JPK

Gro

upBusiness Forecasting and Analytics Forum

March 1-2 • San Francisco, CA

Impact Profit and CustomerSatisfaction with Demand UncertaintyAnalyze the underlying drivers of demand uncertainty to help companies

improve demand planning and ultimately supply chain performance

March 2, 9:45am

View presentation online at:

https://jpkgroupsummits.com/attendee1

Rick Blair – One Network EnterprisesInnovative supply chain planning leader with 29 years of experience spanning

cloud-based planning solutions, multiple manufacturing leadership roles, softwareimplementations, S&OP solution design, best practices advocacy and thoughtleadership. Driver of collaboration and continuous improvement across variedorganizational levels and functional groups. Strong foundation in disciplines of

S&OP, IBP, Demand Planning, Supply Planning, Forecasting, StatisticalForecasting, Production Planning, Procurement and Materials Management.

Understanding & Leveraging Demand Variability

Rick Blair

Who am I?

Rick BlairVP, IBP Solution ConsultingOne Network Enterprises

•11 years in supply chain consulting•18 years in operations management

Experience•Process & Solution Design•IBP, S&OP, Demand Planning, Supply Planning•Statistical Forecasting•Operations & Materials Management•Procurement

2

Let’s start with some numbers

In Major League Baseball…

• What is a typical batting average? (number of hits divided by at bats)

• What is a typical fielding %?(number of times a defensive player properly handles

a batted or thrown ball vs total opportunities)

3

25.4%

98.5%

Variability

Unpredictability

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Which Product Would You Prefer to Manage?

Demand Variability:an opportunity to differentiate yourself

from competitors

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Causes of Demand Variance

Consumer behaviorSeasonalityNew Product IntroductionsPromotionsPrice changesMacro-economic factorsInsufficient supply (can’t buy if no stock on shelf)B2B customer behavior

What Should We Do?

Influence demand or adjust to it?…maybe both

Demand Shaping

Demand shaping is the practice of influencing demand to better align with available supply. Typical levers include:• Price reductions (or increases)• Advertising• Product substitution• Product placement, such as end of aisle or near

checkout station

Demand Agility

Techniques for demand-driven flexibility and responsiveness:• Demand sensing• Fast replenishment, shorter lead times• Supply chain network collaboration• Measure and improve forecast accuracy

Demand Sensing

Goal: isolate or predict demand shifts earlier than normal sensors recognize and adjust • Marketing/Big Data: may monitor social media to

pick up on trends that could influence consumer buying

• Point-of-Sale (POS) demand shifts trigger quick re-planning of requirements

Automated Demand Sensing & Replenishment

Supply Chain Network Collaboration

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Multi Party Networks Enable Real-Time Collaboration

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Supply Chain Visibility Upstream & Downstream Minimizes or Eliminates Bullwhip Effect

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Why Measure Forecast Accuracy?

What is Forecast Accuracy?• A measure of deviation between plan and actual• Less deviation = greater accuracy

What motivates an organization to track accuracy?

• Hold individuals and groups accountable

• Benchmark against other companies

• Manage inventory levels

• Improve management of business

How is MAPE Calculated?

Mean Absolute Percent Error = MAPE = ∑│PE│/N• N = number of periods for which we have PE values

• │PE│ = absolute value of the PE (Percent Error)

Weighted MAPE = average of individual MAPEs weighted by actual shipments

= ∑item MAPE * │(item actual / total actual)│

Let’s consider an example…

MAPE Example

1. Error: The difference between Forecast and Actual

2. Absolute Error: Convert negatives to positives

3. Absolute Percent Error = Absolute Error / Actual

Error

SKU 1

Actual 100

Forecast 90

Error (10)

Absolute Error

SKU 1

Actual 100

Forecast 90

Error (10)

Abs Error 10

Absolute Percent Error

SKU 1

Actual 100

Forecast 90

Error (10)

Abs Error 10

APE 10.0%

MAPE Example

Mean Absolute Percent Error (MAPE)– Average of APEs for multiple items or multiple periods or both

MAPE Calculation

SKU 1 2 3 4 5 6 7 8 9 10 Family A

Actual 100 90 8 1,000 1 90 10 11 50 76 1,436

Forecast 90 100 10 970 3 80 13 10 50 80 1,406

Error (10) 10 2 (30) 2 (10) 3 (1) - 4 (30)

Abs Error 10 10 2 30 2 10 3 1 - 4 30

APE 10.0% 11.1% 25.0% 3.0% 200.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.1%

MAPE 30.5%

MAPE = 30.5% = Average of 10 SKU APE values

MAPE for Family A:At Family level = 2.1%At SKU level = 30.5%

MAPE and WMAPE Calculations

SKU 1 2 3 4 5 6 7 8 9 10 Family A

Actual 100 90 8 1,000 1 90 10 11 50 76 1,436

Forecast 90 100 10 970 3 80 13 10 50 80 1,406

Error (10) 10 2 (30) 2 (10) 3 (1) - 4 (30)

Abs Error 10 10 2 30 2 10 3 1 - 4 30

APE 10.0% 11.1% 25.0% 3.0% 200.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.1%

Wtd MAPE 0.7% 0.7% 0.1% 2.1% 0.1% 0.7% 0.2% 0.1% 0.0% 0.3%

MAPE 30.5%

WMAPE 5.0%

WMAPE Example

Weighted Mean Absolute Percent Error (WMAPE or WAPE)– Average of individual MAPEs weighted by actual shipments

WMAPE = 5.0% = Weighted Average of 10 SKU APE values

WMAPE for Family A:At Family level = 2.1%

At SKU level = 5.0%

MAPE and WMAPE Calculations

SKU 1 2 3 4 5 6 7 8 9 10 Family A

Actual 100 90 8 1,000 2 90 10 11 50 76 1,437

Forecast 90 100 10 970 3 80 13 10 50 80 1,406

Error (10) 10 2 (30) 1 (10) 3 (1) - 4 (31)

Abs Error 10 10 2 30 1 10 3 1 - 4 31

APE 10.0% 11.1% 25.0% 3.0% 50.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.2%

Wtd MAPE 0.7% 0.7% 0.1% 2.1% 0.1% 0.7% 0.2% 0.1% 0.0% 0.3%

MAPE 15.5%

WMAPE 4.9%

MAPE & WMAPE: Impact of Small Changes

What if SKU5 Actual was 2 (not 1)?

MAPE changes from 30.5% to 15.5%WMAPE changes from 5.0% to 4.9% A one unit change can have a huge impact on MAPE.

WMAPE is less sensitive.

MAPE and WMAPE Calculations

SKU 1 2 3 4 5 6 7 8 9 10 Family A

Actual 100 90 8 1,000 - 90 10 11 50 76 1,435

Forecast 90 100 10 970 3 80 13 10 50 80 1,406

Error (10) 10 2 (30) 3 (10) 3 (1) - 4 (29)

Abs Error 10 10 2 30 3 10 3 1 - 4 29

APE 10.0% 11.1% 25.0% 3.0% 0.0% 11.1% 30.0% 9.1% 0.0% 5.3% 2.0%

Wtd MAPE 0.7% 0.7% 0.1% 2.1% 0.0% 0.7% 0.2% 0.1% 0.0% 0.3%

MAPE 10.5%

WMAPE 4.9%

MAPE & WMAPE: Zero Actual Values

What if SKU5 Actual was 0 (not 1)?

MAPE changes from 30.5% to 10.5%WMAPE changes from 5.0% to 4.9%

Pay close attention to instances where Actual = 0. Dividing by

zero causes an error, so the MAPE formula may assign 0%.

This can be misleading since 0% should represent zero

deviation between forecast and actual.

Recommendation: Analyze Top 10 WMAPE Items

In Summary: Are You Asking These Questions?

Does demand variance give you a competitive advantage?

What’s your strategy: Shape demand or be agile?

Can you shorten lead times and replenish faster?

Is your S&OP stuck inside your four walls? Do you collaborate

effectively throughout your supply chain, in real-time?

Are you measuring forecast accuracy to pin blame or improve

the company?

Thank You