TW581955B - Supply chain demand forecasting and planning - Google Patents
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- TW581955B TW581955B TW090126766A TW90126766A TW581955B TW 581955 B TW581955 B TW 581955B TW 090126766 A TW090126766 A TW 090126766A TW 90126766 A TW90126766 A TW 90126766A TW 581955 B TW581955 B TW 581955B
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Description
581955 A7
相關應用交g來考 本專利申請優先於2000年1〇月27提出申請之美國臨時專 利申請案號60/243,425,該專利申請以提及方式整個併入本 文中。 發明領域 本發明與供應鍊計劃和需求預測之系統及方法有關。尤 其,本發明與用以積極預報橫跨多層供應鏈之需求的系統 及方法有關,以避免供需不協調嚴重錯誤。 發明背景 如熟知技藝人士所知,供需不協調對於競爭市場賣方而 言是項嚴重錯誤,因為此類的供需不協調通常會導致錯失 機會、損失利潤、過度的發送成本、損失市場佔有率及客 戶服務不足。為了使銷售及行銷效率增加至最高限度,公 司必須精確預測未來客戶需求,並且使用這個資訊來推動 從製造到配銷作業的商業營運。對於涉及電子商務的商業 而言,由於買方很容易找到符合需求的替代賣方,所以精 確預測需求特別重要。 在製造及配銷產業中’反應現行客戶需求量,以最低過 量庫存供應產°口可降低庫存成本及配銷費用,進而可降低 產品的銷售單位價格。通常,這也可提高利潤率。因此, 賣方需要精確預測產品需求,使賣方可依據準確預測的客 戶需求趨勢,決定銷售計劃、生產計劃及配銷計劃。 藉由分析過去銷售結果趨勢來預測需求之傳統方法的目 標為,預測者應用最精確的統計分析技術及計量經濟模 -4 - 本紙張尺度適财81國家棵準(CNS) A4規格(210X297公董)
裝 訂
581955 A7
型,以提供最精確預測可能性。在這些傳統方法中,執行 的時間序列預測係開發及使用各種預測演算法,這些預測 演算法嘗試以規則形式來描述商業知識及如過去歷史證明 的銷售結果波動趨勢。此類預測演算法的開發(以及用以利 用此類預測演算法的電腦化系統)通常是一項勞動密集的工 作。 然而,產品需求趨勢通常會在短暫生命週期内發生變 化。在此情況下,提供預測過程中使用的資料迅速過時, 並且預測精確度降低。因此,為了保持高精確度預測,必 須持續維護演算法(以及用來產生這些演算法的歷史資料 點),並且必·須很容易調整演算法的預測。 除了電腦方面的困難以外,在以星期、季節或其他循環 事件為基礎的某些產業,會因不同的週期需求模式而使商 業需求更複雜。例如,在某些產業中(.如餐飲業或零售業、 行人交通),產品優惠及銷售量為依星期而變化。同時地, 在許多產業界,產品需求可呈現某些程度可預測的季節、 年度及/或週期性波動。此外,非週期性事件促銷方案、當 地事件、假曰等等,全部都會改變商業面對的需求量。精 確的需求預測必須能夠考慮到這些對需求量的週期性及非 週期性影響。然而,制定能夠如此精確的單一演算法的複 雜度已被歸類為依照經驗及過去商業需求記錄來估計預期 需求類型的商業。如一項可能的預期,當商業愈大且更複 雜時,做出此類預測也會更不確定且風險更高。另外,如 果不可行,則人類計算及預測更頻繁(如每小時或每日)的 -5 - 本紙張尺度適用中國國家標準(CNS) A4規格(210X 297公" ' ------ 裝
mk 商業需求波動也就不切實際,雖然很希望能夠如此進行。 因此,由於生產計劃過程中遇到的更重要問題之一是因 產品未來需求不確定性所造成,所以有大量的文獻及業界 努力嘗試解決這個問題。但^,目前依據預測需求的生 產、物料及運輸計劃仍然是一項重要的挑戰。雖然已有許 夕有關需求計劃理論領域的研究,但是到現在為止,達到 的進展係依據過簡化設想為基礎,或是在計算上是現實世 界應用不可實行的方案。因A,迄今為止,競爭市場的賣 方尚未發展出彈性郤實質上自動化的方法可滿足其需求預 測的需要。 ' 舉例而言,Milne等人提出之美國專利申請案號 6,〇4 9,742發表一種電腦軟體工具,用以比較專案供應計劃 決策與預期的需求數據圖表。M i 1 n e發表的電腦系統提供一 些常式(routine),可用來專案供應與實際遇到的需求,以 協助軟體工具使用者設定電腦系統,以更符合其商業需 求。然而,如同大部份已知的需求預測方法,Milne不具彈 f生的原因為’匕無法比較各種市場内各種產品的多種替代 模型,因此無法協調使用者開發及識別改良的模型。 同樣地,Cheng等人所提出之美國專利案號6,138,103發 表一種用來預測不明確需求的決策方法。Cheng系統使用 一種矩陣來代表可能的需求案例及其發生的相對可能性, 然後使用這些矩陣以依據不明確需求最可能的結果來計算 生產計劃排程。和M i 1 n e —樣,C h e n g無法提供發展用以預 測未來需求之替代演算法及微調此類需求預測演算法的方 本紙張尺度適用中國國家標準(CNS) A4規格(210 X 297公釐) 581955 A7 〜--------Β7 五、^説明(4 ) ~〜'~ 法。 另外,Fields等人提出之美國專利案號5,459,65 6發表一 種系統,可在複數個時間間隔期間内測量及儲存商業需求 資料,並且使用以百分比為主的需求曲線來企劃近期時間 間隔期間内的產品商業需求。fr i e 1 d s系統允許建立適用於 項目的複數個需求曲線,以利用定義的函式及變數來決定 近期需求。然後,可修訂現行及近期時間間隔的商業需求 企劃,以反應所接收到的實際商業需求資料的變動。但 是,Flelds無法考慮到影響未來需求的許多因素,包括個 別市場變化性及產品促銷的影響,因而無法提出積極性預 測。 最後,Andok出之美國專利案號6,032,125發表一種需 求預測系統,其允許使用者依據衍生自各種時間週期(包括 最近幾個月、前幾年類似的期間及其組合)資料的演算法來 預測需求。但是’ A n d 〇專利無法考慮到關於來自於單一組 織或供應鏈(如銷售資料、退貨、批發資料等等)内各種來 源之過去需求的資料。另外,Ando無法提供用以導出各種 需求預測模型及在一段時間後修訂這些模型的系統及方 | 法。 因此,技藝界仍然需要可積極開發替代模型的改良系統 及方法,以橫跨多層組織供應鏈來預測需求,進而避免供 需不協調的嚴重錯誤。 I #明概要 LZnrrr: 本紙張尺度適用中國國家標準(CNS) A4規格(210X 297公爱) 581955 A7 B7 五、發明説明 以精確預測許多產品未來需求及許多市場之產品類型的系 統及隨附的方法。 再者’本發明目的是提供一種使組織能夠產生及比较預 /則舄求之替代模型的系統及相關方法,以便不斷改良需求 預測能力。 同時,本發明目的是提供一種橫跨分散式電腦網路運作 的系統及方法,藉此使用者可位於各種遠端位置,並且系 統可發行所選取的預測,以供整個組織使用,包括下游供 應、製造及運輸計劃系統。 同樣地,本發明目的是提供一種可考慮到影響未來需求 之獨立因果因數(如新競爭產品及價格促銷),以產生及識 別最佳預測的系統及隨附的方法。 另外,本發明較佳具體實施例的目的是提供一種系統及 相關方法,藉此使用者可很容易說明目前的需求趨勢,而 不需要產生完整的新預測。 此外,本發明較佳具體實施例的目的是提供一種系統及 相關方法,以允許使用者更容易產生及比較相關產品、相 關市場產品及考慮到不同預測模型的預測。 為了因應前面說明及其他需要,本發明提供一種用以識 別及考慮推動需求之許多關鍵因素的解決方案,其方式是 提供一種允許多重替代預測演算法的多重模型架構,包括 熟知的統計演算法(如傅立葉(F〇urier)及多重線路迴歸分 析("MLR”)演算法)以及專屬演算法,用以將與各種產1 及/或產業相關的因果因數模型化,以與各種需求資料厚史
581955 A7 B7 五、發明説明(6 ) 責料流關聯’進而產生先進的預測模型。本發明協助使用 者利用可用的需求資料歷史資料流來決定最適合給定問題/ 產業/商業的預測演算法,其方式是能夠產生各種預測以互 相比較’最後,與實際需求相關的輸入資料比較。 藉由針對單一產品或地點採用多重模型,本發明具備多 項案例比較分析功能,其方式是允許使用者使用各種替代 預測演算法理論,從多重歷史資料流(例如,運輸資料、銷 售點資料、客戶訂單資料、退貨資料等等)建立預測。熟知 技藝人士很容易明白,在特定情況可使用各種熟知及專屬 的統計演算法替代方案,以依據歷史資料來預測未來需 求。本發明的多重模·型架構讓使用者能夠比較與各種歷史 資料流配對的統計演算法(以下合稱「模型」),以便執行 各種模擬,以及評估哪一種模型可提供適合給定市場中特 定產品的最佳預測。一旦使用者已決定要使用哪一種模 型,就可發行讓模型提供的預測資訊,以供組織使用(例 如,供下游供應計劃程式使用)。 本發明的較佳具體實施例使用自動化調整功能,以協助 使用者決定適合給定預測演算法的最佳化參數設定值,以 產生最合適的預測模型。這項功能顯著減少使用者利用需 求資料歷史資料流產生可接受之需求預測所需的時間及資 源。 本發明的具體實施例提供一種系統及方法,藉此每當發 生給定事件時,可動態預測適當的供需回應,如當競争者 的特定產品降價(如促銷),或當使用者公司展開銷售或行 本紙張尺度適用中國國家標準(CNS) A4規格(210X 297公釐) 581955
銷活動哙。利用依據本發明的多重模型架構,允許選擇、 自訂及微調適當的預測演算法以應用在給定的需求問題, -方式是產生複數個預測,每項預測均考慮到不同的需求 因果因數及需求歷史資料,並且識別及最佳化要採用的預 測。依據此類的具體實施例,使用者可精確預測客戶的購 買計劃’#此從變化的需纟資料M<資料流來識別及量化 推動需求的關鍵變數,以此方式預測產品或產品線的需 求。 裝
本發明具體實施例已知通常要從數個不同領域獲取預測 貧訊。為此目的,本發明的多重模型架構支援合作和比較 預測。此類的合作和比較預測可能發生在單一組織内部及 横跨由許多商業夥伴所組成的外部長期供應鏈社群。在此 方法中,可共用需求歷史資料來建立更精確的需求預測, 並且可橫跨整個組織或長期供應鏈社群發行採用的預測。 熱知技藝人士很容易明白,本發明的需求預測能力可分散 於整個企業,或者可針對企業特定地理區域以以地域為基 礎地使用。 本發明還提供智慧型事件模型化功能,並且考慮到關於 實際需求變更之已知最新接收資料的管理越位(〇verdde)及 預測調整。就其本身而論,會將事件資料持續併入預測模 型中’以協助在執行時調整預測決策,以及精選供未來企 圖使用的預測處理。藉由納入市場計劃及需求計劃功能, 本發明連結產品混合、促銷和價格分析與傳統需求預測。 本發明的較佳具體實施例是電腦網路及相關方法,用以 -10 - 本紙張又度適用中國國家標準(CNS) A4規格(210 X 297公釐) 581955 A7 B7 五、發明説明(8 ) 促進預測供應鏈内商品需求預測及相關的計劃功能。在本 發明具體實施例的任一具體實施例中,前端使用者介面最 好准許使用者與一個或一個以上資料庫互動,以定義複數 個決策演算法,進而自訂可充分利用使用者在組織方面專 業知識的演算法。此外,前端使用者介面還准許使用者檢 視及修改需求預測,並且採用特定核准的預測,以遍及使 用者組織發行。 本發明的具體實施例還利用互動式圖形介面,以協助供 應商瞭解事件對需求的影響。藉此,利用允許使用者監視 整個組織需求的合作和預測功能,提供整個交易網路的需 求明顯性。藉由協助確保整個組織(或甚至合作組織群組)正 在使用相同的預測數量制訂計劃,本發明有助於緩和整個 交易網路的需求變化,以促進更具效率的合作。 本發明當作一種初期警告系統,預測未來客戶需求、檠 示潛在供應問題並且找出傳統方案無法偵測的模式。本發 明促使供應鏈内的公司能夠瞭解客戶的需求推動力、更精 確預測客戶的未來需要,並且統一整個多重模型架構内不 同的汁劃程序以及與供應鏈其他成員的合作。 接下來的說明書中將會提出本發明的額外功能及優點, 亚且,藉由說明書或實施來學習本發明後應會有某種程度 的瞭解。藉由撰寫的說明書和相關申請專利範圍及附圖中 所具體指出的結構,即可實現並獲得本發明的目的及其他 優點。 應明白,前面的一般說明及下文中的詳細說明均是示範 -11 - 本紙張又度適用中國國家標準WNTS、Δ V OfV7 政、 581955 A7 B7 五、發明説明(, 及解說’並且打算提供如申請專利範圍提出之本發明的進 一步解說。 圖式簡單說明 作為本發明具體實施例圖解的附圖(為提供本發明進一步 暸解而納入,並且被併入及建構說明書的一部份)及說明書 係用來解說本發明原理。附圖中相同的參考數字代表整份 附圖中對應的元件: 圖1顯示如何藉由根據本發明的需求預測單元來組織需求 預測的原理圖; 圖2顯示如何根據本發明具體實施例來獲得替代預測演算 法及各種需求資料歷史.資料流以建立多重模型架構的原理 [£| · 圖, 圖3顯示如何根據本發明具體實施例,本發明的需求預測 系統利用邏輯從歷史資料流及使用者輸入的因果因數來建 立給定需求預測單元的預測之組合式原理及流程圖; 圖4A、4B、4C及4D顯示兩維標繪圖,用以描繪累進考 慮到量、趨勢、季節效應及因果因數之預測間的差異; 圖5顯不根據本發明較佳具體實施例之用以產生預測演算 法之各種係數之方法的流程圖; 圖6顯示根據本發明具體實施例用以產生需求預測(包括 準備及執行需求計劃循環)所執行之整個程序步驟的流程 圖;以及 圖7顯不根據本發明較佳具體實施例之用以產生複數個需 求預測單位之預測之程序的流程圖; • 12 - 本紙張尺歧财® ®冢標準(CNS) A4規格(210X297公爱)
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581955 A7
之根據本發明較佳具體實施例之電子需求預測系統 之作業樣悲及互動的原理圖。 較佳具體實施例謀鈿訪日日 現在將藉由本發明的較佳具體實施例並參考示範性 來說明本發明。 ® 一般而言,就以賣方或供應商身份營運的給定賣方組織 而言,必須針對每種市場中的每個項目循環執行需求預 測。由於對一項項目的需求會因市場及銷售時間範圍而 異,所以根據本發明的需求預測係依據其需求預測單位 (DFU)識別。如圖j所示,根據本發明的〇?1; 1〇〇是一種用 以依據四種主要資訊類型來分類每項需求預測的機制。每 個DFU 100依據複數個需求單位1〇1的一個需求單位、複數 個需求群組102的一組需求群組、複數個地點1〇3的一個地 點及複數個模型1 0 4的一個模型來分類預測。需求單位是一 項唯一產品識別碼,在某種程度上,其作用類似於項目號 碼或目錄(catalog)號碼(即,它識別DFU的預測將應用的 特定產品)。需求群組是需求單位所屬的類別,並且係供預 測用途使用。例如,需求群組可能是關鍵帳戶、商標、客 戶群組或人口統計、廠商類型或市場區域。因此,會使用 其他的相關需求單位進一步分類包含一需求單位及一該需 求單元所屬之需求群組的每個DFU。此外,還會指定每個 DFU的地點’其代表特定預測將應用的地理區域。DFU地 點通常係以市場層級指定,並且特定預測不會考慮配銷中 心。最後,指定給每個D F U的模型定義歷史資料流與預測 -13 - 本紙張尺度適用中國國家標準(CNS) A4規格(210 X 297公釐) 581955
演算法的組合,以在建立該特定DFU的預測時使用。歷史 資料流與預測演算法的組合(在閱讀下文中參考圖2的說明 後將會進一步瞭解)定義統計模型,可供特定使用者選擇以 建立需求預測。 根據本發明使用DFU來分類及組織預測,可允許使用者 使特定產品(需求單位)、給定產品群組内之特定產品(需求 群組)、可能要銷售給特定客戶類型之產品(需求群組)、給 疋市場内之產品或可能之產品(地點)或特定市場内之特定 產品的預測互相關係及關聯,其中採用不同的預測演算法 及歷史資料流組合(模型)。如圖1所示,例如,使用者定義 之DFU領域100,中的給sDFU可能具有「〇8〇]6」(產品 號碼)需求單位D U〗、「零售商」(通路)需求群組D G i、 「美國西南方」(市場區域)地點L !及「銷售點/專屬」(需求 歷史/演鼻法組合)模型Μ !。第二D F U也可能是由d U i、 〇01與1^1所組成,但是郤具有r銷售點/傅立葉」模型μ〕, 因此會產生不同的統計預測。本文中將此類只有模型(按歷 史、演算法或兩者)不同的DFU稱為「模型變數」。 現在請參考圖2,圖中所示的原理圖示範如何根據本發 明,針對給定的需求單位,利用使用者定義的DFU參數2〇〇 來組合替代預測演算法201與各種需求資料歷史資料流 202,以建立複數個模型1〇4。熟知技藝人士很容易明白, 根據本發明具體實施例,如圖2所示之進行可用需求資料歷 史 > 料與預測硬异法之各種組合的功能,配合辨識及結 合模型變數的功能,可促進對考慮到不同需求推動力、產 ______ - 14 · 本紙張尺度適用中國國家標準(CNS) A4規格(210X297公董) 12 五、發明説明( 品生命週期企劃箄黧 、 、多代需求預測進行比較。以此方 式,促使用以識別形忐♦七 成而未預測之較佳模型的功能更加容 易。 類似於模型變數,太 本文中將只有地點不同的DFU稱為 、地點變數」。熟知技藝人士很容易明白,不同地點的需 東通:可月b十刀^似(如針對不同且遠離,郤類似的城市設 產)口而可藉由比較預測與其地點變數的現有預測, 以評估一項DFU預測的精確度。 口此由於本發明採用多重模型架構("MMF ”),所以 ^FU換型變數可具有多重歷史類型與多重預測演算法組 °以構成、’Ό疋產品的數項替代模型。在此方法中,可藉 此建立互相屬於模型變數(DFU的預測以利比較,並且最 後採用最佳模型,如下文所述。 此外,還可考慮到針對同一需求群組(即,例如,同一產 品類別内類似的產品)中第二需求單位建立的統計預測,製 作第一需求單位的評斷預測(如下文中的定義及說明)。本 發明的觀點特別適用於下列情況,其中第一項產品沒有大 置的%求歷史資料,並且足以類似於第二項產品,讓使用 者可預計第二項產品的預測行為類似於第一項產品的預測 行為。 本發明具體實施例已知通常要從數個不同領域獲取預測 資訊。為此目的,本發明使用其多重模型架構(” Μ M F,,)以 支援合作和比較預測。如本文所述,此類的合作和比較預 測可能發生在單一組織内部及橫跨由許多商業夥伴所組成 -15 - 本紙張尺度適用中國國家標準(CNS) Α4規格(210 X 297公釐) 裝 訂 581955
的外部長期供應鏈社群。藉由採用MMF功能,本發明具備 多項案例比較分析功能,其方式是允許使用者使用各種替 代預測演算法理論,從多重歷史資料流(例如,運輸資料、 銷售點資料、客戶訂單資料、退貨資料等等)建立預測。 熟知技藝人士很容易明白,在特定情況可使用各種熟知 及專屬的統計演算法替代方案,以依據歷史資料來預測未 來需求。本發明的MMF讓使用者能夠比較與各種歷史資料 流配對的統計演算法(以下合稱r模型」),以便執行各種 模擬,以及評估哪一種模型可提供適合給定市場中特定產 品的最佳預測。一旦使用者已決定要使用哪一種模型,就 可發行讓模型提供的預測資訊,以供組織使用,例如,供 下游供應計劃方案或運輸經理使用。 在本發明的一項較佳具體實施例中,藉由採,組 織可從已完成銷售及行銷評估的各種外部群組或其他部份 匯入預測值,以比較屬於本發明系統之統計模型產生的預 測。在此方法中,本發明系統或方法產生的統計預測可與 外部評斷預測相比較,以協助使用者精選模型,並且依此 進行預測發行決策。 為了產生特定預測,本發明提供的組織能夠依據替代模 型計算各種預測,然後選用認定最精確之特定預測模型的 結果,並且想要送出(或「發行」),以進行後續的供應計 劃。MMF中使用的演算法係由依據歷史資料流執行的一連 串數學计异所組成,以建立統計預測。本發明的較佳具體 實施例讓使用者能夠藉由控制使用的歷史資料,以達到嚴 -16 · 本紙張尺度適用中國國家標準(CNS) A4規格(210X 297公釐)
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A7 B7 五、發明説明(14 ) ^計制與評斷㈣之間的平衡。統計預測假;t產品的 求L疋就其本身而論,統計預測通常更有助於反映生 產的^熟產品的特定’並轉持與許多客戶的長期生命週 j。攻些預測依賴基本歷史資料(即,不包含如異常或奇特 貝料點的歷史資料)以進行可預測及可重複的需求。 另一方面,評斷預測極度集中於不穩定需求的人類評 估。此類預測最適合高度促銷的新產品,這些產品的客戶 群較小、完全是新產品及/或生命週期短。評斷預測係運用 在特定事件及非重複的事件,並且通常依賴非基本的歷史 資料流。 現在請參考圖3,.圖中顯示如何根據本發明的需求預測系 統及方法,針對給定的〇171; 3〇4,從各種歷史資料流3〇3 中知:供的適當需求資料建立預測308,並且輸入未來的因果 因數資訊306。首先,將各種需求資料歷史資料流儲存至資 料庫中,如圖3所示的歷史資料庫3 〇丨a。顯而易見,單一資 料庫可交替執行如圖3所示之資料庫3 0 1 a至3 0 1 c的功能, 但是’圖3中描繪獨立的資料庫,以更明確描繪資料流程。 使用從DFU資料庫擷取的DFU資料304 (需求單位、需求群 組、地點及模型),資料伺服器302a從歷史資料庫30 la擷 取包含DFU相關之需求資料的歷史資料流3〇3。這個DFU 及D F U相關的適當需求資料歷史資料流被傳送3 0 5至預測產 生伺服器3 02b (和資料庫3 0 1 a至3 0 1 c —樣,可將伺服器 3 02a至3 02b組合成單一伺服器)。預測產生伺服器3〇2b使 用預測和D F U資料3 0 5及使用者輸入3 0 6的因果因數(如透 -17 - 本紙張尺度適用中國國家標準(CNS) A4規格(210X297公釐) 五、發明説明(15 過和個人電腦3 08 —樣的輸入裝置)來產生DFU的需求預測 3 〇7。這個需求預測被傳送至資料庫伺服器3 02a,由資料 庫词服器將該需求預測儲存至預測資料庫3 0 1 c中。標繪圖 3〇7’提供如何為使用者顯示需求預測3 07的實例。 當依據本發明選取適當的統計演算法以組合特定DFU歷 史資料流(即,將它們組合在一起以建立模型),最好依據 基礎產品特性來選用適當的演算法。例如,下列的表格i解 說特定產品生命週期或特性會對其需求模型造成極大的影 響。 產品類型 生命週期階段 特有銷售模式 萬聖節前夕糖果 成熟產品 可預測的季節尖峰 新款汽車零組件 最新問市的產品 偶發需求 電腦處理器晶片 成長或衰退 迅速成長後明顯衰退 雪鏟 成熟產品 無規律的季節尖峰 表格1 根據本發明較佳具體實施例,需求計劃系統支援熟知之 傳統演算法與專屬演算法的組合。 圖4A、4B、4C及4D顯示兩維標繪圖4〇〇a至400d,用以 描緣累進考慮到量、趨勢、季節效應及因果因數之預測 40 1 a至40 1 d間的差異。如圖4A所示,圖中所示的需求預測 4 0 1 a表示只預報未來需要量的預測。本質上,預測4 〇丨a僅 僅是過去需求歷史資料的平均值,並且不考慮需求趨勢、 季節效應及因果因數。在一般情況下,只有在過去需求 「起伏不定」或無法推論模式的隨機變化情況下,此類預 _____ - 18 - 表紙張尺度適用中國國家標準(CNS) A4規格(21〇 X 297公釐) ' --- 581955 A7 B7 五、發明説明( 16 測才會有作用。預測4 〇丨a顯然代表可從需求歷史資料產生 之需求預測的最簡化類型。更複雜的統計預測還會考慮需 求趨勢,如圖4B所示的預測4〇lb。雖然預測40 lb是考慮需 求趨勢的線性預測(在圖中所示的案例中,需求的線性趨勢 係以給定比率遞增),熟知技藝人士很容易明白,利用趨勢 觀念的預測可能屬於其他形式,包括二次方或對數形式。 此外’甚至進一步高級需求預測演算法會考慮季節效應。 如圖4C所示之預測4〇 1 c的季節預測可預報需求高波峰與波 夺’以協助製作過去高低需求循環週期到未來的模型。導 致此類需求高低週期的季節效應通常因市場而異,以及因 產品而異。最後,除了僅以量、趨勢及如圖4C所示之預測 4 0 1 c的季節效應以外,圖4 D的標繪圖示範影響預測之因果 因數的效應(如售價折扣)。例如,傳統經濟理論指出可預 計給定產品的需求會隨著降價而遞增(所有其他狀況不 變)。除了價格變化以外的其他因果因數同樣對預期的需求 造成類似的影響。因此,用許多更精確的預測演算法,嘗 試將因果因數效應量化成預測演算法,以更精確預計產品 的未來需求。本發明能夠提供使用本質上優秀之各種預測 演鼻法建立的替代預測模型,讓使用者能夠使用本身的商 業評斷及經驗來考慮從過去需求資料至各種範圍的量、趨 勢、季節效應及因果因數,以進行更精確的預測。 與特定D FU相關之建立及維護統計模型的工作必須考慮 兩項基本要素,才能提供實際可行的統計預測;它必須考 慮歷史資料以產生統計演算法,並且必須預測未來的歷史 -19 - 本紙張尺度適用中國國家樣準(CNS) A4規格(210X297公爱)
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A7 B7
581955 五、發明説明(17 需求模式。許多統計演算法均是以傅立葉級數為基礎(由於 傅立葉級數的循環性質),並且包括用來預測給定產品未來 需求的多組自定係數(從過去歷史資料決定)。此外,統計 演算法也會使用多重線路迴歸分析(,,MLR,,)以計算具有多 重外部因素(因果因數)之產品的預測,如價格、天氣或影 響產品銷售模式(即,需求)的人口統計資料。 舉例而言,已知在統計需求預測技藝中,利用傅立葉級 數製作具有固定需求或需求係以可預測常數及/或週期率變 更之穩疋產品需求循環的模型。傅立葉級數的運算方式為 使用隨時間序列遞增的頻率及相位角來調整正弦波及餘弦 波。在用於需求計劃時,傅立葉級數中的第一係數代表整 個需求量(或平均值)(圖4 A描繪只考慮平均值的預測 40 1a)。第二係數代表製作單元數量模型的趨勢(或斜率), 其中單兀數量是每個時間週期内的量變化(遞增或遞減圖 4 B描繪考慮平均值及趨勢的預測4 〇丨b )。其他的傅立葉係 數(成對出現)代表需求歷史資料中的季節模式(波峰與波 谷)(圖4C描緣考慮平均值、趨勢及季節效應的預測 40 1c)。可得知,可藉由正弦波及餘弦波非常精確地製作需 求歷史資料中可預測之季節模式的模型。因此,就示範影 響需求之大幅季節效應的生命週期而言,當使用2至3年的 需求歷史資料來產生產品類型的模型係數時,傅立葉型需 求計劃演算法可產生最佳結果。因此,以傅立葉級數為基 礎的需求預測演算法本質上允許公司產生考慮季節效應的 預測。 -20 - 本紙張尺度適用中國國家標準(CNS)A4規格(210X297公釐)
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581955 A7 ______ B7 五、發明説明(18 ) 此外’產業界還可使用以衍生自需求因果因數MLR的統 汁預測凟异法。M L R藉由將一項或一項以上因果因數(即, 推動如價格、天氣、人口統計資料及其他已知之經濟指標 的外部需求)整合到趨勢或循環預測的方式來擴展傅立葉 法,以協助預計此類因果因數對特定產品銷售模式的影 響。當有多重已知的外部因素會顯著影響銷售或需求時, 最適合應用以MLR為主的演算法。時間、產品價格及廣告 成本通常是特別重大的因果因數。因此,M L R利用和傅立 葉法一樣的量、趨勢及季節效應,而且還將獨立的可變因 果因數及事件資料整合至預測中。藉由比較圖4 C所示之預 測4 0 1 c與圖4 D所示之預測4〇 1 d可得知,因果因數對傅立葉 基礎需求(平均值、趨勢及季節效應)造成附加的(正面或負 面)影響。 ' 圖5顯不根據本發明較佳具體實施例之用以產生預測演算 法之各種仏數之方法5 〇 〇的流程圖。如圖所示,首先於步驟 5 01識別適當的歷史資料(如藉由考慮中之特定〇 F ^的模型 定義)。此時,於步驟6〇2 ,系統識別嘗試解析的最大項 (term )數量(模型係數)。這個最大項(term )數量可能是系 統設定的預設值(例如,定出使用之總計算容量的上限), 或疋使用者依據季節尖峰量或資料中呈現的因果因數猜 測。 一旦已選取最大項(term)數量及歷史資料後,於步驟 5 〇 j針對歷史資料執行技藝中已知的最小平方迴歸法( squares regressi〇n)。於步驟5〇3,方法wo嘗試找出產生最 -21 _ 本紙張尺度適用中國國家標準(CNS) A4規格(2i〇 X 297公---- 581955 A7 _____ _B7 五、發明説明(19 ) 小平方錯誤之考慮中預測演算法的係數。本質上,方法5 〇〇 的步驟5 03將使用允許的最大項(term )數量,嘗試使最佳 模型預測符合資料。 此時,產生起始預測模型。於步驟504,方法5〇〇執行重 要振幅測試,以決定最後的模型中應包含多少季節效應項 (term)(小於或等於在步驟502中設定之最大項(term)數量 的數量)。從最小振幅的波峰或波谷(即,正弦波)開始,於 步驟504逐漸排除最小波峰或波谷係數,直到找到仍然符合 振幅重要性測試的最小振幅波峰或波谷(如超過基本(即, 量及趨勢)需求的最小·百分比效應)。一旦找到具有有效振 幅的第一波峰或波谷,系統隨即停止檢查,並且斷定其餘 的波峰及波谷係數代表模型的正確季節效應數量。 於步驟5 0 5,針對於步驟5 0 1選取的歷史資料執行第二次 最小平方迴歸法。但是,在這個迴歸法中,方法5〇〇假設適 當的季節效應項(te]rm )數量相當於於步驟504找到之具有 有效振幅的波峰及波谷數量。藉此,步驟5 〇 5的最小平方迴 歸法產生最後的預測模型。 熟知技藝人士很容易明白,使用者可採用M L R演算法來 計算需求預測,其方式是只包含會顯著影響需求的變數, 或者採用許多因果因數。在任一情況下,以MLR為主的演 算法自動計算影響銷售的每個單獨的因果因數(例如,藉由 計算每個係數的T統計量及相關的p值)。在計算這些效應的 過程中,M L R模型使用未來的因果因數資料,例如,建議 的價格變動。 ___ - 22 - 本紙張尺度適用巾國@家料(CNS) Α4規格(21GX 297公董) " 一
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k 581955 A7 B7 五、發明説明(2〇 ) 另外,假設商業不是固定不變的,則可使用專屬需求計 劃和預測演算法。此類的專屬演算法有助於使用偶發的需 求短期生命週期來製作產品模型,例如,電腦硬體。本發 明被調整以利用一個或一個以上專屬模型,此類的專屬模 型已經過或將要開發以預期此類的偶發產品生命週期。 熟知技藝人士很容易明白,一旦已識別特定D F u需求的 因果因數’使用者必須輸入這些因果因數的歷史及預期未 來值(例如’針對特定需求單位計劃的價格折扣)並儲存至 糸統中,如需求預測系統在使用運用因果因數的模型來建 立預測時使用的資料庫。 .一旦已產生MLR預測並且預測已與實際需求相比較,本 發明就可讓使用者微調MLR預測演算法的係數。針對異常 (過高或過低,並且未來不會重複)的現行歷史資料點,需 求片劃人貝可遮罩不要納入模型的特定歷史資料點。 在本發明的較佳具體實施例中,當建立將要使用河乙尺演 异法來建立DFU的歷史資料流時,可提示使用者建立因果 因數類型。因果因數類型通常代表因果因數的類別或群 ’’且以便水集 > 料點。例如,假曰或價格價動可當作因果 因數類型。一旦已建立因果因數類型,可提示使用者定義 可出現在歷史資料流内的個別因果因數。例如,當作因果 因數的所有假日均可共用同一種假日因果因數類型。但 是,兩個分開的假曰(例如,耶誕節及復活節)將具有單獨 的假日資料流,這是與每個因果因數發生時所發生之歷史 需求變化相關的個別歷史資料流。 ____ - 23 - 本紙張尺度適用中國國家標準(CNS) A4規格(灿X 297公爱) -----—-—
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線 581955 A7 B7 21 五、發明説明( 針對被納入預測演算法計算中的任何因果因數,將會儲 存係數值及統計值(P值及T統計量),並且組合因果因數歷 史資料。已知在統計〆分析技藝中,τ統計量係用來測量個別 變數對多變數函式結果的重要性。一般而言,如果T統計量 專於或大於二’或等於或小於負二,則變數(因果因數)會 對函式數(預測的需求)有重大的影響。 現在請參考圖6,圖中顯示根據本發明較佳具體實施例的 需求計劃方法6 0 0 ’其包括五個基本程序。通常,每個想要 或必須一組新需求預測時,使用者會循環執行這五個程 序。如圖6所示,於步.驟601,使用者藉由識別及安排從各 種内部和外部來源饋送的需·求歷史資料來設定需求預測環 境,藉由將需求單位和需求群組與位置和模型組合在一起 來定義DFU,以及初始化各種資料庫(如歷史資料、DFU及 預測貧料庫)以儲存輸入至需求計劃系統及需求計劃系統產 生的相關資訊。在這個第一步驟中,使用者還選取想要建 立預測的特定DFU。可明白,於步驟6〇1選取tDFU數量的 範圍從單一DFU、到DFU群組(如具有完全一樣需求群組或 是模型變數或地點變數),直到並包括所有現行定義的 DFU。 每個將產品加入至使用者的組織商品或從使用者的組織 商品去除產品時,就必須新增、編輯或移除D F U資料庫中 對應的DFU資訊。因此,於步驟6〇1還有持續更新、修改或 刪除DFU及其構成零件,以反映此類的產品變更。此類的 DFU變更係儲存在適當的資料庫中,以接著在後續執行的 -24 -
鸨求计劃方法6 Ο 0中反映變更。 如圖所示’步驟6 0 2緊接在步驟6 Ο 1之後,並且必需為於 ν驟601中選取的每個準備預測。步驟6〇2實質上包括 執行用以產生需求預測的一組程序;這組程序包括預測產 生德% ’這將藉由下文中圖7及其步驟701至707的討論更 詳細說明。步驟6〇2的完成為使用者及根據本發明的需求預 測系統提供於步驟601中選取之每個〇171;的獨立需求預測。 現在凊參考圖7,其顯示用以更詳細描繪步驟6 0 2的流程 圖。如圖7所示,針對每個「啟用中」dfu (即,具有方法 6〇〇所建立之新預測的dfU)重複步驟701至707。在每個 德環’於步驟1 〇 3,·使用者準備並維護歷史資料。於步驟 1 0 3 ’根據本發明儲存的需求歷史資料包括(例如)運輸資料 流、訂單資料流及/或每個DFU的銷售點資料以及相關資訊 (包括過去的促銷、價格變動及非常態事件)。根據本發明 的需求計劃系統將此類的需求歷史資料儲存於歷史資料庫 中。一般而言,可將需求歷史資料分成兩個類型··基礎型 及非基礎型。基礎型歷史資料是可預測且可重複的需求資 料。反之,非基礎型歷史資料屬於因特殊事件(如促銷或極 端的市場情況)所引發之需求的一部份。就其本身而論,非 基礎型歷史資料通常是由影響過去需求的一部份所組成, 此類的需求被預期不會逐年重複或週期性重複。但是,非 基礎型歷史資料仍然會被儲存在歷史資料庫内,以協助使 用者預測特殊事件對未來需求的影響(以及識別因果因數以 及製作因果因數影響的模型,如前文參考圖3及圖5的說 _-25 - 本紙張尺度適用中國國家標準(CNS) Α4規格(210X 297公釐) 581955 A7 ____ B7 五、發明説明(23 ) 明)。需求預測方法600只使用基礎型歷史資料來開發及微 調特定模型的量、趨勢及季節樣態。非基礎型歷史資料的 適用性因案例而異,以協助預計對因果因數形式之預期特 殊事件所引發之需求的影響。於步驟7〇1,適當地隔離基礎 型歷史資料及非基礎型歷史資料。 熟知技藝人士很容易明白,需求歷史資料通常包括如果 納入預測未來需求之計算中則會造成問題的資料點(如需求 的波峰或波谷)。此類問題資料的實例包括:因非常態市場 狀況所引發的需求變化、由於淘汰或廢棄產品導致需求遞 減、一般資料錯誤以及使用者無法區別基礎型歷史資料與 非基礎型歷史資料。為了說明基礎型歷史資料或非.基礎型 歷史資料中此類的問題資料,於步驟7 〇 1,讓使用者有機會 調整歷史資料以克服此類問題。讓使用者·可調整歷史資料 的機制包括··手動編輯極高或極低值、控制用來忽略偶發 周期及/或其他不可靠歷史資料的相對歷史資料時間週期, 以及遮罩不可靠及/或偶發歷史資料的特定週期。 一旦於步驟7 0 1準備及維護相關歷史資料之後,於步驟 7〇2,利用根據本發明方法600的需求預測系統使用與啟用 中DFU之歷史資料相關的特定統計預測演算法來產生預測 (如圖3的詳細圖式所示)。為了產生需求預測的新模型,於 步驟1 0 :>’使用者嘗試決定衍生自如適當歷史資料證明之過 去需求模式之特定需求預測演算法的係數。決定此類係數 的執行係使用已知且公認的統計迴歸分析,如前文參考圖3 及圖5的說明。此時,提供起始的預測以供使用者檢視。 ___ - 26 - 本紙張尺度適用巾國S家標準(CNS) A4規格(210 X 297公爱) -
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k 581955 A7
在本發明的某些較佳具體實施例中,當於步驟7 〇 2建立模 5L 4在從相關的演异法及需求歷史資料產生模型時,方 法600利用一項自動調整器。自動調整器是一種反覆的演算 法,當使用者啟動時可識別模型錯誤,以協助改良未來需 求預測的品質。於步驟702,自動調整器執行所準備之模型 的評估(在通過步驟7G3之前),以嘗試依據兩項因數來識別 最佳化模型;模型適合度(需求預測與實際需求之間的錯誤 數量)及可行性。根據本發明,具有最佳模型適合度且仍然 滿足可用性的模型係數被視為最佳化模型。當決定錯誤 時,本發明可利用任何已知且公認的錯誤統計測量,包括 平均絕對誤差、平均付誤差、平滑平均平方誤差、根平 均平方誤差及平均絕對誤差。在決定變更自動調整器建議 的預測演算法係數是否可行的過程中,本發明湘模糊邏 輯來評估及平衡丁列五項屬性的指示項:動態平均值平滑 ,、季節數據圖表穩定度、i負誤差分佈、預測劇變及於 最近週期期間適合之預測錯誤的持續性。熟知技蓺人士很 容易明白’需求多半傾向平滑,而不是起伏不定y因此, 自動調整器嘗試防止因果因數形成過度反應的預測需.求。 另外,需求多半不會遇到極端增加或減少(劇變)。在此方 法中,模糊邏輯整體比較因果因數影響五項模糊邏輯因數 的變動狀況。藉此,自動調整器檢視統計迴歸分析產生的 模型,並且確定產生的模型可使用歷史資料來提供最好的 適用性。 於步驟702產生起始預測之後,於步驟7〇3,本發明的需 -27 - 本紙張尺度適用中國國家標準(CNS) A4規格(210X 297公釐) 581955
求預測方法600讓使用者能夠充分利用本身的組織知道來微 調自己的特定模型。在微調的過程中,使用者可引進及/或 如止產品、將歷史資料限制在特定時間週期、手動調整係 數的參數、確定歷史資料點或週期,或手動編輯基本量 (base level)或趨勢係數。 、例如,如果使用者已知在歷史資料的特定點發生需求模 式變化(即,在大規模及成功的廣告活動)之後,在該變化 之後使用者可能想要只依據需求歷史資料開發模型)。此 外’使用者可手動決定統計模型的微調參數,例如,其方 式為指示系統加重最近歷史資料的權值,因為在決定模型 係數過程中,敢近的歷史資料比較舊的歷史資料更具影響 力。另外,在檢視起始預測之後,使用者可藉由修訂歷史 資料來修改模型(其方法類似於前文關於步驟7〇丨討論的方 法)’如藉由遮罩無規律的資料週期。在此方法中,建立 「經過微調的」起始預測,並且繼續步驟7 0 4。 於步驟7 0 4 ’系統繼續調整特定模型產生的統計預測,使 預測考慮到計劃的行銷策略、競爭事件或偶發的單次事 變。此類非常態事變係以外部及内部事件形式發生。外部 事件是使用者或其組織設計用來模擬需求的計劃策。此 外’外部事件可包括競爭者引進新產品,或根據新產品預 測的為修配而拆用舊設備部件的市場。内部事件是商業狀 況’如促銷、交易或新產品問市。另外,此類非常態事變 也可包括忽視災難情況(暴風雨、暴風雪)及市場轉變。 例如,如果使用者已知組織想要針對啟用中的D F U提出 ___ _ - 28 - 本紙張尺度適财國®家標準(CNS) A4規格(210 X 297公董) "" "
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581955 A7 B7 五 發明説明( 26 促銷案(如客戶價格貼現),並且如果於步驟7 0 2產生的預測 模型已利用相關的非基礎型歷史資料,藉由採用適當的因 果因數來說明客戶貼現的影響,則使用者就能夠將因果因 數的各種數值(即、$ 5貼現、$ 1 〇貼現等等)輸入至需求計 劃系統中。然後,系統將允許使用者檢視因果因數的各種 數值對預測需求的影響(如並列的圖形顯示器或其他裝 置)° 一旦使用者已決定啟用中DFU的模型中採用之所有因. 果因數的特定數值,即完成步驟704。 於步驟704調整用以說明此類非常態事變的預測之後,於 步驟7〇5完成DFU預測。完成的預測被儲存在預測資料庫 中’並且藉此供使用者使用,.以在圖6的步驟603進行與其 他DFU的完成預測的比較。 於步驟7 0 5完成預測並且儲存至資料庫中之後,針對啟用 中D F U執行的需求預測方法結束。於步驟7 〇 6,本發明的需 求預測系統儲存歷史預測資料、儲存預測效能資料以及初 始化資料庫,以在獲取新資料流時儲存更新的需求歷史資 料’以便在未來執行方法600時可使用這個新資料,或者之 後視需要在現行需求預測方法6 0 0的步驟6 0 5中使用這個新 資料。 藉由在每個需求預測循環結束時儲存特定DFU的預測資 料’本發明的需求預測系統建置預測的歷史資料及模型資 料’然後,可與實際需求一起使用,以在一段時間週期期 間追蹤個別模型的效能。然後,這個預測效能資料可用來 將回饋提供給預測方法,並且協助改良其準確性。此外, -29 - 本紙張尺度適用中國國家標準(CNS) A4規格(210 X 297公釐)
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線 581955 A7 B7
為了維護及評估現行的需求預測,系統使用實際的需求資 料來持續更新相關的D F U歷史資料。 如圖7所示,針對於步驟6〇1選取的每個DFu重複步驟 70 1至706,直到已準備好每個〇171;的完成預測,並且於步 驟707符合最後的DFU條件。藉此,將於步驟6〇1選取之每 個DFU的完成預測提供給步驟6〇3。 請再次參考圖6,為使用者顯示每個DFU完成預測,並且 讓使用者能夠檢視及比較預測。於這個步驟,使用者能夠 比較(例如)模型變數、地點變數與屬於同一需求群組(依於 步驟6 0 1選取的D F U而定)的預測,以選取市場内特定產品 (需求單位)的最佳現行預測。然後於步驟6〇4,發行這些選 取的預測。 當於步驟604發行所選DFU的預測時,這些需求預測本質 上是「正式的」,因為接著會橫跨整個組織將這些需求預 測與適當的需求單位、需求群組及位置組合結合在一起 (即,相對於步驟6 0 3找到之較不適合的替代模型變數d F U 的預測)。在此方法中,發行的預測可供組織另一方使用, 包括不同部門(如銷售及行銷部門),或供下游電子計劃系 統使用(如製造計劃、供應鏈計劃及運輸網路計劃系統)。 根據本發明更佳具體實施例,當可取得實際需求(通常是 銷售)資料時,一旦藉由使用實際需求資料來開始進行方案 時,就可於步驟6 0 5 (圖中以虛線描繪,以強調步驟的視需 要使用性質)調整發行的需求預測,以說明比較流行的產品 促銷方案(貼現、降價等等)。在此類的具體實施例中,本 _ - 30 - 本紙張尺度適用中國國家標準(CNS) A4規格(210X 297公釐)
t明使用可用的實際需求資料來計算預測調整因數 F AF」,廷是用來依據下列方程式來修改目前發行的預 測:
Forecast! = Forecast〇 * FAF (方程式 〇 其中「Forecast」是起始「折扣轉折點前」(pre-break)需 f預測(即,考慮實際需求資料前的現行發行預測),而 、orecast。」疋經過调整「折扣轉折點後」(p〇st-break)需 求預測(即,考慮實際需求資料後)。faf係按照下列的方 程式來計算: FAF =(折扣轉折點前實際需求)/(折扣轉折點前預測需求) (方程式2). 其中调整預測或第「X」曰折扣轉折點後預測是從第X天 到促銷方案結束計算而得,其中「χ」是計算被調整預測的 曰期。舉例而言,在針對給sDFUt價格促銷的第三天早 上,希望調整預期,前兩天的實際需求資料(零售形式)如 下面的表格2所示。 曰期 折扣轉折點前預測 實際銷售 (#單位) (#單位) 第1天 45 52 弟2天 42 55 表格2 依據前面的方程式2計算得出,預測調整因數為: (52 + 55 )/( 45 + 42)= 1.2299 以此方式建立的第3天折扣轉折點後預測如下面的表格 所示。
581955
—-—_ 曰期 ϋ轉折點前預系厂 (#單位) 第3天折扣轉折點 前預測 —天 ------ 21 26 —_第4天 25 31 —^ 28 34 -_ 苐6天 31 38 天 35 43 表格3 :$視則面的表袼3時,應注意,應將折扣轉折點後預測值 。周i為下一整數。在此方法中,組織内的使用者獲得並且 發行調整預測,以在供應和運輸計劃中運用。 热知技藝人士應明白,需求預測方法6 〇 〇需要使用者互動 及監督的數量及類型因要處理的「新」資訊數量而異。在 已將需求歷史資料流隔離成基礎型及非基礎型歷史資料(於 而求預測方法6 0 0期間)並且沒有可用的新歷史資料的情況 下,步驟7 0 1僅需要需求預測系統識別適當資料庫中適當的 事先隔離歷史資料流。同樣地,如果使用者針對特sDFu 的預測需求,經過一段時間發展出特別精確且可信任的模 型,則於步驟6 0 4發行預測之前,前面於步驟6 〇 3檢視完成 預測很可能僅當作一道手續。反之,在已定義新產品或 DFU的情況下,則需要相當多的使用者互動。但是,在此 方法中,就先前執行的需求預測方法6 〇 〇而言,當在各種資 料庫中建置資料錄時,使用者在決策方面的工作(識別及隔 離歷史資料等等)通常會減輕。 -32 - 本紙張尺度適用中國國家標準(CNS) A4規格(210X 297公釐)
裝 訂
581955
現在請參考圖8,圖中顯示根據本發明具體實施例之電子 需求預測系統800之用以建立及發行需求預測之組件的原理 圖。前文中已參考圖3說明電子需求預測系統8〇()之較佳具 體實施例之個別元件的運作樣態及互動。如圖8所示,電子 需求預測系統8 0 0係由電子互相通訊中的複數個電腦化系統 所組成,包括:預測產生系統8 〇 1、GUI (圖形使用者介面) 和Web平台系統803及資料庫系統8〇2。構成預測系統8〇〇 的系統8 0 1至8 0 3均是由適當的伺服器(電腦裝置)、儲存裝 置(包括資料庫)、記憶體裝置及如電腦網路技藝中已知的 支援硬體所組成,以達成每個系統80丨至8〇3的功能,如下 文所述。但是,熟知技藝人士很容易明白,系統8〇1至8〇3 的功能可耩由單一電腦裝置執行,或藉由電子通訊中的電 腦裝置執行。構成電子需求預測系統800的系統8〇1至8〇3 僅僅係用來示範根據本發明的較佳系統如何在單一工作環 境中運作。 預測產生系統8 0 1提供處理功能、支援必要的電子和邏 輯,以依據前文參考圖3、5、6和7說明的方法,從適當的 歷史資料流、統計預測演算法及使用者輸入來建立DFu模 型。如圖所示,預測產生系統801進行與GUI和Web平台系 統8 0 3之間的電子通訊’以獲得使用者輸入,並且將輸出提 供給使用者。另外,於預測產生處理程序期間,若需要, 預測產生系統801進行與資料庫系統之間的電子通訊,以獲 得需求歷史資料、DFU描述、演算法函式及其他儲存的資 料。 -33 - 本紙張尺度適用中國國家標準(CNS) A4規格(210 X 297公釐)
裝 訂
581955
GUI和Web平台系統803當作預測系統8〇〇的前端,並且 允許使用者在必須時提供輸入給系統,以及檢視和比較預 測。已知在電腦網路連接技藝中,使用者可使用使用者介 面裝置805 (如工作站或個人電腦),按照如上文所述之方法 的4要來檢視及輸入資料805’。由於本發明的較佳具體實 施例是電腦網路,所以需要前端使用者介面,以准許使^ 者與資料庫系統8 0 2中的一個或一個以上資料互動,並且定 義複數個決策演算法。前端使用者介面還准許使用者檢視 及仏改4求預測,並且採用特定核准的預測,以遍及使用 者組織發行。前端使用者介面最好支援圖形互動,以及支 援透過如網際網路之類的分散式網路存取。就其本身而 論,本發明協助組織瞭解事件對需求的影響。另外,使用 分散式網路存取可提供橫跨整個合作組織的需求預測明顯 性。 資料庫系統802包括電子資料儲存襞置,以儲存供預測產 生系統801使用的各種資訊,用以依據本發明產生需求預 測資料庫系統可包括複數個關聯式資料庫(如歷史資料 庫、DFU資料庫及預測資料庫)或單一統一的資料庫資料 表。若需要,GUI和Web平台系統8〇3或預測產生系統8〇1 可從資料庫獲取資訊,或該該處獲取資訊。 視需要,需求預測系統800可以電子互連方式連接至外部 電腦系統,以提供額外的靈活性及公用程式(圖8中以虛線 “示此颌的外部電腦系統,以表示其視需要的性質)。例 如,需求預測系統800可視需要以電子互連方式連接至外部 ___ _- 34 - 本紙張尺度適用中國國家標準(CNS) A4規格(210 X 297公爱)
裝 訂
581955 A7 B7 五、發明説明(32 ) 電子需求資料源804,以獲取最新需求資料的資料饋送 8 04 (如銷售資訊)。在此方法中,可藉由利用產生的資料及 協力廠商輸入的資料來簡化歷史資料流維護(相對於使用者 必須自行輸入資料)。同樣地,需求預測系統8 〇〇可視需要 以電子互連方式連接至組織内部或組織範圍外的外部電子 電腦和管理系統,包括供應計劃系統8 〇 6、製造計劃系統 807及運輸計劃系統808。應明白,可將預測產生系統8〇1 發行的需求預測以電子方式傳輸8 〇 〇,至系統8 〇 6至8 〇 8,以 供組織針對預測的需求積極佈署其他區域時使用。例如, 發行的預期可用來訂購必要的原料、排定工廠資源以依據 預期的需求量進行製造,以及預約散發訂單所需的預期運 輸服務量。 熟知技藝人士應明白,可修改需求預測演算法以考慮特 定產業或狀況遇到的特定需要及/或問題。因此,解說的演 异法不應被視為限制如申請專利範圍的本發明。 雖然本發明最好以軟體實施,但是這不是本發明的限 制’熟知技藝人士應明白,本發明可以硬體或硬體與軟體 的各種組合實施,而不會脫離本發明的範疇。熟知技藝人 士所做的修改或替代均被視為屬於本發明的範疇,而不會 脫離僅%申請專利範圍所限制之本發明的範疇。 刖文已針對圖解及說明的目的提供本發明較佳具體實施 例的說明書。熟知技藝人士應明白可對發表的具體實施例 及本發明觀念進行各種修改及變化,而不會脫離如申請專 利範例之本發明的範疇與精神。 fiiii^(CNS) Α4^(21〇Χ297^)--~-
Claims (1)
- 581955 A8 B8 C8 D8 經濟部中央標隼局貝工消費合作社印製 申請專利範圍 1· 一種用以預測未來需求之方法,該方法包括: 提供複數個決策演算法及複數個需求資料歷史資料 流,該歷史資料資與一地點之一特定產品有關; 針對該地點該產品建立一個以上模型,每個該模型均 包括一配對之該等決策演算法的一個決策演算法及該等 需求資料歷史資料流的需求資料歷史資料流; 針對每個模型計算一預測,其方式是使用其成對的模 型預測演异法,統計迴歸分析每個模型的歷史資料流; 比較該等一個以上模型的該等預測,並且將某些預期 視為完成預測;以及 發行該等完成預測。 2 ·如申請專利範圍第1項之方法,其中針對每個模型計算 一預測的步驟包括:針對每個模型建立一起始預測,當 該起始預測比較其需求資料歷史資料流時具有最小錯 誤,並且調整該起始預測而成為可行的。 3 ·如申请專利範圍第2項之方法,其中調整該起始預測的 步驟包括平衡複數個可行因數,該等可行因數係選自由 動態平均值平滑度、季節數據圖表穩定度、正負錯誤分 佈、預測劇變及於最近歷史期間起始預測錯誤持續性所 組成的群組。 4.如申凊專利範圍第丨項之方法,其中每個預測係依據一 而求預期單位識別,並且其中該需求預期單位包括一需 求單位、一需求群組、一地點及一模型。 5 .如申%專利範圍第1項之方法,該方法進一步包括:依 . ^-- (請先閲讀背面之注意事項再填寫本頁) 訂 ♦ -36 -刈 1955 經濟部中央標隼局貝工消費合作社印装 申請專利範圍 2際需求資料超過該等預測預計之需求的比率來調整 該荨發行完成預測。 6·如申請專利範圍第5項之方法, — ^ ^ 具中將該比率乘以該發 仃完成預測之預計需求的餘數,以產生_調整發行預 測。 7·如:請專利範圍帛6項之方法,其中建立㈣等—個以 上模型允許使用者比較模型變數預測、位置變數預測及 硕似產品的預測。 8· —種用以建立未來需求預測之系統,該系統包括·· 一提供裝置,用以提供複數個需求演算法及複數個需 .求資料歷史資料流,該歷史資料資與一地點之一特定產 品有關; 一建立裝置,用以針對該地點該產品建立一個以上模 型,每個該模型均包括一配對之該等需求演算法之一及 該等需求資料歷史資料流之一; 一計算裝置,用以針對每個模型計算一預測,其方式 疋使用其成對的模型預測演算法,統計迴歸分析每個模 型的歷史資料流; 一比較裝置,用以比較該等一個以上模型的該等預 測’並且將某些預期視為完成預測;以及 一發行裝置,用以發行該等完成預測。 -37 本紙張尺度逋用中國國家標準(CNS ) A4规格(210X297公釐) --------------、1T----- (請先閲讀背面之注意事項再填寫本頁)
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-
2001
- 2001-10-29 AU AU2002214666A patent/AU2002214666A1/en not_active Abandoned
- 2001-10-29 EP EP01983221A patent/EP1350199A4/en not_active Withdrawn
- 2001-10-29 US US09/984,347 patent/US7080026B2/en not_active Expired - Fee Related
- 2001-10-29 TW TW090126766A patent/TW581955B/zh not_active IP Right Cessation
- 2001-10-29 WO PCT/US2001/042824 patent/WO2002037376A1/en not_active Application Discontinuation
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
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US8280757B2 (en) | 2005-02-04 | 2012-10-02 | Taiwan Semiconductor Manufacturing Co., Ltd. | Demand forecast system and method |
CN102890799A (zh) * | 2011-07-22 | 2013-01-23 | 埃森哲环球服务有限公司 | 业务成效权衡仿真器 |
TWI698829B (zh) * | 2018-03-22 | 2020-07-11 | 日商日立製作所股份有限公司 | 需求預測系統及方法 |
Also Published As
Publication number | Publication date |
---|---|
EP1350199A4 (en) | 2006-12-20 |
AU2002214666A1 (en) | 2002-05-15 |
WO2002037376A1 (en) | 2002-05-10 |
US20020169657A1 (en) | 2002-11-14 |
US7080026B2 (en) | 2006-07-18 |
EP1350199A1 (en) | 2003-10-08 |
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