Planning and Sourcing

Transcription

1 Planning and Sourcing Sales Forecast Accuracy A Lot of Talk, but Is There Enough Action? Facilitated by Matt Wilkerson and Colin Maxwell September 9-10, 2008 New Orleans, LA

2 Session Content Analysis of the data collected Session scope Best practices in forecasting 2

3 Analysis of the Data Collected Participants by Industry Participants by Segment Re tail 27% Whls/Distr 4% Department Store and Discount 12% Automotive and Truck Parts 8% Electronics 37% CPG 69% Personal Care and Drugs 12% Statistics on Participants: Grocery, Food and Beverage 31% Last 3 years of input: 2006, 2007 and 2008 Annual revenues between $450 MM and $6,400 MM Industries / Segments 3

4 Session Scope Consortium Data for this Session will Focus on: Forecast accuracy measurement Most used metrics Gap between the forecast accuracy attained vs. goal Forecasting systems: Purchased vs. in-house developed; plans over the next 3 years Available vs. most used forecasting techniques Available vs. most used forecasting software features Readiness for collaborative forecasting, planning and replenishment (CFPR) Taking into account the qualitative judgments, and measuring their respective impact on accuracy Importance of certain factors in creating forecast errors Tracking lost sales or the gap between demand and your actual sales Characterization of the forecast accuracy attained and future trend from the subjective standpoint 4

5 Forecast Accuracy Measurement Forecast Accuracy Metrics % Importance (max = 10) % 80% 70% 60% 50% 40% 30% 20% 10% Most Utilized (%) 0 Mean absolute percent error (MAPE) Percent error Forecast ratio Standard Mean error Mean deviation absolute error (or deviation) Mean squared error 0% Metrics MAPE is the most utilized method. MAPE, along with Forecast Percent Error, are perceived as the top two important metrics. 5

6 Forecast Accuracy Measurement MAPE Attained vs. Goal by Industry / Segment 80.0% Mean Absolute Percent Error (%) 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 35.0% 15.0% CPG / Electronics 42.7% 30.3% CPG / Grocery & Foods 25.0% 20.0% CPG / Personal Care 36.9% 22.3% 27.5% Overall CPG Retail / Electronics 15.0% 24.8% 23.9% Retail / Grocery & Foods 20.0% 25.0% 32.5% 20.0% 20.9% 18.5% Retail / Department Store Overall Retail Overall CPG + Retail Industry / Segment The gap - - between the current performance and the goal varies by industry / segment 6

7 Forecasting Software More than 75% of participating companies use an automated system, with one-third of them using an in-house system. All participants indicated that over the next 3 years, they will upgrade their system rather than purchasing another system in-house developed system 21% Forecasting Solution manual 21% purchased automated system 58% 7

8 Forecast Techniques Forecasting Techniques Mostly Used 100% 80% 60% 40% 20% 0% 0% 0% Trend Line Analysis and Box-Jenkins, if available, are the most used, along with Exponential Smoothing and The Moving Average. 8 % Available, % Used Exponential smoothing Moving average Regression Life cycle analysis Straight line projection Naïve forecast Trend line analysis Decomposition Simulation Box-Jenkins OMTS Other OMTS Expert systems Neural networks Technique % Available % Used % Used if Available

9 Forecasting Software Features % Available % Used if Available Features 59% 53% 41% 63% 59% 59% 47% 41% 71% 35% 35% 53% 41% 53% 53% 53% 18% 0% 100% 100% 100% 90% 90% 90% 88% 86% 83% 83% 83% 78% 71% 67% 44% 33% 0% 0% Able to automatically identify one-time events in historical data or current forecasts and allow edits Able to transfer history from preceding to succeeding SKUs, based on an effective date Software automatically selects or recommends the best fit forecasting technique or algorithm Able to add, edit or delete specific events - promotions, holidays, etc. at product, customer or market levels Supports commenting at the appropriate level of detail to document subjective adjustments to the forecast Software allows user to select different forecasting techniques or algorithms for each product or product group Recognizes future confirmed orders and forecasts accordingly Able to identify out-of-stock periods and adjust future periods appropriately Software allows multiple users to view the forecast concurrently online Able to identify product groups that may need re-forecasting due to forecast error, profit contribution etc. Able to specify number of sales days in each period Able to "lock" forecasts at the product or product group levels while other portions of the forecast are updated Ability to adjust forecasts using a graphical interface to drag a forecast line or similar graphic (i.e. drag and drop) Software allows multiple users to update the forecast concurrently online Software allows the forecast to be updated over the Internet by authorized users Software allows the forecast to be viewed over the Internet by authorized users Calculates dependent demand for SKUs which are components of other SKUs (kits) through multiple level bills of material. Supports a range of possible demands, based on user-defined confidence intervals Most used features, along with second most used, if available. Also highlighted the least used. 9

10 Readiness for Collaborative Forecasting Forecasting Software Readiness for CFPR 6% 18% 18% 58% Software runs on a server that supports multiple simultaneous users Software runs on a server using technologies that support multiple simultaneous users over the Internet (or an Intranet) Software runs on standalone PC's Software runs on standalone PC's, but forecast files can be shared over a network Over 75 percent of participating companies have the forecasting system configuration suitable for CFPR. 10

11 Handling the Qualitative Input Handling the Qualitative Input 100% 90% 80% NO NO 70% NO To what Extent 60% 50% 40% 30% YES YES 20% YES 10% 0% a. Documented and entered? b. Established ownership / accountability? c. Analyzed & measured afterwards? The Business Intelligence is captured, documented and accounted for in over 70% of the cases, yet less than half are post-analyzed for performance. 11

12 Factors in Creating Forecast Errors Critical Factors in Creating Forecasting Errors Importance across industries Identified as critical Mentioned as factors Marketing activities tend to have the biggest impact on forecast accuracy. 12 Trade promotions, discounts New product introductions Inaccurate historical data Limited fcst capabilities Poor supply chain execution Limited fcst staff, skills Lack of collaborative fcst Vendor/supplier shortfalls Forecast is finance driven Production shortfalls Poor in store ops (retail only) Inappropriate fcst techniques Sales Force incentive to over fcst Dominant infl. of single department Dominant infl. of single individual Distr. incentive to over forecast Factors reviewed 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Criticality (max = 10) Mentioned (%)

13 Tracking Lost Sales Tracking / Estimation of Lost Sales Importance across industries Identified as critical Mentioned % % 60% 50% 40% 30% 20% Criticality (max = 10) Mentioned (%) 90% 80% 10% 0% Orders placed but not filled Sales force feedback Customers sharing their sales forecasts Independent distributor feedback Reported market growth for all competitors Customer surveys Market research on industry and market trends Sources reviewed

14 Tracking Lost Sales Conclusions: One-third of participants don t track lost sales at all Orders placed but not filled deemed to be the most critical source. Across industries Don't Track 33% Track 67% 14

15 Subjective Standpoint Current and Future CURRENT Self Evaluation of the Forecast Accuracy FUTURE Trend Evaluation of the Forecast Accuracy Somewhat accurate, meets some of our requirements, but could be improved 53% 12% Substantially accurate, meets most of our requirements 35% Innaccurate, meets few requirements, could be improved significantly 12% No change in accuracy 56% Improving moderately 44% 35% are currently content with the attained forecast accuracy, yet 56% don t expect an improvement in the near future. 15

16 Best Practices in Forecasting Highest return from investing in forecasting systems can be attained when: A single version of the truth" is promoted: Across the organization: (1) using the same repository system and (2) the plan can be uniquely converted from the SKU unit level to the financial level Collaboration with the other supply chains in the vertical Resulting Forecast = Quantitative calculation + Qualitative input Promotional Events are: Communicated, documented and entered within the set time fence Tracked for performance and analyzed for the impact on accuracy Forecast Accuracy is continuously and consistently measured: MAPE (Mean Absolute Percent Error) by ABC class Together with % of SKU count over a set forecast accuracy Together with monitoring of the Forecast Bias factor (over-forecasted vs. under-forecasted) Deviations are identified, accounted for, explained, learned from, and their impact is looped back in the forecasting exercise. 16