DEMAND PLANNING LEADERSHIP EXCHANGEPRESENTS:                      The web event will begin momentarily with               ...
Sector     Days +   %+                                                                  CPG          8      13.5%         ...
Forecast Accuracy Review – Inventory ReviewForecast Accuracy vs. Safety StockForecast Accuracy vs. the PlantConservative F...
Headline: The safety stock component of inventory is directly impacted by                   changes in Forecast AccuracyHo...
Forecast Accuracy Performance Goals   The goal of Forecast Performance management is to:   – Maximize the amount of actual...
Factors that generally affect Forecast Performance:    Sales Volume    – The higher the volume of product sales, the more ...
Forecast Error is caused by:    Lack of Forecast Validity    – Applying market intelligence to the wrong time period or pr...
Understanding Accuracy & Relative Bias   Certain measures should be integrated into the Demand   Planning process   –   Bi...
Inventory reaches various locations for differentreasons; each reason has a different characteristic.                     ...
All Safety Stock Strategies are grounded in ForecastAccuracy    Directly Effected where the change is Forecast Accuracy is...
How Deep Does Your Forecast AccuracyMonitoring/Participation Methodology Go?   Answer on the right hand side of your scree...
Headline:     Improving Forecast Accuracy is Meaningful to the Plant!How to Effect the Trend Line:    Engage Manufacturing...
“Lag” is the number of time periods between forecast creation period        and forecast target period                    ...
Improves over time for the same lag as we learn to forecast                    better                    – Improved model ...
Inventory commitment occurs continuously throughout the manufacturing process                     Out-Sourced             ...
Forecast Accuracy needs to be measured where inventory commitment is Highest      –         Institutionalize a process for...
Where do you measure your Forecast accuracy?     Answer on the right hand side of your screen         Select appropriate l...
Headline:  Errors on the high side protects Customer Service levels &             maintains top line revenue projectionsHo...
An indicator identifying if the error across the data sample ischronically high or low– This tendency to over or under for...
20 Bias is more critical than accuracy on a single SKU           Constantly over forecasting by 20% is more damaging than ...
Cycle Stock                                                                                               Average Inventor...
Forecast Value Add (FVA) is used to identify the overall effect that an activity          has on forecast accuracy /error....
Headline:  Pre-building inventory defeats any initiative to reduce    safety stock through improved forecast accuracyHow t...
With pre-built inventory the importance of forecasts accuracy extendsmuch further into the future                         ...
25
Communication is the Key to leverage Forecast AccuracyImprovements   The reach is far. Safety Stock / Inventory Commitment...
Increasing Forecast Accuracy CAN Reduce Inventory-Adjust SS Strategy-Align Demand Signal with Manufacturing-Focus on the “...
Join us on LinkedIn: Demand Planning Leadership ExchangeFollow us on Twitter: @Plan4Demand             Complete our survey...
Contact us today!
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Demand Planning Leadership Exchange: Increasing Forecast Accuracy... Does it Really Reduce Inventory?

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866.P4D.INFO | Plan4Demand.com | Info@plan4demand.com

Trend Lines vs. Headlines

The standard supply chain planning philosophy is that by
increasing forecast accuracy, you can better manage and
reduce inventory levels. Is this really true?

Does the trending data back up this common assumption?

The general rule of thumb claims 1% of forecast accuracy improvement should reduce inventory by 1%, up to about
an 80% accuracy level before hitting a point of diminishing
return. Over the last decade global companies have focused their efforts on supply chain management best practices. Despite the headlines and success stories, a recent survey revealed that 3 out of the 4 business sectors actually had their days-on-hand inventory increase… Why?

It’s time to get focused in on the trend lines, and understand what’s really fueling the headlines. This Leadership Exchange webinar will provide practical insight and pragmatic tips to connect forecast accuracy with inventory effectively.
A few key take-a-ways from this session include:
• Understanding how Forecast Accuracy impacts different Inventory types
• How to synchronize for results all the way down to the Plant level
• Where and When Forecast Bias fits into the mix
Make Forecast Accuracy Headlines That
Translate Into Inventory Reduction Trend Lines

Join our exclusive
Demand Planning
Leadership Exchange Group
on LinkedIn http://linkd.in/DPLeadershipExchange

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Demand Planning Leadership Exchange: Increasing Forecast Accuracy... Does it Really Reduce Inventory?

  1. 1. DEMAND PLANNING LEADERSHIP EXCHANGEPRESENTS: The web event will begin momentarily with your host: & Guest CommentatorNovember 13th, 2012 plan4demand
  2. 2. Sector Days + %+ CPG 8 13.5% Chemical 1.3 1.8% Pharma 9.6 7.8% Despite the headlines and success stories, a recent survey, for the period 2000 to 2011, revealed that 3 out of the 4 business sectors actually had their days-on-hand inventory increase … Why? Where and When did Forecast Accuracy Initiatives fail to impact inventory levels? What potential areas should we look at to explain the lack of impact?Source : Supply Chain Insights LLC
  3. 3. Forecast Accuracy Review – Inventory ReviewForecast Accuracy vs. Safety StockForecast Accuracy vs. the PlantConservative Forecast BiasEffects of Pre-buildBottom Line
  4. 4. Headline: The safety stock component of inventory is directly impacted by changes in Forecast AccuracyHow this effects the Trend Line: Adjusting safety stock policy is a critical step in ensuring forecast accuracy initiatives will have a lasting effect Depending on the magnitude of the safety stock in comparison to the overall inventory, improving forecast accuracy may or may not have a large impact on the overall inventory Understanding the percentage of inventory types is essential in deciding if improving forecast accuracy will impact inventory levels significantly
  5. 5. Forecast Accuracy Performance Goals The goal of Forecast Performance management is to: – Maximize the amount of actual demand that is explained by the forecast in order to minimize noise – Provide feedback to the forecasting process to minimize bias • Enable continuous forecast improvement Demand forecasts are: – Made for specific time periods (weeks, months) and are extended over a specific forecast horizon – Subject to forecast error Demand forecasts are NOT : – Goals, targets, or objectives – Expected to be absolutely right
  6. 6. Factors that generally affect Forecast Performance: Sales Volume – The higher the volume of product sales, the more accurate the forecast will be Forecast Lag – Accuracy improves the closer to the time of sales – Customer data and market intelligence reliability increases with time as well Competition – In markets with heavy competition, forecasting is difficult due to unpredictable competitor behavior Product Life-Cycle Stage – Mature products are more predictable than new or declining products
  7. 7. Forecast Error is caused by: Lack of Forecast Validity – Applying market intelligence to the wrong time period or products – Using invalid history to generate the forecast – Poor Statistical/Algorithm models that do not correctly identify seasonal patterns or shifts in demand levels Bias (not Error!) – Unrealistic expectations by individuals or groups – Forcing the Total forecast to equal a target without taking into account how the demand for individual product will be affected – A lack of vision to external factors Noise – Random fluctuation in demand – Noise generally cannot be predicted nor forecasted
  8. 8. Understanding Accuracy & Relative Bias Certain measures should be integrated into the Demand Planning process – Bias – Forecast Accuracy (FA) – Mean Absolute Percent Error (MAPE) – Weighted Mean Absolute Percent Error (WMAPE) – Coefficient of Variation (CV) – Forecast Value Add (FVA)
  9. 9. Inventory reaches various locations for differentreasons; each reason has a different characteristic. Safety Stocks Stochastic Stochastic Inventory Profile Cycle Stocks Linear Nonlinear Pre-Build Stocks Pipeline Stocks Deterministic Deterministic Linear Nonlinear Merchandizing Stocks Manufacturing Lead Time
  10. 10. All Safety Stock Strategies are grounded in ForecastAccuracy Directly Effected where the change is Forecast Accuracy is carried into the Calculation – Statistical Safety Stock: Safety Factor X MSE x Plan Lead Time* • Mean Square Error Fcst Duration * or Mfg Lead Time Indirectly Effected where the change is Forecast Accuracy will require direct Planner intervention – Days Forward Coverage • Number of Days are based Management Policy – Reorder Point • Management Policy
  11. 11. How Deep Does Your Forecast AccuracyMonitoring/Participation Methodology Go? Answer on the right hand side of your screen Select ALL departments that apply A. Marketing B. Sales C. Manufacturing D. Supply Planning E. Demand Planning F. Customer Service
  12. 12. Headline: Improving Forecast Accuracy is Meaningful to the Plant!How to Effect the Trend Line: Engage Manufacturing in the Process Measure and take action on the correct lag to provide the best results for inventory reduction – Synchronize the Demand Planning lag measurement with the period where critical Inventory decisions are made • Raw Material • Brite’s – Postponement • Pre-Builds Align Manufacturing with the Demand Signal – The more in sync the production plan is with the demand plan, the better! – This ensures the Plant makes inventory that is required …. not just desired
  13. 13. “Lag” is the number of time periods between forecast creation period and forecast target period Forecast Target Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Lag 10 Lag 11Forecast Creation Feb Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Lag 10 Mar Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Month Apr Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 May Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Jun Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6Actual X X X X X X X X X X X X Which forecast should we chose to compare to the actual demand? – Choose one or more “critical” lags when commitments are made – Lead time is a good representation of the point of commitment
  14. 14. Improves over time for the same lag as we learn to forecast better – Improved model tuning – Improved incorporation of market intelligence Improves as the lag decreases for the same target period – More current information, including history for recent periods – More concrete promotional and market program information Forecast Target Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Lag 10 Lag 11Forecast Creation Feb Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Lag 10 Mar Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Month Apr Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 May Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Jun Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6Actual X X X X X X X X X X X X
  15. 15. Inventory commitment occurs continuously throughout the manufacturing process Out-Sourced In-Sourced Packaging Operations Cooking / Mixing Raw Material Packaging Inventory Commitment Jan Feb Mar Apr May Jan Feb Mar Apr May Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4Creation CreationForecast Forecast Feb Lag 0 Lag 1 Lag 2 Lag 3 Feb Lag 0 Lag 1 Lag 2 Lag 3 Mar Lag 0 Lag 1 Lag 2 Mar Lag 0 Lag 1 Lag 2 Actual X X X X X Actual X X X X X
  16. 16. Forecast Accuracy needs to be measured where inventory commitment is Highest – Institutionalize a process for where plants have visibility into the end volatility of their inventory In-Sourced Out-Sourced Packaging Operations Cooking / Mixing Raw Material Packaging Inventory Commitment Jan Feb Mar Apr May Jan Feb Mar Apr May Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4Forecas ForecasCreatio Creatio Feb Lag 0 Lag 1 Lag 2 Lag 3 Feb Lag 0 Lag 1 Lag 2 Lag 3 t t Mar Lag 0 Lag 1 Lag 2 Mar Lag 0 Lag 1 Lag 2
  17. 17. Where do you measure your Forecast accuracy? Answer on the right hand side of your screen Select appropriate lag that apply A. Only Measure at a Single Lag (0) B. Measure at Manufacturing Lag (2-3) C. Measure at Raw Material Lead Time Lag (3-4) D. Measure at Deployment Lag (0-1) E. Don’t Know!
  18. 18. Headline: Errors on the high side protects Customer Service levels & maintains top line revenue projectionsHow this Effects the Trend Line: Forecast bias directly affects the cycle stock Persistent same sign errors (BIAS) extends the time inventory remains in cycle stock Measuring and then lowering forecast bias can optimize cycle stock levels ABC classification will help guide you to important data points
  19. 19. An indicator identifying if the error across the data sample ischronically high or low– This tendency to over or under forecast can have a rippling affect across the supply chainIs measured over multiple periods of the same forecast, ormeasured at lead timeAn indicator of a significant demand change– highlighting periods where the fitted forecast has relative error outside of a threshold over the time horizon selected.
  20. 20. 20 Bias is more critical than accuracy on a single SKU Constantly over forecasting by 20% is more damaging than over forecasting 30% one month than under forecasting 30% the next… Abs Pct Abs Pct Fcst Absolute Pct Fcst Fcst Fcst Absolute Pct Fcst Fcst Hist Fcst Error Error Error Error Hist Fcst Error Error Error Error Period 1 500 650 (150.00) 150 -30.00% 30.00% Period 1 500 600 (100.00) 100 -20.00% 20.00% Period 2 650 455 195.00 195 30.00% 30.00% Period 2 520 650 (130.00) 130 -25.00% 25.00% Period 3 550 715 (165.00) 165 -30.00% 30.00% Period 3 550 605 (55.00) 55 -10.00% 10.00% Total 1700 1820 (120.00) 510 -7.06% 30.00% Total 1570 1855 (285.00) 285 -18.15% 18.15% • In this example, a period of over-forecasting is • In this example, the SKU was consistently followed by a period of under forecasting over-forecasted every period • In total, the SKU was off by 120 units over • In total, the SKU was off by 285 units over three periods for a Forecast Error of 7.06% three periods for a Forecast Error of 18.15% • Although Error on a period by period basis was worse on the left, you can see the Net Error was better over time
  21. 21. Cycle Stock Average InventoryInventory Inventory Order Qty Cycle Stock Average Inventory Safety Stock Safety Stock Time Time A biased forecast can: – Create surplus inventory through over forecasting by increasing the average days of inventory on hand – Under forecasting forces an unnecessary out-of-stock position • Decreases customer service levels • Increases costs due to inventory expediting and production overtime
  22. 22. Forecast Value Add (FVA) is used to identify the overall effect that an activity has on forecast accuracy /error. Along with Coefficient of Variation (CV), the FVA will allow you to: – Identify ability to affect change on “forecast-able” products – Classify those products that require significant effort with little return – Evaluate relative planner effectiveness and workload among other team members – In FVA analysis, you would compare the analyst’s override to the statistically generated forecast to determine if the override makes the forecast better In this case, the naïve model was able to achieve MAPE of 25% • The statistical forecast added value by reducing MAPE five percentage points to 20% • However, the analyst override actually made the forecast worse, increasing MAPE to 30% • The override’s FVA was five percentage points less than the naïve model’s FVA, and was 10 percentage points less than the statistical forecast’s FVASource: Michael Gilliland SAS Chicago APICS 2011
  23. 23. Headline: Pre-building inventory defeats any initiative to reduce safety stock through improved forecast accuracyHow to Effect the Trend Line: Understand how much the business “pre-builds” When & Where inventory decisions are occurring – Shifts decisions further into the future and adjust the lag analysis
  24. 24. With pre-built inventory the importance of forecasts accuracy extendsmuch further into the future Packaging Operations Cooking / Mixing Raw Material Inventory Commitment Packaging Jan Feb Mar Apr May June July Aug Sept Nov Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9 Creation Forecast Feb Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Mar Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7
  25. 25. 25
  26. 26. Communication is the Key to leverage Forecast AccuracyImprovements The reach is far. Safety Stock / Inventory CommitmentWorry about Trend Lines not The Headlines It is about solving tomorrows problems, Today Use the Head Lines to point you to the Trend Line decisions Forecasting processes that are not far reaching in their focus are missing large opportunitiesForecast Accuracy measurements are a tool to leverageperformance not a club to discipline performance
  27. 27. Increasing Forecast Accuracy CAN Reduce Inventory-Adjust SS Strategy-Align Demand Signal with Manufacturing-Focus on the “Right” LAGs for your organization-Acknowledge BIAS and Address it!-ABC Classification Consensus-Utilize FVA (Once Mature) and build confidence in yourDemand Planners
  28. 28. Join us on LinkedIn: Demand Planning Leadership ExchangeFollow us on Twitter: @Plan4Demand Complete our survey & receive a $5 Starbucks Gift Card Upcoming Leadership Exchanges Save the Date! November 15th December 5th Supply Planning Leadership Exchange: S&OP Leadership Exchange: SAP PP/DS: S&OP KPIs & Metrics What You Need to Know Setting a course to achieve ROI
  29. 29. Contact us today!
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