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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