1. Forecasting Workflows
For demand management
Amol Adgaonkar
Ravi Uchil
July 2012

2. 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

3. 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

4. 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

5. 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

6. A typical assortment scatter plot

7. 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

8. 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.

9. 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

10. 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.

11. 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)

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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.