price. That is, the actual number of sales of a product greatly depends on the selling price, but by adjusting the selling price, the effect of increasing or decreasing the number of sales due to selling price fluctuations can be removed.
[0055]
For the data for which the sales promotion flag 512 is set, the sales price is adjusted by dividing the sales price by the sales promotion coefficient 1215 after the above-described sales price adjustment. As with sales prices, the number of sales varies greatly depending on whether or not sales promotions were made, so this sales promotion adjustment can be converted into sales numbers when sales promotions are not made, and the effects of sales increases and decreases due to sales promotions can be removed. .
[0056]
As a result, the selling price and the sales promotion adjustment are performed for all past performance data stored in the sales database 5. The sales number adjusted sales number obtained here, and the sales price and sales adjusted sales number are referred to as adjusted sales number A.
[0057]
The adjusted sales number A is stored in the adjusted sales number A database 52. As shown in FIG. 5B, the adjusted sales number A database 52 includes a product code 521, a store code 522, a date 523, a day of the week 524, a holiday flag 525, a weather code 526, and a region. The data item includes a code 527, a scale code 528, and an adjusted sales number A529.
[0058]
Next, the weather adjustment S2 will be described with reference to FIGS.
[0059]
Here, the weather adjustment is performed using the adjusted sales number A and the adjusted sales number A. Therefore, the sales number adjustment unit 11 preprocesses all the data stored in the adjusted sales number A database 52 to generate the weather aggregation database 53 (S21). In this pre-processing, for example, predetermined items in the adjusted sales quantity A database 52 are aggregated using the product code 521 and the weather code 526 as keys.
[0060]
For example, as shown in FIG. 9A, the weather summary database 53 includes a product code 531, a weather code 532, a weather ratio 533, an adjusted sales number A534, and a frequency 535 as data items.
[0061]
Here, the weather ratio 533 represents the ratio of the time when the sun actually came out in the time zone when the sun was above the horizon, divided into 8 stages from 0 to 1 based on the weather code. Is. Here, for example, when the weather code indicates clear, the weather ratio is “1”, when the weather code is clear and cloudy or when cloudy and then clear, the weather ratio is “0.5”, and when the weather code indicates rain Let the weather ratio be “0” or the like. In other words, since the approximate time when the sun is supposed to have come out is determined by the weather, the weather ratio is uniquely determined if the weather code is determined.
[0062]
The adjusted sales number A 534 is an average of the adjusted sales number A of all the stores for the past 120 days at each weather ratio 533.
[0063]
The frequency 535 indicates the total number of stores sold at each weather ratio 533 in the past 120 days at all stores. For example, in the past 120 days, if there are 20 days when 10 stores sell the product D when the weather ratio is “0.5”, the frequency 535 when the weather ratio 533 is “0.5” is “200”. Is counted.
[0064]
Referring to FIG. 8 again, the sales number adjustment unit 11 performs statistical processing on the data stored in the weather summary database 53 to obtain the relationship between the weather and the sales number. Here, regression analysis is performed for each product using data in the weather summary database 53, and a regression curve is calculated (S22). This regression analysis process will be described with reference to FIG. FIG. 10 shows a processing image when a straight line indicating the relationship between the weather ratio and the number of sales is calculated using the least square method on the xy plane with the weather ratio on the x-axis and the number of sales on the y-axis. Show.
[0065]
On the xy plane shown in FIG. 10, the data of the weather summary database 53 is plotted with weighting according to the frequency, and c in equation (4) is plotted using this plot group. ₀ And c ₁ Is calculated using the least squares method.
y = c ₀ + C ₁ x (4)
[0066]
Returning to FIG. 8, the weather coefficient is calculated for each product using the straight line of the equation (4) obtained in step S22 (S23). As for the weather coefficient, for example, as in the case of calculating the selling price coefficient, the weather ratio with the highest frequency (hereinafter referred to as the reference point weather ratio) is obtained, and the adjusted sales number A (hereinafter referred to as the reference adjusted sales ratio) at that time is obtained. (Referred to as number A) is determined using equation (4) and is determined based on this reference-adjusted number of sales A. For example, the adjusted sales number A corresponding to all the weather ratios is obtained by Expression (4), and the ratio between the reference adjusted sales number A and the adjusted sales number A at the other seven weather ratios is obtained. This is the weather coefficient for each weather ratio.
[0067]
This will be specifically described with reference to FIG. In the example of FIG. 10, it is assumed that the weather ratio with the highest frequency is 0.625, and the adjusted sales number A (reference adjusted sales number A) at that time is 100. At this time, the weather coefficient when the weather ratio is 0.625 is “1”. Further, if the adjusted sales number A is 90 when the weather ratio is 0.5, the weather coefficient at that time is 0.9.
[0068]
In the present embodiment, weather coefficients are calculated for all weather ratios 533 stored in the weather summary database 53. The sales database 5 can store future data (scheduled selling price, etc.). When the sales database 5 has a weather ratio that exists only in the future, the weather coefficient is also calculated for this. Can do.
[0069]
The weather coefficient calculated in step S22 is stored in the weather coefficient table 122. As shown in FIG. 9C, the weather coefficient table 122 includes a product code 1221, a weather code 1222, and a weather coefficient 1223 as data items.
[0070]
Next, the weather adjustment of the number of sales of each product is performed using the weather coefficient. That is, the weather adjustment is performed by dividing the adjusted sales number A529 of the adjusted sales number A database 52 by the weather coefficient 1223 corresponding to each weather code 526. That is, although the number of sales of the product varies depending on the weather, the effect of the weather on the increase / decrease of the sales number is eliminated by performing this weather adjustment. The number of sales obtained here means that the influence of the weather has been removed in addition to the selling price and the sales promotion, and this sales number is referred to as adjusted sales number B.
[0071]
The adjusted sales number B is stored in the adjusted sales number B database 54. The data items of the adjusted sales number B database 54 include, for example, a product code 541, a store code 542, a date 543, a day of the week 544, a holiday flag 545, a region, as shown in FIG. It has a code 546, a scale code 547, and an adjusted sales number B548.
[0072]
Next, store adjustment S3 is demonstrated using FIGS. 11-12.
[0073]
Here, the store adjustment is performed using the adjusted sales number B after the sales price, sales promotion, and weather adjustment. Therefore, the store data database 55 is generated by pre-processing the data stored in the adjusted sales number B database 54 (S31). In this pre-processing, for example, the product code and the store code in the adjusted sales number B database 54 are used as a key and totalized. For example, as shown in FIG. 12A, the store total database 55 has a product code 551, a store code 552, a region code 533, a scale code 554, and an adjusted sales number B555 as data items.
[0074]
After the preprocessing, the sales adjustment processing unit 11 uses the data in the store total database 55 to calculate a store coefficient indicating a sales ratio for each store for each product (S32). The store coefficient is calculated for each product for each store using, for example, the following equation (5).
[0075]
[Expression 1]

[0076]
The sales adjustment processing unit 11 calculates a store coefficient for each store and classifies each store into one of a plurality of clusters according to the characteristics of each store. For example, clustering can be performed according to a region code 553 that indicates the region where each store exists, a size code 554 that indicates the size of each store, or the amount of sales on holidays. Subsequent processing of S3 and processing of S4 and S5 can also be performed in cluster units.
[0077]
The store coefficient and clustering result calculated here are stored in the store coefficient table 123. As illustrated in FIG. 12C, the store coefficient table 123 includes a product code 1231, a store code 1232, a store coefficient 1233, and a cluster number 1234.
[0078]
Next, using the store coefficient obtained here, store adjustment is performed for each product and each day with respect to the adjusted sales number B (S33). Here, if there is a closed store, the total number of sales decreases on that day, so the decrease in the number of sales due to store closure is compensated. As a result of this store adjustment, the sales fluctuation due to the closure of the store is further removed from the adjusted sales number B. The number of sales after the store adjustment is called adjusted sales number C. The adjusted sales number C for each day is obtained by the following equation (6).
[0079]
[Expression 2]

[0080]
In addition, the store coefficient sum of the store which the denominator of the right side of Formula (6) is open is calculated | required as follows. That is, since there is originally no data on a closed shop in the sales database 5, when the adjusted sales number B548 in the adjusted sales number B database 54 is tabulated daily, the store code 542 is extracted, The store coefficient 1233 of the store code 542 is totaled.
[0081]
As a result, even if there is a store closed on the day on which the adjusted sales number C is calculated, the number of sales expected when the store is not closed can be obtained.
[0082]
The adjusted sales number C is stored in the adjusted sales number C database 56. The data items of the adjusted sales number C database 56 include, for example, a product code 561, a cluster number 562, a date 563, a day of the week 564, a holiday flag 565, a store, as shown in FIG. It has a coefficient total 566 and adjusted sales number C567.
[0083]
Next, holiday adjustment S4 will be described with reference to FIGS.
[0084]
Here, the holiday factor is adjusted by using the sales price, the sales promotion, the weather, and the adjusted sales number C after the store adjustment. Therefore, a holiday coefficient is calculated for each product using the data stored in the adjusted sales number C database 56 (S41). The holiday coefficient is calculated by the following formula (7) for all holidays (Saturday, Sunday and national holidays) in the past 120 days.
[0085]
[Equation 3]

Weekdays are days other than holidays.
[0086]
The holiday coefficient calculated here is stored in the holiday coefficient table 124. For example, as shown in FIG. 14A, the holiday coefficient table 124 includes a product code 1241, a cluster number 1242, a holiday coefficient 1243, and a holiday flag 1244.
[0087]
Then, a holiday adjustment is performed for each product using the holiday coefficient for the adjusted sales number C567 in which the holiday flag 565 is set (S42). The holiday adjustment is performed, for example, by dividing the adjusted sales number C of each holiday by the holiday coefficient. By this holiday adjustment, the sales fluctuation due to the holiday is further removed from the adjusted sales number C. The number of sales after holiday adjustment is called adjusted sales number D.
[0088]
The adjusted sales number D is stored in the adjusted sales number D database 57. The data items of the adjusted sales number D database 57 include, for example, a product code 571, a cluster number 572, a date 573, a day of the week 574, a holiday flag 575, a store, as shown in FIG. It has a coefficient total of 576 and an adjusted number of sales D577.
[0089]
Next, the day adjustment S5 will be described with reference to FIGS.
[0090]
Here, the day of the week adjustment is performed using the adjusted sales quantity D adjusted for selling price, sales promotion, weather, shop, and holidays. Therefore, using the data stored in the adjusted sales number D database 57, the day of the week coefficient indicating the sales characteristics of each day of the week is calculated (S51). The day of the week coefficient is calculated as follows.
[0091]
First, out of the past 120 days in which the actual data is stored in the sales database 5, the adjusted number of sales D for 114 days excluding the most recent 3 days and the latest 3 days is subjected to a moving average and the day of the week. Remove another characteristic.
[0092]
For example, as shown in FIG. 16, the moving average is performed using the moving average calculation target date and the adjusted number of sales D for three days before and after that date (for seven days including the target day). That is, the average value of the adjusted number of sales D on the 7th is the moving average value on the calculation target date. Then, the day of the week coefficient is calculated by averaging the value obtained by dividing the adjusted sales number D on the target day by this moving average value for each day of the week.
[0093]
The day of the week coefficient obtained here is stored in the day of the week coefficient table 125. For example, as shown in FIG. 17A, the day-of-week coefficient table 125 includes a product code 1251, a cluster number 1252, a day of the week 1253, and a day-of-week coefficient 1254.
[0094]
Returning to FIG. 15, the day of the week is adjusted for the adjusted number of sales D using the day of the week coefficient corresponding to each day of the week (S 52). For example, the day adjustment is performed by dividing the adjusted sales number D577 stored in the adjusted sales number D database by the day of the week coefficient 1254 of the corresponding day 574. As a result, the sales fluctuation due to the day of the week is further removed from the adjusted sales number D. Here, the number of sales from which the sales fluctuation due to the day of the week has been removed is the adjusted number of sales from which all sales fluctuation factors have been removed. Then, the adjusted sales number is stored in the adjusted sales database 18.
[0095]
As shown in FIG. 17B, the adjusted sales database 18 includes a product code 181, a cluster number 182, a date 183, a day 184, a holiday flag 185, a store coefficient total 186, and an adjusted The number of sales 187 is included as a data item.
[0096]
As described above, the sales number adjustment unit 11 uses various data such as the actual sales number stored in the sales database 5 (hereinafter, referred to as the sales number before adjustment). The degree to which the factor contributes to the fluctuation in the number of sales is obtained as a coefficient, and the adjusted number of sales is obtained by removing the fluctuation in the number of sales due to each fluctuation factor using the coefficient. The adjusted number of sales and each coefficient obtained here are used as follows.
[0097]
The reference value prediction unit 13 will be described with reference to FIG.
[0098]
The reference value prediction unit 13 uses the adjusted sales numbers for each product stored in the adjusted sales database 18 to determine all future sales numbers (hereinafter referred to as reference values) corresponding to the adjusted sales numbers. Forecast for products.
[0099]
The reference value prediction unit 13 performs a test to determine whether or not the influence of the fluctuation factor has been sufficiently removed for the adjusted sales number, a test process (S6) for removing the outliers, and an adjusted sales after the test process. An exponential smoothing process (S7) for calculating a reference value using an exponential smoothing method for the number is performed.
[0100]
First, the verification process S6 will be described.
[0101]
The reference value prediction unit 13 takes a histogram for the past adjusted number of sales for each product, and determines whether this histogram follows a normal distribution by calculating the skewness shown in Equation (8).
[0102]
[Expression 4]

n is the number of adjusted sales in samples (days), x _i Is adjusted sales, x _av Is x _i Mean value, V is x _i Is the dispersion of.
[0103]
Since the skewness is 0 when the normal distribution is completely followed, products with a skewness within a predetermined range including 0 are treated as those in which the fluctuation factors are sufficiently removed. On the other hand, for products whose skewness is outside the predetermined threshold range, it is considered that the factor of variation is not sufficiently removed by the adjustment performed by the sales adjustment unit 11. Therefore, the reference value prediction unit 13 extracts products whose skewness is outside the predetermined threshold range.
[0104]
A so-called box mustache diagram is created using the adjusted sales number data of the product extracted here. An example of a method for creating a box mustache will be described with reference to FIG.
[0105]
The adjusted number of sales data for one day is arranged in ascending order of values, and after obtaining the median, 25% and 75% points are obtained. And from the 25% point to the 75% point is represented by a box. Then, a whisker having a length that is 1/2 of the distance from the median to the lower end of the box is attached to the lower end of the box, and a whisker having a length of 1/2 of the distance from the median to the upper end of the box is attached to the upper end of the box. wear. Then, the adjusted sales number data further outside the upper and lower whiskers is removed as the away value. Alternatively, the outlier may be replaced with an average value calculated using adjusted sales data other than the outlier.
[0106]
Next, the exponential smoothing process S7 will be described.
[0107]
A reference value for each product is calculated by applying an exponential smoothing method for each product to the adjusted sales data from which the outliers have been removed in the verification process. The formula for calculating the predicted value by the exponential smoothing method is shown below.
f _{t + 1} = Α · x _t + (1-α) f _t ... (9)
f _{t + 1} Is the predicted value for the next day, x _t Is the adjusted number of sales on the current day of the target product, f _t Indicates the predicted value of the target product for the day. α takes a value from 0 to 1.
[0108]
First, the past adjusted sales number data stored in the adjusted sales database 18 is applied to the equation (9) to calculate the optimum α. For example, for various α (eg, 0 to 1 in increments of 0.01), the adjusted number of sales is x _t To the next day's predicted value f _{t + 1} Is calculated. At this time, the predicted value f _t The initial value of is assumed to be 0. And the predicted value f _{t + 1} And adjusted sales x _{t + 1} Is summed over the data for the past 120 days, and α that minimizes the sum is taken as the optimum α.
[0109]
When the optimum α is determined, the reference value for the next day is calculated using this α. That is, x _t Today's adjusted sales, f _t Substitute today's predicted value when α is optimal for, and obtain the next day's reference value.
[0110]
This reference value is used when the predicted value calculation unit 15 predicts the number of future sales. At this time, it is assumed that the reference value of the future day after the next day is the same as the reference value of the next day.
[0111]
Next, the coefficient correction unit 14 will be described. The coefficient correction unit 14 performs a model suitability determination (S8) for verifying the prediction accuracy of the prediction model of this system for each product, and a correction process (S9) for correcting the coefficient based on the result of the model suitability determination.
[0112]
Here, the predicted value calculation unit 15 to be described next stores the calculated predicted value of the number of sales in the prior predicted value storage unit 19. That is, the predicted value storage unit 19 stores the predicted value of the number of sales for each past product.
[0113]
In the model suitability determination at step S8, for each product, an error between the accumulated prior prediction value and the actual data of the number of sales before adjustment stored in the sales database 5 is obtained. Then, the error is analyzed to determine the accuracy of the prediction model of the present system, and a product whose prediction accuracy is low and the prediction model of the present system is not compatible with the actual data is extracted. For example, the coefficient correction unit 14 obtains a distribution of errors between the prior predicted value for each predetermined number of days in the past and the number of sales before adjustment. Since this error distribution is considered to follow a normal distribution, products that deviate greatly from the normal distribution or products that have an error distribution close to the normal distribution but have a standard deviation larger than a predetermined threshold are extracted.
[0114]
In the coefficient correction process in step S9, each coefficient is corrected using data relating to the product extracted as having a low degree of fitness with the model. For example, the optimization algorithm is used to calculate a coefficient that minimizes the error between the pre-predicted value of the extracted product and the number of sales before adjustment. The coefficient table 12 is updated using the calculated coefficient, and the coefficient is corrected.
[0115]
The predicted value calculation unit 15 obtains each coefficient accumulated in the coefficient table 12, the reference value of each product predicted by the reference value prediction unit 13, and sales conditions such as a planned selling price stored in the sales database 5. Use to calculate the predicted value of future sales. For example, the predicted value of the number of sales for each daily product of each store is calculated using the following formula.
Estimated number of sales = reference value x selling price coefficient x promotional coefficient x weather coefficient x store coefficient x holiday coefficient x day of week coefficient (10)
[0116]
The selling price coefficient is a selling price coefficient corresponding to the selling price scheduled for the forecast date. The sales promotion coefficient is set to 1 when the sales promotion is not scheduled on the prediction target day. The weather coefficient is a weather coefficient corresponding to the expected weather (weather forecast) on the prediction target day. The store coefficient is a store coefficient of the prediction target store. The day of the week coefficient is a day of the week coefficient corresponding to the day of the prediction target day. The holiday coefficient is 1 when the prediction target day is a weekday.
[0117]
The order proposal system 2 proposes the order quantity to each retail store using the predicted number of sales of each product calculated by the demand prediction system 1 described so far, that is, the demand forecast value for each product. .
[0118]
The order proposal system 2 makes an order proposal to each retail store based on the demand forecast value calculated by the demand forecast system 1 and the inventory status of each retail store.
[0119]
The processing procedure of the order proposal system 2 will be described with reference to FIG. The order proposal system 2 obtains the sales forecast value for each product from the demand prediction system 1 and stores the stock quantity for each product in each retail store from the stock database 6 storing the stock status of each retail store. Get the number of deliveries scheduled for delivery.
[0120]
The order proposal system 2 acquires the reference stock quantity for each store and each product from the retail store system 4. Here, the standard stock quantity is the stock quantity determined by each retail store. For example, when products are displayed on the display shelves of the store, it seems that there is a sense of volume and appeals to customers. It may be a stock quantity. In this case, the reference stock quantity can be determined based on how crowded the display space is or how much it is used. For example, the ratio of the number of displayed products with respect to the number of products that can be displayed in the display space may be used as the occupation rate of the display space crowded or free space. In this case, the reference stock quantity is determined based on an occupation rate that is predetermined for each product.
[0121]
Further, the standard inventory quantity for each product in each retail store is reported to each retail store system 4 order proposal system 2 (S111).
[0122]
Then, the order proposal system 2 calculates the number of order proposals for each store product (S101). An example of the calculation formula at this time is Formula (11).
Number of proposed orders = Forecasted number of sales (for 3 days)-Number of stocks-Number of deliveries + number of base stocks (11)
[0123]
Then, the order proposal system 2 transmits the order proposal number to each retail store system 4 to make an order proposal (S102).
[0124]
Each retail store system 4 receives this order proposal, and the person in charge of the store confirms the number of order proposals and makes an order request (S112). At this time, the number of orders can be changed.
[0125]
In response to the order request from the retail store system 4, the order proposal system 2 places a formal order with the order system 3 (S103).
[0126]
Note that the reference stock quantity may be replaced with a safety stock quantity necessary for preventing out of stock.
[0127]
The above-described embodiments of the present invention are examples for explaining the present invention, and are not intended to limit the scope of the present invention only to those embodiments. Those skilled in the art can implement the present invention in various other modes without departing from the gist of the present invention.
[0128]
For example, the above-described demand forecasting system, ordering proposal system, and ordering system can be implemented in a centralized manner on a single computer machine, or can be realized separately on a single computer machine. However, it can also be realized in a distributed manner by a plurality of computer machines.
[Brief description of the drawings]
FIG. 1 is a diagram showing a configuration of an inventory management system according to an embodiment to which the present invention is applied.
FIG. 2 is a diagram showing a configuration of a demand prediction system according to the present embodiment.
FIG. 3 is a diagram illustrating an example of a data structure of a sales database 5;
FIG. 4 is a flowchart showing a procedure for adjusting selling price / promotion.
5A is a diagram showing an example of the configuration of a selling price totaling database 51, FIG. 5B is an example of the configuration of an adjusted sales number A database 52, and FIG. 5C is a selling price / promotion coefficient table 121;
FIG. 6 is a diagram showing the relationship between the number of sales and the selling price ratio.
FIG. 7 is a diagram showing the relationship between the number of sales and the selling price ratio.
FIG. 8 is a flowchart showing a weather adjustment procedure.
9A is a diagram showing an example of the configuration of a weather summary database 53, FIG. 9B is an example of the configuration of an adjusted sales number B database 54, and FIG. 9C is a weather coefficient table 122;
FIG. 10 is a diagram showing the relationship between the adjusted sales number A and the weather ratio.
FIG. 11 is a flowchart showing a store adjustment procedure;
12A is a diagram showing an example of the configuration of a store total database 55, FIG. 12B is an example of the configuration of an adjusted sales number C database 56, and FIG. 12C is a store coefficient table 123;
FIG. 13 is a flowchart illustrating a holiday adjustment procedure.
14A is a diagram illustrating an example of a configuration of a holiday coefficient table 124, and FIG. 14B is a diagram illustrating an example of a configuration of an adjusted sales number D database 57;
FIG. 15 is a flowchart showing a procedure for day adjustment.
FIG. 16 is a diagram for explaining a procedure for calculating a moving average value;
17A is a diagram showing an example of the configuration of a day of the week coefficient table 125, and FIG.
FIG. 18 is a diagram for explaining a test processing procedure;
FIG. 19 is a diagram showing a configuration of an order proposal system according to the present embodiment.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 ... Demand forecasting system, 2 ... Order proposal system, 3 ... Ordering system, 4 ... Retail store system, 5 ... Sales database, 6 ... Inventory database, 11 ... Sales number adjustment part, 12 ... Coefficient table, 13 ... Reference value prediction , 14 ... coefficient correction unit, 15 ... predicted value calculation unit, 18 ... adjusted sales database, 19 ... advance prediction value database, S1 ... sales price / promotion adjustment process, S2 ... weather adjustment process, S3 ... store adjustment process, S4 ... holiday adjustment process, S5 ... day of the week adjustment process, S6 ... verification process, S7 ... exponential smoothing process, S8 ... model suitability determination process, S9 ... correction process.

Claims

A system for adjusting the number of products sold, comprising a computer system comprising storage means for storing predetermined information, and arithmetic means for executing arithmetic processing using a predetermined processing method stored in the storage means,
The storage means
The standard selling price set for the product and the selling price ratio represented by the ratio of the selling price set at the time of actual sales, and the number of sales of the product sold at the selling price, the same selling price Stores the number of stores where the product was sold and the sales frequency represented by the product of the number of sales days at the sales price, a regression calculation processing method, a ratio calculation processing method, and a division processing method,
The computing means is
The selling price ratio corresponding to the highest sales frequency, the sales volume corresponding to the selling price ratio, and the regression calculation processing method are read from the storage means, the reference sales number is calculated by regression calculation and stored in the storage means,
The calculated reference sales number, the sales number corresponding to the selling price ratio other than the selling price ratio used for the reference sales number, the ratio calculation processing method is read from the storage means, the reference sales number, and the sales number According to the ratio, the coefficient of variation in which the selling price fluctuates the number of sales of the product is calculated and stored in the storage means,
The sales number of the product, the coefficient of variation, and the division processing method are read from the storage unit, and the adjusted number of sales is calculated by dividing the number of sales by the coefficient of variation.
A system for adjusting the number of products sold.

The regression calculation processing method is a regression calculation processing method using a least square method.
The system for adjusting the number of sales of merchandise according to claim 1 .

A system for adjusting the number of products sold, comprising a computer system comprising storage means for storing predetermined information, and arithmetic means for executing arithmetic processing using a predetermined processing method stored in the storage means,
The storage means
For one product, the weather ratio obtained by replacing the weather of each day sold with a predetermined value, the number of sales of the product sold at the weather ratio, the number of stores that sold the product at the same weather ratio, And storing the weather frequency represented by the product of the sales days with the weather ratio, a regression calculation processing method, a ratio calculation processing method, and a division processing method,
The computing means is
The weather ratio corresponding to the highest weather frequency, the sales number of the product sold at the weather ratio, and the regression calculation processing method are read from the storage means, and the reference sales number is calculated by the regression calculation to the storage means. Remember,
The calculated reference sales number, the sales number corresponding to the weather ratio other than the weather ratio used for the reference sales number, and a ratio calculation processing method are read from the storage means, and the weather fluctuates the sales number of the product. Calculating a coefficient of variation in which the degree to be calculated is stored in the storage means;
Read the number of sales of the product sold at the weather ratio, the variation coefficient, and the division processing method from the storage means, and calculate the adjusted number of sales by dividing the sales number by the variation coefficient
A system for adjusting the number of products sold.

The regression calculation processing method is a regression calculation processing method using a least square method,
The relationship between the weather ratio and the number of sales is expressed by a linear function by the regression calculation method.
The sales number adjustment system for merchandise according to claim 3 .

A system for adjusting the number of products sold, comprising a computer system comprising storage means for storing predetermined information, and arithmetic means for executing arithmetic processing using a predetermined processing method stored in the storage means,
The storage means
For one product, the total number of sales in a predetermined period of a plurality of stores, the number of sales of the product in a predetermined period in a specific store, the number of sales in a specific store for the day, and a store code that identifies each store And the store code of the store that is open on that day, the division processing method, and the addition processing method,
The computing means is
The total number of sales within a predetermined period of the plurality of stores, the number of sales within a predetermined period of the specific store, the division processing method is read from the storage means, and the total number of sales at the plurality of stores as a whole, According to the ratio of the number of sales at a specific store, the number of sales at the specific store calculates a coefficient of variation that quantifies the degree to which the number of sales of the product varies, and stores it in the storage means,
The number of sales of the specific store for the day, the store code of the store corresponding to the calculated coefficient of variation, the store code of the store operating on the day are read from the storage means, and the day of the store at the specific store Divide the number of sales by the coefficient of variation that is the sum of the coefficients of variation corresponding to the stores that are open on that day to calculate the adjusted number of sales.
A system for adjusting the number of products sold.

A system for adjusting the number of products sold, comprising a computer system comprising storage means for storing predetermined information, and arithmetic means for executing arithmetic processing using a predetermined processing method stored in the storage means,
The storage means
Stores the average number of sales on weekdays, the number of sales on holidays, the average value of sales on holidays, and the division processing method for one product,
The computing means is
The average value of the number of sales on the weekday, the average value of the sales number on the holiday, and the division processing method are read from the storage unit, and the average value of the sales number on the holiday is compared with the average value of the sales number on the weekday. According to the ratio, the holiday calculates a coefficient of variation obtained by quantifying the degree to which the number of sales of the product varies.
Read the number of sales on the holidays, the coefficient of variation, and the division processing method from the storage unit, and calculate the number of adjusted sales by dividing the number of sales on the holidays by the coefficient of variation.
A system for adjusting the number of products sold.

A system for adjusting the number of products sold, comprising a computer system comprising storage means for storing predetermined information, and arithmetic means for executing arithmetic processing using a predetermined processing method stored in the storage means,
The storage means
For one product, memorize the number of daily sales associated with the day of the week, a moving average calculation processing method, a division processing method, and an average value calculation processing method,
The computing means is
The number of sales on a specific day, the number of sales for seven days before and after that day, and the moving average calculation processing method are read from the storage means, and the moving average is calculated by the moving average to the storage means. Remember,
The number of sales of the specific day, the moving average of the specific day, the division processing method, and the average value calculation processing method are read from the storage means, and the number of sales of the specific day is divided by the moving average of the specific day By calculating the average number of sales, the day of the week corresponding to the specific day calculates a coefficient of variation that quantifies the degree to which the number of sales of the product varies, and stores it in the storage means,
Read the number of sales, the coefficient of variation, and the division processing method from the storage means, and divide the number of sales by the coefficient of variation to calculate the adjusted number of sales
A system for adjusting the number of products sold.

The storage means further stores an exponential smoothing processing method and a multiplication processing method,
The calculation means reads the adjusted sales number, the index smoothing processing method from the storage means, calculates a reference value by the index smoothing method, stores the reference value in the storage means,
The calculated reference value, the variation coefficient, and the multiplication processing method are read from the storage unit, and the reference value and the variation coefficient are multiplied to further calculate the number of future sales of the one product.
8. The system for adjusting the number of sales of merchandise according to claim 1, wherein:

A system for adjusting the number of products sold, comprising a computer system comprising storage means for storing predetermined information, and arithmetic means for executing arithmetic processing using a predetermined processing method stored in the storage means,
The storage means
The standard selling price set for the product, the selling price ratio represented by the ratio of the selling price set at the time of actual sale, the number of sales of the product sold at the selling price, and the sale of the product The sales frequency represented by the product of the number of stores that sold the product at the same sales price and the number of sales days at the sales price, and the weather on each day the product was sold. Stores that identify individual stores and the weather frequency represented by the product of the weather ratio replaced with a predetermined numerical value, the number of stores that sold the product at the same weather ratio, and the number of sales days at the weather ratio Code, store code of the store that is open on that day, regression calculation processing method, ratio calculation processing method, addition processing method, average value calculation processing method, moving average calculation processing method, and division processing method Remember,
The computing means is
For one product, the selling price ratio corresponding to the highest sales frequency, the number of sales corresponding to the selling price ratio, and the regression calculation processing method are read from the storage means, and the reference sales number is calculated by regression calculation and the storage means Remember
The calculated reference sales number, the sales number corresponding to the selling price ratio other than the selling price ratio used for the reference sales number, the ratio calculation processing method is read from the storage means, the reference sales number, and the sales number According to the ratio, the selling price coefficient that quantifies the degree to which the selling price fluctuates the number of sales of the product is calculated and stored in the storage means,
Read the number of sales of the product, the coefficient of variation, and the division processing method from the storage means, divide the number of sales by the selling price coefficient, calculate the first adjusted sales number and store it in the storage means,
For the one product, the weather ratio corresponding to the highest weather frequency, the first adjusted sales number of the product sold at the weather ratio, and the regression calculation processing method are read from the storage means, and a reference is obtained by regression calculation. Calculate the number of sales and store in the storage means,
The calculated reference sales number, the first adjusted sales number corresponding to the weather ratio other than the weather ratio used for the reference sales number, and a ratio calculation processing method are read from the storage unit, and the weather is the product Calculating a weather coefficient that quantifies the degree of change in the number of sales and storing it in the storage means;
The first adjusted sales number, the weather coefficient, and the division processing method of the product sold at the weather ratio are read from the storage unit, and the sales number is divided by the weather coefficient to obtain a second adjusted sales number. Is calculated and stored in the storage means,
For one product, the second adjusted sales number of the plurality of stores within a predetermined period and the addition processing method are read from the storage means, and the total of the second adjusted sales number of the plurality of stores within the predetermined period is calculated. And storing in the storage means,
The calculated sum of the second adjusted sales numbers of the plurality of stores within a predetermined period, the second adjusted sales number of the specific stores within the predetermined period, and the division processing method are read from the storage unit, The number of sales at the specific store varies the number of sales of the product according to the ratio of the second adjusted sales at the specific store with respect to the total of the second adjusted sales at the entire store. A store coefficient obtained by quantifying the degree is calculated and stored in the storage means,
The second adjusted number of sales of the specific store for the day, the store code of the store corresponding to the calculated coefficient of variation, and the store code of the store operating on the day are read from the storage means, Dividing the second adjusted sales number of the day at the store by the sum of the store coefficients corresponding to the stores operating on that day, and calculating the third adjusted sales number, the memory Memorize in the means,
For the one product, the third adjusted sales number and average value calculation processing method is read from the storage means, and the average value of the third adjusted sales number on weekdays and the average value of sales numbers on holidays are calculated and Memorize it in the memory means,
The calculated average value of the third adjusted sales number on the weekday, the average value of the third adjusted sales number on the holiday, and the division processing method are read from the storage unit, and the third adjusted sales number on the weekday is calculated. Based on the ratio of the average value of the third adjusted sales numbers of the holidays with respect to the average value, a holiday coefficient in which the degree of fluctuation of the sales numbers of the products by the holidays is calculated and stored in the storage unit And
The third adjusted sales number for the holiday, the variation coefficient, and the division processing method are read from the storage unit, and the third adjusted sales number for the holiday is divided by the holiday coefficient to obtain the fourth adjusted adjusted sales. A number is calculated and stored in the storage means;
For the one product, the fourth adjusted sales number for a specific day, the fourth adjusted sales number for seven days before and after that day, and the moving average calculation processing method are read from the storage means, Calculate the moving average of a specific day and store it in the storage means;
The fourth adjusted sales number on the specific day, the moving average of the specific day, the division processing method, and the average value calculation processing method are read from the storage unit, and the fourth adjusted sales number on the specific day is specified. By calculating the number of sales obtained by dividing by the moving average of the day, the day of the week corresponding to the specific day calculates the day of the week coefficient that quantifies the degree to which the number of sales of the product fluctuates and calculates the storage Memorize in the means,
The fourth adjusted sales number, the variation coefficient, and the division processing method are read from the storage unit, and the fourth adjusted sales number is divided by the day of the week coefficient to calculate the fifth adjusted sales number.
A system for adjusting the number of products sold.

The storage means further stores an exponential smoothing processing method and a multiplication processing method,
The calculation means reads the fourth adjusted sales number, the exponent smoothing method from the storage means, calculates a reference value by the exponent smoothing method, stores the reference value in the storage means,
The calculated reference value, the coefficient, and the multiplication processing method are read from the storage means, and the reference value and the coefficient are multiplied to further calculate the future sales number of the one product.
The system for adjusting the number of sales of merchandise according to claim 9.

A method for adjusting the number of sales of products performed by a computer system comprising storage means for storing predetermined information and calculation means for executing calculation processing using a predetermined processing method stored in the storage means,
The storage means
The standard selling price set for the product, the selling price ratio represented by the ratio of the selling price set at the time of actual sale, the number of sales of the product sold at the selling price, and the sale of the product The sales frequency represented by the product of the number of stores that sold the product at the same sales price and the number of sales days at the sales price, and the weather of each day when the product was sold Stores that identify individual stores and the weather frequency represented by the product of the weather ratio replaced with a predetermined numerical value, the number of stores that sold the product at the same weather ratio, and the number of sales days at the weather ratio Code, store code of the store that is open on that day, regression calculation processing method, ratio calculation processing method, addition processing method, average value calculation processing method, moving average calculation processing method, and division processing method Remember,
The computing means is
For one product, the selling price ratio corresponding to the highest sales frequency, the number of sales corresponding to the selling price ratio, and the regression calculation processing method are read from the storage means, and the reference sales number is calculated by regression calculation and the storage means Remember
The calculated reference sales number, the sales number corresponding to the selling price ratio other than the selling price ratio used for the reference sales number, the ratio calculation processing method is read from the storage means, the reference sales number, and the sales number According to the ratio, the selling price coefficient that quantifies the degree to which the selling price fluctuates the number of sales of the product is calculated and stored in the storage means,
Read the number of sales of the product, the coefficient of variation, and the division processing method from the storage means, divide the number of sales by the selling price coefficient, calculate the first adjusted sales number and store it in the storage means,
For the one product, the weather ratio corresponding to the highest weather frequency, the first adjusted sales number of the product sold at the weather ratio, and the regression calculation processing method are read from the storage means, and a reference is obtained by regression calculation. Calculate the number of sales and store in the storage means,
The calculated reference sales number, the first adjusted sales number corresponding to the weather ratio other than the weather ratio used for the reference sales number, and a ratio calculation processing method are read from the storage unit, and the weather is the product Calculating a weather coefficient that quantifies the degree of change in the number of sales and storing it in the storage means;
The first adjusted sales number, the weather coefficient, and the division processing method of the product sold at the weather ratio are read from the storage unit, and the sales number is divided by the weather coefficient to obtain a second adjusted sales number. Is calculated and stored in the storage means,
For one product, the second adjusted sales number of the plurality of stores within a predetermined period and the addition processing method are read from the storage means, and the total of the second adjusted sales number of the plurality of stores within the predetermined period is calculated. And storing in the storage means,
The calculated sum of the second adjusted sales numbers of the plurality of stores within a predetermined period, the second adjusted sales number of the specific stores within the predetermined period, and the division processing method are read from the storage unit, The number of sales at the specific store varies the number of sales of the product according to the ratio of the second adjusted sales at the specific store with respect to the total of the second adjusted sales at the entire store. A store coefficient obtained by quantifying the degree is calculated and stored in the storage means,
The second adjusted number of sales of the specific store for the day, the store code of the store corresponding to the calculated coefficient of variation, and the store code of the store operating on the day are read from the storage means, Dividing the second adjusted sales number of the day at the store by the sum of the store coefficients corresponding to the stores operating on that day, and calculating the third adjusted sales number, the memory Memorize in the means,
For the one product, the third adjusted sales number and average value calculation processing method is read from the storage means, and the average value of the third adjusted sales number on weekdays and the average value of sales numbers on holidays are calculated and Memorize it in the memory means,
The calculated average value of the third adjusted sales number on the weekday, the average value of the third adjusted sales number on the holiday, and the division processing method are read from the storage unit, and the third adjusted sales number on the weekday is calculated. Based on the ratio of the average value of the third adjusted sales numbers of the holidays with respect to the average value, a holiday coefficient in which the degree of fluctuation of the sales numbers of the products by the holidays is calculated and stored in the storage unit And
The third adjusted sales number for the holiday, the variation coefficient, and the division processing method are read from the storage unit, and the third adjusted sales number for the holiday is divided by the holiday coefficient to obtain the fourth adjusted adjusted sales. A number is calculated and stored in the storage means;
For the one product, the fourth adjusted sales number for a specific day, the fourth adjusted sales number for seven days before and after that day, and the moving average calculation processing method are read from the storage means, Calculate the moving average of a specific day and store it in the storage means;
The fourth adjusted sales number on the specific day, the moving average of the specific day, the division processing method, and the average value calculation processing method are read from the storage unit, and the fourth adjusted sales number on the specific day is specified. By calculating the number of sales obtained by dividing by the moving average of the day, the day of the week corresponding to the specific day calculates the day of the week coefficient that quantifies the degree to which the number of sales of the product fluctuates and calculates the storage Memorize in the means,
The fourth adjusted sales number, the variation coefficient, and the division processing method are read from the storage unit, and the fourth adjusted sales number is divided by the day of the week coefficient to calculate the fifth adjusted sales number.
The method for adjusting the number of sales of products characterized by this.

The storage means further stores an exponential smoothing processing method and a multiplication processing method,
The calculation means reads the fourth adjusted sales number and the index smoothing method from the storage means, calculates a reference value by the index smoothing method, stores the reference value in the storage means, and stores it in the storage means And
The calculated reference value, the coefficient, and the multiplication processing method are read from the storage unit, and the reference value and the coefficient are multiplied to further calculate the future sales number of the one product.
The method for adjusting the number of sales of merchandise according to claim 11.

A computer program comprising: a storage means for storing predetermined information; and an arithmetic means for executing arithmetic processing using a predetermined processing method stored in the storage means.
In the storage means,
The standard selling price set for the product, the selling price ratio represented by the ratio of the selling price set at the time of actual sales, the number of sales of the product sold at the selling price, and the sale of the product The sales frequency represented by the product of the number of stores that sold the product at the same sales price and the number of sales days at the sales price, and the weather on each day the product was sold. Stores that identify individual stores and the weather frequency represented by the product of the weather ratio replaced with a predetermined numerical value, the number of stores that sold the product at the same weather ratio, and the number of sales days at the weather ratio Code, store code of the store that is open on that day, regression calculation processing method, ratio calculation processing method, addition processing method, average value calculation processing method, moving average calculation processing method, and division processing method Remember,
The calculation means includes
For one commodity, the selling price ratio corresponding to the highest sales frequency, the sales volume corresponding to the selling price ratio, and the regression calculation processing method are read from the storage means, and the reference sales number is calculated by regression calculation, and the storage means Remember
The calculated reference sales number, the sales number corresponding to the selling price ratio other than the selling price ratio used for the reference sales number, the ratio calculation processing method is read from the storage means, the reference sales number, and the sales number According to the ratio, the selling price coefficient that calculates the degree to which the selling price fluctuates the number of sales of the product is calculated and stored in the storage means,
Read the number of sales of the product, the coefficient of variation, and the division processing method from the storage unit, divide the number of sales by the selling price coefficient, calculate the first adjusted sales number, and store it in the storage unit,
For the one product, the weather ratio corresponding to the highest weather frequency, the first adjusted sales number of the product sold at the weather ratio, and the regression calculation processing method are read from the storage means, and a reference is obtained by regression calculation. Calculate the number of sales and store in the storage means,
The calculated reference sales number, the first adjusted sales number corresponding to the weather ratio other than the weather ratio used for the reference sales number, and a ratio calculation processing method are read from the storage unit, and the weather is the product Let the storage means calculate and calculate a weather coefficient that quantifies the degree of change in the number of sales,
The first adjusted sales number, the weather coefficient, and the division processing method of the product sold at the weather ratio are read from the storage unit, and the sales number is divided by the weather coefficient to obtain a second adjusted sales number. Is calculated and stored in the storage means,
For one product, the second adjusted sales number of the plurality of stores within a predetermined period and the addition processing method are read from the storage means, and the total of the second adjusted sales number of the plurality of stores within the predetermined period is calculated. Stored in the storage means,
The calculated sum of the second adjusted sales numbers of the plurality of stores within a predetermined period, the second adjusted sales number of the specific stores within the predetermined period, and the division processing method are read from the storage unit, The number of sales at the specific store varies the number of sales of the product according to the ratio of the second adjusted sales at the specific store with respect to the total of the second adjusted sales at the entire store. Store the store coefficient with the degree converted into a numerical value and store it in the storage means;
The second adjusted number of sales of the specific store for the day, the store code of the store corresponding to the calculated coefficient of variation, and the store code of the store operating on the day are read from the storage means, The second adjusted number of sales for the day at the store is divided by the sum of the store coefficients corresponding to the stores operating on that day, and the third adjusted number of sales is calculated to store the memory. Memorize the means,
For the one product, the third adjusted sales number, average value calculation processing method is read from the storage means, and the average value of the third adjusted sales number on weekdays and the average value of sales numbers on holidays are calculated, and Memorize it in the memory means,
The calculated average value of the third adjusted sales number on the weekday, the average value of the third adjusted sales number on the holiday, and the division processing method are read from the storage unit, and the third adjusted sales number on the weekday is calculated. Based on the ratio of the average value of the third adjusted sales number of the holidays with respect to the average value, the holiday calculates a holiday coefficient in which the degree to which the number of sales of the product fluctuates is calculated and stored in the storage unit Let
The third adjusted sales number of the holiday, the variation coefficient, and the division processing method are read from the storage unit, and the third adjusted sales number of the holiday is divided by the holiday coefficient to obtain a fourth adjusted sales number. Calculated and stored in the storage means,
For the one product, the fourth adjusted sales number for a specific day, the fourth adjusted sales number for seven days before and after that day, and the moving average calculation processing method are read from the storage means, Calculate the moving average of a specific day and store it in the storage means,
The fourth adjusted sales number on the specific day, the moving average of the specific day, the division processing method, and the average value calculation processing method are read from the storage unit, and the fourth adjusted sales number on the specific day is specified. By averaging the number of sales obtained by dividing by the moving average of the day, the day of the week corresponding to the specific day calculates the day of the week coefficient that quantifies the degree to which the number of sales of the product fluctuates and calculates the memory Memorize the means,
The fourth adjusted sales number, the variation coefficient, and the division processing method are read from the storage unit, and the fourth adjusted sales number is divided by the day of the week coefficient to calculate the fifth adjusted sales number.
A computer program for causing a computer system to adjust the number of items sold.

The storage means further stores an exponential smoothing processing method and a multiplication processing method,
The calculation means reads the fourth adjusted sales number, the index smoothing processing method from the storage means, calculates a reference value by the index smoothing method, stores the reference value in the storage means,
The calculated reference value, the coefficient, and the multiplication processing method are read from the storage unit, and the reference value and the coefficient are multiplied to further calculate the future sales number of the one product.
A computer program for causing a computer system according to claim 9 to adjust the number of items sold.