Best indicator adaptive forecasting method

• US 20050096964A1
• Filed: 10/29/2003
• Published: 05/05/2005
• Est. Priority Date: 10/29/2003
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Tsai, Roger Yen-Luen
• Application Number:
  US10/694,737
• Publication Number:
  US 20050096964A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A Best Indicator Adaptive (BIA) method fuses several singular indicators into one composite model to provide a new forecasting combination scheme. BIA uses the sizes of the spread of the distribution taking into account the variation of the distribution parameters themselves. Underlying the BIA method is the common theme and unifying theory of the power of quotient and the methods of making use of order composition and sales opportunities pipeline progression.

Claims

1. A computer implemented best indicator adaptive method for demand forecasting comprising the steps of:

implementing a plurality of forecasting subsystems which make use of one or more different indicators;

generating forecasts based on one or more of said indicators;

refining the forecasts based on distribution demand; and

selecting a single composite forecast model for demand forecasting of a product.

2. The computer implemented method recited in claim 1, wherein the different indicators used by the plurality of forecasting subsystems include Load (L), Ship (S) and Customer Acceptances history (CA_hist).

3. The computer implemented method recited in claim 2, wherein the step of generating forecasts includes the steps of:

generating a forecast from Load (L);

generating a forecast from Ship (S);

generating a forecast from Load and Ship (LS); and

generating a forecast from Customer Acceptances history (CA_hist).

4. The computer implemented method recited in claim 3, wherein the step of refining the forecasts based on distribution demand using Customer Requested Date (CRAD) and includes the steps of:

generating a forecast from Load (L) and CRAD as CA_L,CRAD;

generating a forecast from Ship (S) and CRAD as CA_S,CRAD; and

generating a forecast from Load (L) and Ship (S) as CA_LS,CRAD.

5. The computer implemented method recited in claim 4, wherein the step of selecting a single composite forecast model for demand forecasting of a product includes the steps of:

for each forecast CA_L, CA_S, CA_LS, CA_L,CRAD, CA_S,CRAD, CA_LS,CRAD and CA_hist, determining a forecast error;

eliminating CA_LS and CA_LS,CRAD if data is for a historical period shorter than a predetermined period;

eliminating any other forecast due to expert knowledge;

for all remaining forecasts, selecting a forecast having a smallest error; and

outputting a selected forecast as an optimum forecast.

6. A computer implemented best indicator adaptive method for demand forecasting comprising the steps of:

implementing a plurality of forecasting subsystems making use of single, double or triple sets of four sources of information, Load (L), Ship (S), Customer Acceptances (CA), and Customer Request Date (CRAD);

forecasting Customer Acceptances (CA) based on Load (L) to generate CA_L;

forecasting Customer Acceptances (CA) based on Ship (S) to generate CA_S;

forecasting Customer Acceptances (CA) based on Load (L), Ship (S) and Customer Acceptances history (CA_hist) to generate CA_LS;

using a log mean to sigma ratio of CRAD distribution, adjusting the forecasts CA_L, CA_S and CA_L,S to arrive at more accurate forecasts CA_L,CRAD, CA_S,CRAD, and CA_LS,CRAD; and

using adaptive optimization, selecting a final optimum forecast with a smallest mean average percent historical error specific to geography and product grouping while eliminating candidates based on dependency of forecast error of individual candidates on length of historical data.

7. The computer implemented method recited in claim 6, wherein the step of selecting a final optimum forecast includes the steps of:

for each forecast CA_L, CA_S, CA_LS, CA_L,CRAD, CA_S,CRAD, CA_LS,CRAD, and CA_hist, determining a forecast error;

eliminating CA_LS and CA_LS,CRAD if data is for a historical period shorter than a predetermined period;

eliminating any other forecast due to expert knowledge;

for all remaining forecasts, selecting a forecast having a smallest error; and

outputting a selected forecast as an optimum forecast.