November 13th, 2012 plan4demand
DEMAND PLANNING LEADERSHIP EXCHANGE PRESENTS:
The web event will begin momentarily with your host:
& Guest Commentator
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
Sector Days + % +
CPG 8 13.5%
Chemical 1.3 1.8%
Pharma 9.6 7.8%
Source : Supply Chain Insights LLC
… 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?
Forecast Accuracy Review – Inventory Review
Forecast Accuracy vs. Safety Stock
Forecast Accuracy vs. the Plant
Conservative Forecast Bias
Effects of Pre-build
Bottom Line
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
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
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
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
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)
Inventory reaches various locations for different
reasons; each reason has a different characteristic.
Pre-Build Stocks
Cycle Stocks
Pipeline Stocks
Safety Stocks
Merchandizing Stocks
Deterministic
Linear
Deterministic
Nonlinear
Stochastic
Linear
Stochastic
Nonlinear Inventory Profile
Manufacturing Lead Time
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:
• Mean Square Error
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
Safety Factor X MSE x Plan Lead Time*
Fcst Duration * or Mfg Lead Time
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
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
“Lag” is the number of time periods between forecast creation period
and forecast target period
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
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
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
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
X X X X X X X X X X X X
Forecast Target Month
Fo
reca
st C
reat
ion
Mo
nth
Actual
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
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
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
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
X X X X X X X X X X X X
Forecast Target Month
Fo
reca
st C
reat
ion
Mo
nth
Actual
Jan Feb Mar Apr May
Fore
cast
Cre
ation
Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4
Feb Lag 0 Lag 1 Lag 2 Lag 3
Mar Lag 0 Lag 1 Lag 2
Actual X X X X X
Inventory commitment occurs continuously throughout the manufacturing process
Raw Material
Inventory Commitment
Cooking / Mixing
Packaging
Jan Feb Mar Apr May
Fore
cast
Cre
ation
Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4
Feb Lag 0 Lag 1 Lag 2 Lag 3
Mar Lag 0 Lag 1 Lag 2
Actual X X X X X
Operations
Packaging
Out-Sourced In-Sourced
Jan Feb Mar Apr May
Fore
cas
t
Cre
atio Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4
Feb Lag 0 Lag 1 Lag 2 Lag 3
Mar Lag 0 Lag 1 Lag 2
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
Raw Material
Inventory Commitment
Cooking / Mixing
Packaging
Jan Feb Mar Apr May
Fore
cas
t
Cre
atio Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4
Feb Lag 0 Lag 1 Lag 2 Lag 3
Mar Lag 0 Lag 1 Lag 2
Operations
Packaging Out-Sourced In-Sourced
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!
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
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.
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…
20
Hist Fcst Fcst Error
Absolute Error
Pct Fcst Error
Abs Pct Fcst Error
Period 1 500 650 (150.00) 150 -30.00% 30.00%
Period 2 650 455 195.00 195 30.00% 30.00%
Period 3 550 715 (165.00) 165 -30.00% 30.00%
Total 1700 1820 (120.00) 510 -7.06% 30.00%
Hist Fcst Fcst Error
Absolute Error
Pct Fcst Error
Abs Pct Fcst Error
Period 1 500 600 (100.00) 100 -20.00% 20.00%
Period 2 520 650 (130.00) 130 -25.00% 25.00%
Period 3 550 605 (55.00) 55 -10.00% 10.00%
Total 1570 1855 (285.00) 285 -18.15% 18.15%
• In this example, a period of over-forecasting is
followed by a period of under forecasting
• In total, the SKU was off by 120 units over
three periods for a Forecast Error of 7.06%
• In this example, the SKU was consistently
over-forecasted every period
• In total, the SKU was off by 285 units over
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
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
Safety Stock
Cycle Stock
Time
Inve
nto
ry
Average Inventory
Ord
er
Qty
Safety Stock
Cycle Stock
Time
Inve
nto
ry Average Inventory
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
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
Jan Feb Mar Apr May June July Aug Sept Nov
Fore
cast
Cre
ation
Jan Lag 0 Lag 1 Lag 2 Lag 3 Lag 4 Lag 5 Lag 6 Lag 7 Lag 8 Lag 9
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
With pre-built inventory the importance of forecasts accuracy extends
much further into the future
Raw Material Inventory Commitment
Cooking / Mixing
Packaging
Operations
Packaging
25
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
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
November 15th
Supply Planning Leadership Exchange:
SAP PP/DS: What You Need to Know
December 5th
S&OP Leadership Exchange:
S&OP KPIs & Metrics Setting a course to achieve ROI
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