JP7315733B2 - New product sales forecast method - Google Patents
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Description
本発明は新製品の販売量の予測方法に関する。 The present invention relates to a method for predicting sales volume of new products.
ビジネス経営において、製品ニーズの予測は１つ重要な課題であり、その中で、新製品の販売量の予測がそのうちの１つの難しいが、重要なサブ課題である。近代的な生産において、製品のライフサイクルは著しく短縮され、特に、携帯電話、自動車等の分野の製品は、新製品の発表が非常に頻繁である。企業はまもなく市販製品の販売量の見通しについて差し迫ったニーズを持っており、新製品の上場初期の販売量を正確に予測することで企業の生産、マーケティングの配置に意思決定の根拠を提供することができる。新製品の販売量の予測は一般製品の販売量の予測よりもさらに難しい。その主な原因は製品が上場前に如何なる履歴を示す販売量データもなく、この履歴を示す販売量データから販売量の規則を学ぶことができない。 In business management, forecasting product needs is one of the important tasks, in which forecasting sales volume of new products is one of the difficult but important sub-tasks. In modern production, the life cycle of products has been significantly shortened, and especially in the fields of mobile phones, automobiles, etc., new product releases are very frequent. Enterprises will soon have an urgent need for forecasting the sales volume of commercial products. Accurately forecasting the sales volume of new products in the initial stage of listing can provide a decision-making basis for the production and marketing layout of enterprises. Forecasting the sales volume of new products is more difficult than forecasting the sales volume of general products. The main reason is that there is no sales volume data that shows any history before the product is listed, and the sales volume rule cannot be learned from the sales volume data that shows this history.
現在、新製品の販売量への予測は主にエキスパート評価法により、即ち企業は複数名の当該分野のエキスパートからなる評価チームを設立し、エキスパートの経験及び過去製品への研究に基づいて、討論によってコンセンサスを達成し、新しい市販製品の市場に現れる初期の販売量の見通しをつける。しかし、当該方法はコストが高く、大きい主観性を有し、既にあった類似した市販製品の履歴データへの有効利用も足りない。 At present, the forecasting of new product sales is mainly based on the expert evaluation method, that is, an enterprise sets up an evaluation team consisting of experts in the field, based on the experts' experience and research on past products, achieves consensus through discussion, and forecasts the initial sales volume of new products on the market. However, the method is costly, subjective, and does not make good use of existing historical data of similar commercial products.
なお、販売についての履歴データを用いて、販売予測を行う技術については、特許文献１に記載されている。 A technique for predicting sales using history data on sales is described in Patent Document 1.
これに対し、本発明の目的は、市販製品の販売量データを利用して、客観的、低コストで正確に新製品の販売量を予測できる新製品の販売量の予測方法を提供することにある。 On the other hand, it is an object of the present invention to provide a method for predicting the sales volume of a new product that can accurately predict the sales volume of a new product objectively and at low cost by using the sales volume data of commercial products.
上記の目的を達成するために、本発明の新製品の販売量の予測方法は、コンピュータにより、新製品のあらゆる特徴を含む特徴集合を構築する特徴集合構築ステップと、前記特徴集合中の全部又は一部の特徴を有する市販製品の販売量を取得する販売量データ取得ステップと、前記特徴集合から１つの特徴部分集合を抽出し、類似性計量関数を使用することにより、前記市販製品から特徴部分集合中の前記特徴を有し且つ前記特徴部分集合で前記新製品に類似する複数の類似製品を選別する類似製品選別ステップと、複数の前記類似製品の販売量の差異度を算出し、もし前記差異度が予め所定した許容値よりも小さいと、複数の前記類似製品の販売量が一致性検査にパスしたと認定し、さもなくば、複数の前記類似製品の販売量が一致性検査にパスしていないと認定する販売量一致性検査ステップと、前記一致性検査にパスした複数の前記類似製品の販売量を使用して、前記新製品の販売量を予測する新製品の販売量予測ステップと、を含み、複数の前記類似製品の販売量が一致性検査にパスしていない場合、前記特徴集合から抽出された前記特徴部分集合とは異なる特徴部分集合を抽出し、類似製品選別ステップで選別された前記類似製品の販売量が一致性検査にパスするまで、前記類似製品選別ステップ及び前記販売量一致性検査ステップを繰り返すことを特徴とする。 To achieve the above objects, the method for predicting the sales volume of a new product of the present invention comprises a feature set construction step of constructing a feature set including all features of a new product by a computer, a sales volume data acquisition step of obtaining the sales volume of a commercial product having all or part of the features in the feature set, and a plurality of similar products having the features in the feature subset and similar to the new product in the feature subset by extracting one feature subset from the feature set and using a similarity metric function from the commercial product. a similar product sorting step of sorting; a sales volume consistency inspection step of calculating a difference in sales volume of a plurality of similar products, determining that the sales volumes of the plurality of similar products have passed the consistency inspection if the difference is smaller than a predetermined allowable value; otherwise, determining that the sales volumes of the plurality of similar products have not passed the consistency inspection; , wherein if the sales volumes of a plurality of similar products do not pass the consistency check, a feature subset different from the feature subset extracted from the feature set is extracted, and the similar product selection step and the sales volume consistency check step are repeated until the sales volume of the similar products selected in the similar product selection step passes the consistency check.
本発明によれば、市販製品の販売量データを利用して、客観的、低コストで正確に新製品の販売量を予測できる。 According to the present invention, it is possible to accurately predict the sales volume of a new product objectively and at low cost by using the sales volume data of commercial products.
以下、特定の具体的な実施例で本発明の実施形態を説明する。当業者は本明細書に開示された内容から本発明のその他の優れた点及び効能を容易に理解することができる。本発明の記述は好適な実施例を合わせて一緒に紹介するが、しかし、これは決して本発明の特徴が当該実施形態のみに限ることを表すものではない。まさしく逆であり、実施形態を合わせて本発明を紹介する目的は本発明の特許請求の範囲に基づいて延出し得るその他の選択又は改造を包むことである。本発明への理解を深めるために、以下の記述において、多くの具体的な細部を含む。本発明はこれらの細部を使用せずに実施することもできる。この他、本発明の重点を混乱又は混同させることを避けるために、ある具体的な細部は記述において省略される。なお、コンフリクトしない場合、本発明中の実施例及び実施例中の特徴を互いに組み合わせることができる。 Certain specific examples follow to illustrate embodiments of the invention. A person skilled in the art can easily understand other advantages and advantages of the present invention from the contents disclosed herein. Although the description of the present invention is presented together with preferred embodiments, it is by no means intended to imply that the features of the present invention are limited to those embodiments only. Quite the contrary, the purpose of presenting the invention in conjunction with the embodiments is to cover any other options or modifications that may be extended under the scope of the claims of the invention. To provide a better understanding of the present invention, the following description contains many specific details. The invention may be practiced without these details. In addition, certain specific details are omitted in the description to avoid confusing or confounding the emphasis of the invention. In addition, when there is no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.
図１は、本発明の新製品の販売量の予測方法のフローチャートである。本発明では、新製品は、市場に出されていない製品であり、如何なる販売量の履歴データもない製品であって、それは全く新しい製品であってもよく、旧製品の新型であってもよい。本発明は以上２種類の新製品の販売量を予測することができる。 FIG. 1 is a flow chart of the new product sales volume prediction method of the present invention. In the present invention, a new product is a product that has not been put on the market and does not have any historical data of sales volume, which may be a completely new product or a new version of an old product. The present invention can predict the sales volume of the above two types of new products.
本発明の発明者は、特徴が類似している製品の販売量が類似しているはずだと想到したため、新製品に類似している市販製品を探し、市販製品の過去の販売量を使用することにより、新製品の販売量を予測することができる。類似計量関数を構造することで、新製品と市販製品との類似性を比較し、市販製品から新製品に類似する複数の類似製品を探し出す。しかし、探し出した複数の類似製品の間の販売量が類似しない可能性があり、「特徴が類似している商品の販売量が類似するべきである」という仮定を満たさない。したがって、本発明は、特徴部分集合検索、販売量一致性検査をさらに取り入れることでこの問題を解決するのに用いられる。 Since the inventors of the present invention conceived that the sales volume of products with similar characteristics should be similar, the sales volume of the new product can be predicted by looking for commercial products that are similar to the new product and using the past sales volume of the commercial product. By constructing a similarity metric function, the similarity between the new product and the commercial product is compared, and multiple similar products similar to the new product are found from the commercial products. However, there is a possibility that the sales volumes of the similar products found may not be similar, and the assumption that "products with similar characteristics should have similar sales volumes" is not satisfied. Therefore, the present invention is used to solve this problem by further incorporating feature subset search, sales volume consistency test.
したがって、図１に示すように、本発明のコンピュータにより実行される、新製品の販売量の予測方法は、主に特徴集合構築ステップ、販売量データ取得ステップ、類似製品選別ステップ、販売量一致性検査ステップ、及び新製品の販売量予測ステップを含む。以下、各ステップについて説明するが、これらはコンピュータで実行される。 Therefore, as shown in FIG. 1, the computer-implemented new product sales volume prediction method of the present invention mainly includes a feature set construction step, a sales volume data acquisition step, a similar product selection step, a sales volume consistency test step, and a new product sales volume prediction step. Each step is described below and is executed by a computer.
特徴集合構築ステップでは、新製品のあらゆる特徴を含む特徴集合を構築する。特徴は使用者が販売量に対して影響を有すると思う特徴、例えば、外観、価格、性能等である。
販売量データ取得ステップでは、前記特徴集合中の全部又は一部の特徴を有する市販製品の販売量を取得する。ここで、本発明において、新製品の販売量を予測する時間粒度（時間単位）、例えば、月間予測、週間予測、及び予測しようとする時間長さを確認し、それから、上場商品に対応する粒度、対応した時間長さの販売量データ、例えば、月間販売量、週間販売量、及び何ヶ月の月間販売量、何週間の周間販売量を収集する。
The feature set building step builds a feature set that includes all the features of the new product. Features are features that users perceive to have an impact on sales volume, such as appearance, price, performance, and the like.
The sales volume data obtaining step obtains the sales volume of the commercial product having all or part of the features in the feature set. Here, in the present invention, the time granularity (hour unit) for predicting the sales volume of the new product is confirmed, for example, monthly prediction, weekly prediction, and the time length to be predicted, and then the granularity corresponding to the listed product and the sales volume data of the corresponding time length, such as monthly sales volume, weekly sales volume, monthly sales volume for several months, and weekly sales volume for several weeks are collected.
この他、本発明では、収集した市販製品の特徴の特徴値に対してデータ処理をさらに行うことができる。例えば、欠損値を補完し、連続的な特徴（数値特徴）について、市販製品の当該特徴平均値を用いて補完を行うことができる。類別特徴について、１つの「未知」という類別を新規作成して補完を行う。予測の正確性を保証するために、市販製品の販売量に欠損を有することを許さない。連続的な特徴について、各サンプルの当該特徴における特徴値の単位の一致を保持することを確保する必要がある。類別特徴について、同一類別には異なる表現が存在する場合、当該表現を調整し、表現の一致を保持することを確保する。 In addition, the present invention can further perform data processing on the feature values of the features of the commercial products collected. For example, missing values can be imputed, and continuous features (numerical features) can be imputed using the feature mean value of the commercial product. For the classification feature, one new classification "unknown" is created to complement it. To ensure the accuracy of our forecasts, we do not allow deficiencies in the sales volume of our commercial products. For consecutive features, it is necessary to ensure that the unit of feature value for that feature in each sample remains consistent. For class features, if there are different wordings for the same class, adjust the wordings to ensure that wording consistency is maintained.
類似製品選別ステップでは、前記特徴集合から１つの特徴部分集合を抽出し、類似性計量関数を使用することで、前記市販製品から前記特徴部分集合中の前記特徴を有し、且つ前記特徴部分集合で前記新製品に類似する複数の類似製品を選別する。 In the similar product selection step, one feature subset is extracted from the feature set, and a similarity metric function is used to select a plurality of similar products from the commercial products that have the features in the feature subset and are similar to the new product in the feature subset.
本発明では、類似性計量関数を構造し、２つの製品の類似性を測定する。本発明は重み付け余弦距離又は重み付けユークリッド距離を使用して新製品と市販製品との類似性を計量する。即ち、前記類似性計量関数は、（数１）に示す重み付け余弦距離関数であってもよい。 We construct a similarity metric function to measure the similarity of two products. The present invention uses weighted cosine distance or weighted Euclidean distance to measure similarity between new and commercial products. That is, the similarity metric function may be a weighted cosine distance function shown in (Formula 1).
（数１）中、d(xi,xj)は前記新製品と第j個の前記市販製品との重み付け余弦距離、xi,kは前記新製品の第k個の特徴の特徴値、xj,kは第j個の前記市販製品の第k個の特徴の特徴値、Pは前記特徴部分集合中の前記特徴の数量、wkは予め設定した第k個の特徴の重み、且つ（数２）である。 In (1), d(x i , x j ) is the weighted cosine distance between the new product and the j-th commercial product, x i,k is the feature value of the k-th feature of the new product, x j,k is the feature value of the k-th feature of the j-th commercial product, P is the number of the features in the feature subset, w k is the preset weight of the k-th feature, and (2).
また、前記特徴が数値特徴である場合、前記特徴値として当該特徴の数値を用い、前記特徴が類別特徴である場合、前記新製品の特徴と前記市販製品の特徴とが同一であるとき、前記新製品と前記市販製品の当該特徴の特徴値をともに０とし、前記新製品と前記市販製品との特徴が異なるとき、前記新製品と前記市販製品の当該特徴の特徴値をともに１と記し、前記重み付け余弦距離が小さい所定数量の前記市販製品を前記類似製品とする。使用者はニーズ（予測精度、計算能力等）に応じて当該所定数量を決定することができ、即ち何個の市販製品を選んで類似製品とすることを決定することができる。 In addition, when the feature is a numerical feature, the numerical value of the feature is used as the feature value, and when the feature is a categorical feature, when the feature of the new product and the feature of the commercial product are the same, the feature value of the feature of the new product and the commercial product are both set to 0, and when the features of the new product and the commercial product are different, the feature value of the feature of the new product and the commercial product are both set to 1, and a predetermined number of the commercial products having a small weighted cosine distance are set as the similar products. The user can determine the predetermined quantity according to his/her needs (prediction accuracy, computing power, etc.), ie, how many commercial products can be selected as similar products.
前記類似性計量関数は、（数３）に示す重み付けユークリッド距離関数であってもよい。 The similarity metric function may be a weighted Euclidean distance function shown in (Equation 3).
式中、d(xi,xj)は前記新製品と第j個の前記市販製品との重み付けユークリッド距離、xi,kは前記新製品の第k個の特徴の特徴値、xj,kは第j個の前記市販製品の第k個の特徴の特徴値、Pは前記特徴部分集合中の前記特徴の数量、wkは予め設定した第k個の特徴の重み、且つ（数２）である。 where d(x i , x j ) is the weighted Euclidean distance between the new product and the j-th commercial product, x i,k is the feature value of the k-th feature of the new product, x j,k is the feature value of the k-th feature of the j-th commercial product, P is the number of the features in the feature subset, w k is the preset weight of the k-th feature, and (Equation 2).
また、前記特徴が数値特徴である場合、前記特徴値として当該特徴の数値を用い、前記特徴が類別特徴である場合、前記新製品と前記市販製品との特徴が同一である場合、xi,k－xj,kを０とし、前記新製品と前記市販製品との特徴が異なる場合、xi,k- xj,kを１とし、前記重み付けユークリッド距離が小さい所定数量の前記市販製品を前記類似製品とする。使用者はニーズ（予測精度、計算能力等）に応じて当該所定数量を決定することができ、即ち何個の市販製品を選んで類似製品とすることを決定することができる。 In addition, when the feature is a numerical feature, the numerical value of the feature is used as the feature value, and when the feature is a categorical feature, x i,k - x j,k is set to 0 when the features of the new product and the commercial product are the same, and x i,k - x j,k is set to 1 when the features of the new product and the commercial product are different, and a predetermined number of the commercial products with a small weighted Euclidean distance are set to the similar products. The user can determine the predetermined quantity according to his/her needs (prediction accuracy, computing power, etc.), ie, how many commercial products can be selected as similar products.
（数３）、重みwkは特徴の重要性を表し、重みwkは使用者の特徴への理解によって人で与えることができ、又は販売量と当該特徴の類似性を検査することで、相関性の大きい特徴により大きい重みを与えることができる。 (Equation 3), the weight w k represents the importance of the feature, and the weight w k can be given by a person according to the user's understanding of the feature, or by examining the similarity of the sales volume and the feature, the feature with high correlation can be given a higher weight.
販売量一致性検査ステップでは、複数の前記類似製品の販売量の差異度を算出し、もし前記差異度が予め所定した許容値より小さいと、複数の前記類似製品の販売量が一致性検査にパスしたと認定し、さもなくば、複数の前記類似製品の販売量が一致性検査にパスしなかったと認定する。 In the sales volume consistency checking step, the degree of difference in sales volume of the plurality of similar products is calculated, and if the difference is smaller than a predetermined allowable value, it is determined that the sales volume of the plurality of similar products has passed the consistency test, otherwise, it is determined that the sales volume of the plurality of similar products has not passed the consistency test.
ここで、前記差異度は（数４）によって算出することができる。 Here, the degree of difference can be calculated by (Equation 4).
（数４）中、εは前記差異度、yinは第n個の前記類似製品の第iの時間単位における販売量、max yinは前記yin中の最大値、即ち複数の類似製品の第iの時間単位における販売量の最大値、min yinは前記yin中の最小値、即ち複数の類似製品の第iの時間単位における販売量の最小値、median yinは前記yin中の中位値、即ち複数の類似製品の第iの時間単位における販売量の中位値、Mは前記類似製品の販売量の期間中に含まれる時間単位の数量である。 In (Formula 4), ε is the difference, y in is the sales volume of the n-th similar products in the i-th time unit, max y in is the maximum value in y in , that is, the maximum sales volume of multiple similar products in the i-th time unit, min y in is the minimum value in y in , that is, the minimum sales volume of multiple similar products in the i-th time unit, and median y in is the median value in y in , that is, the sales volume of multiple similar products in the i-th time unit. The median value of , M is the number of hourly units included in the sales volume period of said similar product.
（数５）中、εは前記差異度、sigmaiは複数の前記類似製品の第iの時間単位における販売量の平均値、muiは複数の前記類似製品の第iの時間単位の販売量の標準偏差 である。 In (Equation 5), ε is the degree of difference, sigma i is the average value of the sales volume of the plurality of similar products in the i-th time unit, and mu i is the standard deviation of the sales volume of the plurality of similar products in the i-th time unit.
複数の前記類似製品の販売量が一致性検査にパスしなかった場合、即ち、前記差異度が所定の許容度以上の場合に、類似製品選別ステップで選別された前記類似製品の販売量が一致性検査にパスするまで、前記特徴集合から抽出された前記特徴部分集合とは異なる別の特徴部分集合を抽出し、前記類似製品選別ステップ及び前記販売量一致性検査ステップを繰り返す。 If the sales volumes of a plurality of similar products do not pass the consistency check, that is, if the degree of difference is equal to or greater than a predetermined tolerance, another feature subset different from the feature subset extracted from the feature set is extracted, and the similar product selection step and the sales volume consistency check step are repeated until the sales volumes of the similar products selected in the similar product selection step pass the consistency check.
本発明では、ランダム法、減少法、又は増加法等を用いて前記特徴部分集合を抽出する。ランダム法とは、前記特徴集合中で任意数の特徴をランダムに抽出し、前記特徴部分集合を構成することを指す。具体的には、Ｓ個（あらゆる特徴数≧Ｓ、Ｓもランダム値）の特徴をランダムに抽出し、特徴集合を構成し、前記類似製品選別ステップ及び前記販売量一致性検査ステップを繰り返し、もし前記類似製品の販売量が一致性検査を満足すると、前記類似製品の販売量を使用して新製品の上場後の販売量を予測する。もし前記類似製品の販売量が一致性検査を満足していないと、前記類似製品の販売量が一致性検査を満足するまで、再びランダムに抽出することで新しい特徴部分集合を構成する。このほか、もし類似製品選別ステップ及び前記販売量一致性検査ステップの繰り返し回数が所定の閾値を超えると、繰り返しを停止させ、必要とするより多くの上場商品のデータを補充した後再度試してみる。このようにして、役に立たない計算量を減らし、新製品の販売量を予測する効率を高めることができる。 In the present invention, the feature subset is extracted using a random method, a decreasing method, an increasing method, or the like. The random method refers to randomly extracting an arbitrary number of features from the feature set to construct the feature subset. Specifically, randomly extract S features (every feature number≧S, S is also a random value), construct a feature set, repeat the similar product selection step and the sales volume consistency test step, and if the sales volume of the similar product satisfies the consistency test, use the sales volume of the similar product to predict the sales volume after listing of the new product. If the sales volume of the similar product does not satisfy the consistency test, construct a new feature subset by random sampling again until the sales volume of the similar product satisfies the consistency test. In addition, if the number of iterations of the similar product selection step and the sales volume consistency check step exceeds a predetermined threshold, the iteration will be stopped and will be tried again after supplementing the data of more listed commodities as needed. In this way, the amount of useless computation can be reduced and the efficiency of predicting new product sales can be increased.
減少法とは、まず前記特徴集合を前記特徴部分集合として前記類似製品選別ステップ及び前記販売量一致性検査ステップを行い、複数の前記類似製品の販売量が一致性検査にパスしなかった場合、前記特徴部分集合から１つの特徴をランダムで削除してもう一つの前記特徴部分集合とし、再び前記類似製品選別ステップ及び前記販売量一致性検査ステップを行うことを指す。前記特徴部分集合が空になる又は複数の前記類似製品の販売量が一致性検査にパスするまで、このように繰り返す。 The reduction method first performs the similar product selection step and the sales volume consistency check step with the feature set as the feature subset, and if the sales volumes of a plurality of similar products do not pass the consistency check, one feature is randomly deleted from the feature subset to make another feature subset, and the similar product selection step and the sales volume consistency check step are performed again. This is repeated until the feature subset is empty or sales of multiple similar products pass the consistency check.
増加法とは、まず前記特徴集合から１つの特徴をランダムに抽出し、前記特徴部分集合として前記類似製品選別ステップ及び前記販売量一致性検査ステップを行い、複数の前記類似製品の販売量が一致性検査にパスしなかった場合、前記特徴部分集合に前記特徴集合中の、先に抽出された特徴とは異なる１つの特徴をランダムで増加してもう一つの前記特徴部分集合とし、再び前記類似製品選別ステップ及び前記販売量一致性検査ステップを行うことを指す。特徴部分集合が前記特徴集合中の全部特徴を含む又は複数の前記類似製品の販売量が一致性検査にパスするまで、このように繰り返す。 The increase method refers to first randomly extracting one feature from the feature set, performing the similar product selection step and the sales volume consistency check step as the feature subset, and if the sales volume of a plurality of similar products does not pass the consistency check, randomly increase one feature in the feature set that is different from the previously extracted feature to the feature subset to make another feature subset, and perform the similar product selection step and the sales volume consistency check step again. It repeats in this manner until a feature subset contains all the features in the feature set or the sales volume of a plurality of the similar products passes the consistency check.
新製品の販売量予測ステップでは、前記一致性検査にパスした複数の前記類似製品の販売量を用いて、前記新製品の販売量を予測する。具体的には、本発明では、新製品の予測販売量は（数６）によって算出することができる。 In the new product sales volume prediction step, the sales volume of the new product is estimated using the sales volumes of the plurality of similar products that have passed the consistency check. Specifically, in the present invention, the predicted sales volume of a new product can be calculated by (Equation 6).
（数６）中、yiは新製品の第iの時間単位の予測販売量、Nは前記類似製品の数量、wnは第n個の前記類似製品の重み、且つ（数７）である。yinは第n個の前記類似製品の第iの時間単位における販売量である。 In (6), y i is the predicted sales volume of the i-th time unit of the new product, N is the quantity of the similar product, w n is the weight of the n-th similar product, and (7). y in is the sales volume of the n-th similar product in the i-th time unit.
このように、本発明により、市販製品の販売量データを利用して、客観的に、低コストで、且つ正確に新製品の販売量を予測することができる。 Thus, according to the present invention, it is possible to objectively, inexpensively, and accurately predict the sales volume of a new product by using the sales volume data of commercial products.
この他、本発明では、原因分析ステップをさらに有しても良い。即ち、前記一致性検査にパスした複数の前記類似製品及び前記特徴部分集合について、前記特徴部分集合中の特徴の毎に対し、当該特徴において前記新製品に類似する前記類似製品の数量を算出し、算出した類似製品の数量が最も多い所定個数の前記特徴を最も類似特徴として前記販売量類似の原因とし、その中、前記特徴が数値特徴である場合、前記新製品と前記類似製品との前記特徴の特徴値の差を算出し、前記特徴値の差の絶対値が予め設定した閾値よりも小さいとき、前記新製品と前記類似製品とが当該特徴で類似すると判定し、前記特徴が類別特徴である場合、前記新製品と前記市販製品の特徴が同一であるとき、前記新製品と前記類似製品とが当該特徴で類似すると判定する。 In addition, the present invention may further have a cause analysis step. That is, with respect to the plurality of similar products and the feature subsets that have passed the matching test, for each feature in the feature subset, the quantity of the similar products similar to the new product is calculated for each feature, and a predetermined number of the features with the largest number of calculated similar products is regarded as the most similar feature as the reason for the similarity in sales volume. If it is smaller than the threshold, it is determined that the new product and the similar product are similar in the feature, and if the feature is a classification feature, and the feature of the new product and the commercial product are the same, the new product and the similar product are determined to be similar in the feature.
これにより、新製品の販売量を予測した後、使用者に販売量に大きい影響を与える特徴を提供することができる。 Thus, after predicting the sales volume of a new product, it is possible to provide users with features that have a large impact on the sales volume.
また、本発明では、複数の類似製品の販売量が一致性検査にパスした場合、引き続き新しい特徴部分集合を抽出し、繰り返す回数が所定の回数に達するまで、類似製品選別ステップ及び販売量一致性検査ステップを繰り返すこともできる。この場合には、本発明により、複数の新製品の予測販売量と当該予測販売量に対向する最も類似特徴との組み合せを得、使用者の参考に供することができる。 In addition, in the present invention, when the sales volume of a plurality of similar products pass the consistency check, a new feature subset is continuously extracted, and the similar product selection step and the sales volume consistency check step can be repeated until the number of repetitions reaches a predetermined number. In this case, according to the present invention, a combination of forecasted sales of a plurality of new products and the most similar feature opposite to the forecasted sales can be obtained for the user's reference.
以下、理解し易いために、具体的な事例を使って本発明について説明する。
２０１９年新たに上場３つの車種（Ｗ,Ｙ,Ｚ）について販売量を予測し、その中で、ＷとＹは年度モデルチェンジ車種であり、Ｚは全く新しい車種である。目標としては、新車種が上場後３ヵ月の販売量を予測する。
Hereinafter, the present invention will be described using specific examples for ease of understanding.
We forecast the sales volume of three new models (W, Y, Z) to be listed in 2019. Among them, W and Y are model change models for the year, and Z is a completely new model. The goal is to predict the sales volume for the three months after the new model is listed.
１.データの収集と処理
下記の（表１）に示す３種類のデータの収集
１つ目は、予測待ちの新製品の特徴データ、即ち特徴及び特徴値、部材特徴集合である。本例では、特徴は、具体的に、ブランド、価格、排出ガス量（電動の場合０と記する）、輸入車であるか、車両タイプ、座席数、車体タイプ、変速段数、トランスミッションタイプ、駆動方式、ＡＢＳアンチロック・ブレーキ・システム、サンルーフタイプ、コンソールカラービッグスクリーン、シリンダ数、最大馬力、燃費、最高車速、パワーウインドウ、排出ガス基準の計２０個の特徴を含む。
1. Collection and Processing of Data Collection of three types of data shown in (Table 1) below. In this example, the features specifically include a total of 20 features: brand, price, exhaust gas amount (0 if electric), imported vehicle, vehicle type, number of seats, vehicle body type, number of gears, transmission type, drive system, ABS anti-lock brake system, sunroof type, console color big screen, number of cylinders, maximum horsepower, fuel efficiency, maximum vehicle speed, power windows, and emission standards.
２つ目は、参考のための新製品の特徴を含む市販製品の特徴データ、即ち特徴及び特徴値である。 The second is feature data of commercial products, ie, features and feature values, including features of new products for reference.
３つ目は、参考のための市販製品の販売量データである。本例では、目標は新車種が上場後３ヵ月の販売量を予測する。したがって、本例では、上場商品に対応して上場後３ヵ月の販売量を収集する必要がある。計３７種類（Ｏ１～Ｏ３７）の上場車種のデータを収集しており、５つの大手自動車ブランド（Ｂ１～Ｂ５）を含む。 The third is sales volume data of commercial products for reference. In this example, the goal is to predict the sales volume of a new model three months after it goes public. Therefore, in this example, it is necessary to collect the sales volume for the listed product for three months after listing. It collects data on a total of 37 listed car models (O1-O37), including five major car brands (B1-B5).
収集した特徴データについて、データ処理を行う。まず、欠損値を補完する。連続的な特徴につき、データ中の当該特徴の平均数を用いて欠損値を補完する。類別特徴につき、１つの「未知」という類別を新築して補完することができる。また、予測の正確性を保証するために、市販製品の販売量データは欠損を許さない。連続的な特徴につき、各サンプルの当該特徴における特徴値の単位の一致を保持することを確保する必要がある。類別特徴につき、同一の類別に異なる表現が存在する場合、表現を調整し、表現の一致を保持することを確保する。 Data processing is performed on the collected feature data. First, impute missing values. For continuous features, impute missing values using the mean number of that feature in the data. Each category feature can be supplemented by creating one "unknown" category. In order to ensure the accuracy of forecasts, sales volume data of commercial products are not allowed to be missing. For consecutive features, it is necessary to ensure that the unit of feature value for that feature in each sample remains consistent. For categorization features, if there are different representations for the same categorization, adjust the representations to ensure that the representations remain consistent.
２.類似製品の選別
本例では、類似性計量関数として重み付け余弦距離を用い、市販製品から類似製品を選別し、重み付けの重みを人で与える。
2. Selection of Similar Products In this example, weighted cosine distance is used as a similarity metric function to select similar products from commercially available products, and weights for weighting are given manually.
３.販売量の一致性検査
本例では、（数８）を用いて類似製品の販売量の差異度を算出する。
3. Consistency Test of Sales Volume In this example, (Equation 8) is used to calculate the degree of difference in sales volume of similar products.
本例では、予め所定した許容度は１.２であり、もし選別した類似製品の販売量の差異度εが許容度より小さいと、一致性検査にパスすることになる。 In this example, the predetermined tolerance is 1.2, and if the difference ε in the sales volume of the sorted similar products is less than the tolerance, it will pass the consistency test.
４.特徴部分集合の選び取り
本例では、類似製品を選別する場合、ランダム法で特徴部分集合を選び取った。具体的には、１５個の特徴をランダムで選び取って特徴部分集合を構成し、新製品とあらゆる市販製品とが当該特徴部分集合における距離を算出し、以下の結果を得た。Ｗに最も近い車種はＯ２、Ｏ９であった。Ｙに最も近い車種はＯ１１、Ｏ３２であった。Ｚに最も近い車種はＯ１６、Ｏ２９であった。
4. Selection of Feature Subsets In this example, when selecting similar products, a feature subset was selected by a random method. Specifically, 15 features were randomly selected to construct a feature subset, and the distance between the new product and all commercial products in the feature subset was calculated, and the following results were obtained. The car models closest to W were O2 and O9. The car models closest to Y were O11 and O32. The car models closest to Z were O16 and O29.
５.販売量の予測
それぞれ新製品に類似する２つの類似製品を得た後、類似製品の重み付け平均数を新製品の毎月の予測販売量として、重みは類似製品が新製品に類似すれば類似するほど、重みがますます大きくなって、正規化を行う。換言すれば、重みは類似製品と新製品との重み付け余弦距離の逆数であって、正規化を行って得た数値である。新製品の販売量を予測した結果は次の（表２）の通りであった。
5. Predicting sales volume After obtaining two similar products that are similar to the new product, the weighted average number of similar products is taken as the monthly forecast sales volume of the new product, and the more similar the similar product is to the new product, the greater the weight, and the weight is normalized. In other words, the weight is the reciprocal of the weighted cosine distance between the similar product and the new product, which is a normalized number. The result of predicting the sales volume of the new product is as follows (Table 2).
本例では、根平均二乗パーセンテージ誤差ＲＭＳＰＥ（Ｒｏｏｔ Ｍｅａｎ Ｓｑｕａｒｅ Ｐｅｒｃｅｎｔａｇｅ Ｅｒｒｏｒ）を用いて予測販売量について評価を行い、結果としてはＲＭＳＰＥ＝６.３であった。これで分かるように、本発明によれば、上場の前３ヵ月の販売量の変化傾向を捉えることができ、類似結果を取得することができ、即ち、本発明によれば、予測販売量を得ることができる。 In this example, the Root Mean Square Percentage Error (RMSPE) was used to evaluate the forecast sales volume, resulting in RMSPE=6.3. As can be seen, according to the present invention, it is possible to capture the changing trend of sales volume in the three months before listing and obtain similar results, that is, to obtain forecast sales volume according to the present invention.
６. 原因の分析
新製品が類似製品に最も類似した５つの特徴を算出し、原因分析を行い、新製品が上記５つの特徴において類似製品と最も類似するので、上場後の販売量も類似していることを表明している。例えば、Ｗの類似車種は「Ｏ２」と「Ｏ９」で、最も類似した５つの特徴は、価格、排量、輸入車であるか、車両タイプ、座席数であった。Ｙの類似車種は「Ｏ１１」と「Ｏ３２」で、最も類似した５つの特徴は、価格、輸入車であるか、車両タイプ、 座席数、駆動方式であった。
6. Analysis of the cause Calculate the 5 characteristics that the new product is most similar to similar products, conduct a cause analysis, and show that the new product is most similar to the similar product in the above 5 characteristics, so the sales volume after listing is also similar. For example, W's similar models are 'O2' and 'O9', and the 5 most similar characteristics were price, displacement, imported or not, vehicle type, and number of seats. Y's similar models were "O11" and "O32", and the five most similar characteristics were price, imported or imported, vehicle type, number of seats, and drive system.
以上、本発明の実施形態について説明したが、しかし、実施形態はただ例を挙げ説明しただけであって、本発明の範囲を限定する意図を有しない。これらの実施形態はその他の各種の態様で実施することができ、本発明の主旨を超えない範囲内に種々の省略、置換、変更、組合を行うことができる。これらの実施形態及びその変形が本発明の範囲と主旨に含まれると共に、特許請求の範囲に記載の発明及びそれと均等の範囲内にも含まれる。 Embodiments of the present invention have been described above, however, the embodiments are merely examples and are not intended to limit the scope of the present invention. These embodiments can be implemented in various other aspects, and various omissions, substitutions, modifications, and combinations can be made without departing from the scope of the present invention. These embodiments and modifications thereof are included in the scope and gist of the present invention, and are also included in the scope of the invention described in the claims and the scope equivalent thereto.
Claims (6)
使用者が販売量に対して影響を有すると考える外観、価格および性能である新製品の特徴を含む特徴集合を構築する特徴集合構築ステップと、
前記特徴集合中の全部又は一部の特徴を有する市販製品の販売量を取得する販売量データ取得ステップと、
前記特徴集合から１つの特徴部分集合を抽出し、類似性計量関数を使用することにより、前記市販製品から前記特徴部分集合中の前記特徴を有し、且つ前記特徴部分集合で前記新製品に類似する複数の類似製品を選別する類似製品選別ステップと、
複数の前記類似製品の販売量の差異度を算出し、もし前記差異度が予め所定した許容値よりも小さいと、複数の前記類似製品の販売量が一致性検査にパスしたと認定し、さもなくば、複数の前記類似製品の販売量が一致性検査にパスしていないと認定する販売量一致性検査ステップと、
前記一致性検査にパスした複数の前記類似製品の販売量を使用して、前記新製品の販売量を予測する新製品の販売量予測ステップと、を含み、
複数の前記類似製品の販売量が一致性検査にパスしていない場合、前記特徴集合から抽出された前記特徴部分集合とは異なる特徴部分集合を抽出し、類似製品選別ステップで選別された前記類似製品の販売量が一致性検査にパスするまで、前記類似製品選別ステップ及び前記販売量一致性検査ステップを繰り返すことを特徴とする新製品の販売量の予測方法。 A new product sales forecasting method comprising computer-implemented steps of:
a feature set building step of building a feature set containing features of the new product that are the appearance, price and performance that the user considers to have an impact on sales volume;
a sales volume data obtaining step of obtaining the sales volume of a commercial product having all or part of the features in the feature set;
a similar product selection step of extracting a feature subset from the feature set and using a similarity metric function to sort out a plurality of similar products from the commercial product that have the features in the feature subset and are similar to the new product in the feature subset;
a sales volume consistency checking step of calculating the degree of difference in the sales volumes of the plurality of similar products, determining that the sales volumes of the plurality of similar products have passed the consistency inspection if the degree of difference is smaller than a predetermined allowable value, and otherwise determining that the sales volumes of the plurality of similar products have not passed the consistency inspection;
a new product sales volume prediction step of predicting the sales volume of the new product using the sales volumes of the plurality of similar products that pass the consistency check;
A method for predicting the sales volume of a new product, characterized by extracting a feature subset different from the feature subset extracted from the feature set when the sales volumes of a plurality of similar products do not pass the consistency inspection, and repeating the similar product selection step and the sales volume consistency inspection step until the sales volume of the similar products selected in the similar product selection step passes the consistency inspection.
前記差異度は、（数４）によって算出され、
The degree of difference is calculated by (Equation 4),
前記類似製品選別ステップでは、ランダム法を用いて前記特徴部分集合を抽出し、即ち、前記特徴集合から任意数の特徴をランダムに抽出し前記特徴部分集合を構成することを特徴とする新製品の販売量の予測方法。 In the method for predicting the sales volume of a new product according to claim 1,
In the similar product selection step, the feature subset is extracted using a random method, that is, an arbitrary number of features are randomly extracted from the feature set to form the feature subset. A method for predicting the sales volume of a new product.
前記類似製品選別ステップでは、減少法を用いて前記特徴部分集合を抽出し、即ち、まず前記特徴集合を前記特徴部分集合として前記類似製品選別ステップ及び前記販売量一致性検査ステップを行い、
複数の前記類似製品の販売量が一致性検査にパスしなかった場合、前記特徴部分集合から１つの特徴をランダムで削除してもう一つの前記特徴部分集合とし、再び前記類似製品選別ステップ及び前記販売量一致性検査ステップを行い、
前記特徴部分集合が空になる又は複数の前記類似製品の販売量が一致性検査にパスするまでこのように繰り返すことを特徴とする新製品の販売量の予測方法。 In the method for predicting the sales volume of a new product according to claim 1,
In the similar product selection step, the feature subset is extracted using a reduction method, that is, the similar product selection step and the sales volume matching step are performed with the feature set as the feature subset,
if the sales volumes of a plurality of similar products do not pass the consistency check, randomly remove one feature from the feature subset to make another feature subset, and perform the similar product selection step and the sales volume consistency check step again;
A method for predicting new product sales, characterized by repeating this until the feature subset is empty or the sales of a plurality of the similar products pass a consistency check.
前記類似製品選別ステップでは、増加法を用いて前記特徴部分集合を抽出し、即ち、まず前記特徴集合から１つの特徴をランダムに抽出し前記特徴部分集合とし、前記類似製品選別ステップ及び前記販売量一致性検査ステップを行い、
複数の前記類似製品の販売量が一致性検査にパスしなかった場合、前記特徴部分集合に前記特徴集合中の、抽出された特徴とは異なる１つの特徴をランダムで増加してもう一つの前記特徴部分集合とし、再び前記類似製品選別ステップ及び前記販売量一致性検査ステップを行い、
特徴部分集合が前記特徴集合中の全部の特徴を含む又は複数の前記類似製品の販売量が一致性検査にパスするまでこのように繰り返すことを特徴とする新製品の販売量の予測方法。 In the method for predicting the sales volume of a new product according to claim 1,
In the similar product selection step, the feature subset is extracted using an increasing method, that is, one feature is first randomly extracted from the feature set to be the feature subset, and the similar product selection step and the sales volume consistency check step are performed;
If the sales volumes of a plurality of similar products do not pass the consistency check, one feature different from the extracted feature in the feature set is randomly added to the feature subset to make another feature subset, and the similar product selection step and the sales volume consistency check step are performed again;
A method for predicting new product sales, characterized by repeating this until a feature subset contains all the features in said feature set or the sales of a plurality of said similar products pass a consistency check.
原因分析ステップをさらに有し、即ち、前記一致性検査にパスした複数の前記類似製品及び前記特徴部分集合について、前記特徴部分集合中の特徴の毎に対し、当該特徴で前記新製品に類似する前記類似製品の数量を算出し、算出した類似製品の数量が最も多い所定個数の前記特徴を最も類似の特徴として前記販売量の類似の原因とし、
前記特徴が数値特徴である場合、前記新製品と前記類似製品との前記特徴の特徴値の差を算出し、前記特徴値の差の絶対値が予め設定した閾値よりも小さい時、前記新製品と前記類似製品とが当該特徴で類似すると判定し、
前記特徴が類別特徴である場合、前記新製品と前記市販製品との特徴が同一である時、前記新製品と前記類似製品とが当該特徴で類似すると判定することを特徴とする新製品の販売量の予測方法。 In the method for predicting the sales volume of a new product according to claim 1,
further comprising a cause analysis step, i.e., with respect to the plurality of similar products and the feature subsets that have passed the consistency check, for each feature in the feature subsets, calculate the quantity of the similar products that are similar to the new product by the feature, and take the feature of a predetermined number with the largest number of calculated similar products as the most similar feature as the similar cause of the sales volume;
When the feature is a numerical feature, calculating the difference in the feature value of the feature between the new product and the similar product, and determining that the new product and the similar product are similar in the feature when the absolute value of the difference in the feature value is smaller than a preset threshold;
A method for predicting the sales volume of a new product, wherein when the feature is a classification feature and the features of the new product and the commercial product are the same, it is determined that the new product and the similar product are similar in the feature.
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