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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.
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Understanding & Leveraging Demand Variability
Rick Blair
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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
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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%
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Variability
Unpredictability
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Which Product Would You Prefer to Manage?
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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
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What Should We Do?
Influence demand or adjust to it?…maybe both
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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
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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
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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
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Automated Demand Sensing & Replenishment
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Supply Chain Network Collaboration
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Multi Party Networks Enable Real-Time Collaboration
Legacy
Legacy
Legacy
Legacy
Legacy
Legacy
Legacy
Legacy ERP / EDI Single Multi-
Tenant Instance
Cust 1
Cust 2
Cust 3
Cust 3
Cust 3
Hub
Hub
Hub
Hub
Hub
NETWORK PLATFORM
Single Multi-Tenant Multi Party Instance
Cust1
Cust 2
Cust 3
Cust 4
Cust 5
Legacy
Legacy
Legacy
Legacy
Legacy
Legacy
Legacy
Legacy
Service Center
Legacy
Supplier
Supplier
Legacy
Partner
Service Center
3PL / Carrier
Prime Vendors
Allies
Legacy Legacy
Legacy
3PL / Carrier
3rd Party Service
Suppliers
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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
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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…
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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%
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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%
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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%
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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.
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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.
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Recommendation: Analyze Top 10 WMAPE Items
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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?
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Thank You