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Trends, Myths and Reality in Supply Chain
Joe ShamirCEO ToolsGroup
September 2017
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Global VisionAccelerating Business PerformanceBy efficiently delivering service to the customers of our customers
Leveraging Market Driven Demand Analytics and Supply Chain Optimization
Global E2E Approach
EUROPE AmsterdamMilanMunichBarcelonaLondonStockholm
AMERICABostonOntarioMexico D.F.
ASIAMalaysiaIndiaJapan
MIDDLE EASTIsrael
AFRICACape Town
Your B4B PartnerDelivering Measurable Outcomes 4 Customers
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Agenda
Trends
1
1Increasing uncertainty. New challenges of SC outcomes2Functional approach to probabilistic SC planning and optimization3Questions and discussion4
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1. Trends
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Platform as a Service
Infrastructure as a Service
Outcome as a ServiceBusiness Process Outsourcing - Monitoring and Control – Reporting - KPI’s
Software as a ServiceFull Automation - Supply Chain Planning Solution - Machine learning - Advanced Algorithms - - Complex Networks - Long Tail
Your B4B Partner
Outcome as a Service
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Example of technological evolution to BPO: Communication
Communicationas a Service
Help Desk BPO: Outcome as a
Service
Automated internalswitch board
“In the basement”
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Global cloud traffic worldwide
In Exabyte
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Forecast of Big Data market size
Based on revenue (billion U.S. dollars)
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Competitive public cloud Platform as a Service (PaaS) revenue forecast
Worldwide data. From 2013 to 2019 (in million U.S. dollars)
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Growth forecast of information technology support services spending
Worldwide data. From 2016 to 2020
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Platform as a Service
Infrastructure as a Service
Outcomes as a ServiceBusiness Process Outsourcing - Monitoring and Control – Reporting - KPI’s
Software as a ServiceFull Automation - Supply Chain Planning Solution - Machine learning - Advanced Algorithms - - Complex Networks - Long Tail
Your B4B Partner
Outcomes as a Service
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2. IncreasinguncertaintyNew challenges of Supply Chain outcomes
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After Walmart introduced in the 90’s Low-cost “Service-level”Amazon introduced Low-cost Customer “Service-Time”
Amazon using economy of scale logistic efficiency to drive e-commerce customer service improvements through faster delivery
Remember when two day shipping was considered fast (and was expensive)?
Prime Now Service- customers in major cities can get up one hour same day delivery service and two hours free
23 minute candy delivery in Manhattan
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Increased Activity in the Market
• Volume and number of transactions have risen
• Driven by rise of Online Brokerages in 1990s, together with surge in HFT (High Frequency Trading) in 2000s
• Hyper reactivity to news and signals affects stock prices more dramatically than in the past
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Stock Market Volatility: The time-period makes the difference
• Intermediate and long-run volatility have not increased• Short-term volatility has risen
Deviation of Daily ReturnsDeviation of Annual Monthly Returns
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Things have Changed – The “Long Tail” is Here
Drivers
• Expanding SKU proliferation to meet customer needs
• Closer to the consumer• More responsive – high granularity
replenishment cycles (time buckets smaller)
• Forecasting at SKU-Location, not product family
Shrinking
Sku Count
Sale
s Vo
lum
es
Growing
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Why Automation and Outcome as a service is becoming important
• Increasing uncertainty • Increasing control frequency• Product-Line extensions• Lack of talent in SCP
Increasing decision making by an order of magnitude
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Why management wants a better forecast when Forecast accuracy is bound to decrease in time?
• On average by improving short term forecast error by 30% one can reduce inventory by 5%
• On average by applying inventory optimization one can reduce inventory by 20%
…In making decisions under uncertainty people replace the complex task of assessing probabilities with limited and simplified heuristic principles that can lead to severe systematic errors.
Judgement under Uncertainty: Heuristics and Biases (Science, 1974)Amos tversky and Daniel Kahneman.
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“Illusion of Validity”: Over-fitting
“Judgment Under Uncertainty” (Kahnemann and Tversky)
The peaks of August and October are random fluctuations or a seasonal variation.
Over-fitting provides “high quality of the match between selected outcome and the input” ; an illusion of consistency
Unfortunately, the accuracy of the March 2014 forecast for the rest of the year is poor as random fluctuations dominate the demand.
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Availability Heuristics: Anchoring
“Judgment Under Uncertainty” (Kahnemann and Tversky)
Manual forecast in the presence of the budget and Statistical Forecast
Manual forecast in the presence of the budget
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Availability Heuristics: Forecast Vs. Expectation
“Judgment Under Uncertainty” (Kahnemann and Tversky)
In “Tail” items with intermitted demand, the most probable value is zero! But the forecast is never zero. With zero forecast one keeps zero stock and provides no service.
When demand is stable or “Normal”, the expected value and most probable value are similar.
When demand is lumpier with skewed distribution, the mode is at the left of the expectation.
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-6
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Representativeness Heuristics: “misconception of chance”
Representing a case of 72% service level with classical saw tooth, measurement during short periods of time can provide either 100% or zero service level with “hysteric” consequences.
“Judgment Under Uncertainty” (Kahnemann and Tversky)
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3. Functional approach to probabilistic SC planning and optimization
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Advanced Modeling of Demand reduces its Volatility
Statistical forecast
Trend, Seasonality, calendarsand daily sale patterns
Market Intelligence
3
6
7
Media event effect
Web indicators / sentiment
4
Special Events
5
New Product Introduction
THE DAILY BASELINE
DEM
AND
INSI
GHT
INCR
EASI
NG
1
2
STOCHASTIC MODELING
DEMAND SHAPING
MACHINE LEARNING
PLANNER
Trade Promotion
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Automation layer
Business Trade-off layer
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4. Myths and reality
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Recent Myths in Supply Chain
• Machine Learning can model and drive the high compelexityof SC if provide with all relevant input parameters
• One can simplify the complex supply chain and manage it with few “lean-principles” (DDMRP)