Forecasting Workflows For demand management Amol Adgaonkar Ravi Uchil July 2012

Assumptions These slides intend to provide a baseline and raw material for discussion at the forecasting workshop These workflows and processes are suggestive and will need to be modified to suit the respective organizations to incorporate specific details The forecasting processes provide a pathway for continuous improvement and not a path for perfect results Forecasting is as much of an art as a science

Forecasting Workflows Data Mgmt. Workflows Application Workflows Collaboration Workflows Regular Actual v/s forecast monitoring and mass adjustments using Forecast Analysis screens Forecast Accuracy related KPIs (e.g. MAPE) and segmentation strategy around monitoring MAPE. Adjusting for the “Forecast Bias” using tracking signal Tuning Best fit results and recommendations Managing Outliers, their detection and adjustments Forecast parameters and stream configuration to adapt to changing business requirements

1. Forecast v/s Actual differences  Forecast v/s Actual difference monitoring can help identify certain high level business trends e.g. growth in sales over the last few months. Thus adjusting the “mean” of the forecast is important for better planning against the changes in demand

2. Forecast Accuracy & KPIs  (1 – MAPE) is a good measure of forecast accuracy across the model. However the MAPE values can be quiet high for slow movers due to the nature of the demand. An appropriate segmentation strategy is necessary for more realistic view of the business

A typical assortment scatter plot

3. Forecast bias adjustment  When the tracking signal is large, it suggests that the time series has undergone a shift; a larger value of the smoothing constant should be more responsive to a sudden shift in the underlying signal* Metric at a segment level Approach (fix what RSE, MAD) Mechanism to adjust forecast Reports / UI * Trigg, D.W. and Leach, A.G. (1967). "Exponential smoothing with an adaptive response rate". Operational Research Quarterly, 18 (1), 53–59

4. Best Fit Forecasting  Best Fit forecasting can generate the statistically appropriate recommendations provided the input parameters have been optimally designed. Various alternatives exist to compute the forecast with the least amount of errors.

4.Best Fit Options by segment Methods Window slices and slides Alpha, Beta, Gamma Intermittency Test Autocorrelation Analysis Smooth All except Croston’s & Double Exp. Increase window and slices and slides Widen grid search N/A Yes Erratic Same as Smooth May not be necessary (?) Intermittent Croston’s if > 12 slices else Single exp. N/A – because it looks at the entire horizon No Lumpy Best Fit (w/ MAPE as criteria) except Double Exp. Min Holdout window should be >6 months and the Max the number of slides

5. Outlier Management  It might be necessary to go beyond the application to find out the true sources of demand for the outliers. E.g. if is a non-standard order quantity or a data error or a potentially new sales channel that needs to be setup.

6. Forecast Parameters & Streams Tune Forecast Parameters Tune Alpha, Beta & Gamma values Tune number of periods of history Choice of forecast error for SS calculations Manage seasonal profiles Tune Best Fit Parameters Choice of forecast methods by segment Choice of error metrics to identify best fit Tune Global settings Redefine Stream configurations New business processes (e.g. Export demand) New requirements (e.g. Bin level forecasting)

7. Applying Additional Business Intelligence  It is essential to include additional knowledge from the business / field as a part of the forecasting process. This additional information (either at a pair level or at a segment level) originates outside the statistical realm of the application and needs to be applied as an adjustment or an override to the standard output

Workflow Summary & Tools No. Name KPIs UI & Reports 1 Forecast v/s Actual Pair counts Review reasons 2 Forecast Accuracy MAPE, MAD & RMSE Custom Report 3 Forecast Bias Adjustment Tracking Signal Custom Report & UI 4 Best Fit forecast tuning Best fit errors 5 Outlier Management Outlier counts 6 Forecast Parameters & Stream Configurations Business requirements UI 7 Apply Business Intelligence Additional Information from Business UI / Servigistics Support

DMAIC Cycles No. Define Measure Analyze Improve Control 1 Forecast v/s Actual % Diff >, < Tolerance Root cause of variation (E.g. Sales increases) Forecast Adjustments Chart the performance 2 Forecast Accuracy MAPE, MAD & RMSE Measurement criteria (segmentation) Forecast Parameters Chart the performance & segmentation 3 Forecast Bias Adjustment Tracking Signal Recent shifts in demand patterns Forecast responsiveness Track the tracking signals over time 4 Best Fit forecast tuning Best fit errors Algorithmic alignment with data segments Best fit parameters Track Best fit forecast accuracy 5 Outlier Management Outlier counts Demand sources for the outliers Tune outlier detection and adjustment parameters Monitor Outlier counts 6 Define New Business Requirements Forecast Parameters & Stream Configurations Identify gaps in alignment with Business requirements Implement changes Monitor Impact of changes

Metric aggregation techniques - TBD No. Workflow Measure Technique Weighting factor Notes 1 Forecast v/s Actual % Diff >, < Tolerance Segment where % Diff > threshold. Requires custom columns to store aggregated values over months 2 Forecast Accuracy MAPE, MAD & RMSE Weighted average of MAPE Demand Order lines/units ? Another option is ∑MAPE or ∑MAD (for the warehouse) 3 Forecast Bias Adjustment Tracking Signal Count of pairs > 0.5 and < 0.5 4 Best Fit forecast tuning Best fit errors 5 Outlier Management Outlier counts Count of outliers 6 Define New Business Requirements Forecast Parameters & Stream Configurations

Process Cadence No. Daily Weekly Monthly Quarterly As Required 1 Forecast v/s Actual 2 Forecast Accuracy 3 Forecast Bias Adjustment 4 Best Fit forecast tuning 5 Outlier Management 6 Forecast Parameters & Stream Configurations 7 Additional Business Intelligence

Forecast Collaboration & Consensus Collaborate in parallel rather than sequential mode Use a single reference table Develop aggregated reports / pivot tables in units and dollars Incorporate market intelligence via Forecast Analysis and disaggregate in proportion Top down & bottom up approach Collaborate on demand and gain consensus for the most accurate forecast   Ability to do consensus forecasting considering multiple plan inputs from within an organization and the ability to do collaborative planning by considering multiple plan inputs external to the organization. Consensus forecasting involves collaborating with both internal constituents (such as sales/mktg) and external constituents (such as suppliers) to gather their version of the demand plan. Typically, the Demand Planner will rationalize these various plan inputs to develop a “first cut” version of the final Demand Plan. This “first cut” plan is then analyzed and debated by all the process constituents with the end result being a final Demand Plan. This Demand Plan is then sent downstream for supply planning consideration. It is important to gather as much information from those closest to the end consumer (i.e., sales reps, marketers, distributors, and retailers) so that as much of the guesswork as possible can be taken out of the development of the demand plan.