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

1. DEMAND PLANNING LEADERSHIP EXCHANGE
PRESENTS:
The web event will begin momentarily with
your host:
& Guest Commentator
November 13th, 2012 plan4demand

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. Forecast Accuracy Review – Inventory Review
Forecast Accuracy vs. Safety Stock
Forecast Accuracy vs. the Plant
Conservative Forecast Bias
Effects of Pre-build
Bottom Line

4. Headline:
The safety stock component of inventory is directly impacted by
changes in Forecast Accuracy
How 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. 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. 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. 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. 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. Inventory reaches various locations for different
reasons; 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. All Safety Stock Strategies are grounded in Forecast
Accuracy
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. How Deep Does Your Forecast Accuracy
Monitoring/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. 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. “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 11
Forecast 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 6
Actual 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. 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 11
Forecast 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 6
Actual X X X X X X X X X X X X

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 4
Creation
Creation
Forecast
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. 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 4
Forecas
Forecas
Creatio
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. 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. Headline:
Errors on the high side protects Customer Service levels &
maintains top line revenue projections
How 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. An indicator identifying if the error across the data sample is
chronically high or low
– This tendency to over or under forecast can have a rippling affect
across the supply chain
Is measured over multiple periods of the same forecast, or
measured at lead time
An 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
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. Cycle Stock
Average Inventory
Inventory
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. 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 FVA
Source: Michael Gilliland SAS Chicago APICS 2011

23. Headline:
Pre-building inventory defeats any initiative to reduce
safety stock through improved forecast accuracy
How 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. With pre-built inventory the importance of forecasts accuracy extends
much 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

26. Communication is the Key to leverage Forecast Accuracy
Improvements
The reach is far. Safety Stock / Inventory Commitment
Worry 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 opportunities
Forecast Accuracy measurements are a tool to leverage
performance not a club to discipline performance

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 your
Demand Planners

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