Summary Pilot Work Project Zebra

Summary Pilot Work Project Zebra

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  Building Outside-in Processes Supply ChainInsights LLC Copyright © 2022 LORA CECERE, FOUNDER | lora.cecere@supplychaininsights.com
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  Supply Chain InsightsLLC. Copyright © 2022
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  Biggest Impact? RedefiningTime 4 Supply Chain Insights LLC. Copyright © 2022 • Market Latency: The translation of a market signal to a buying pattern to a visible order pattern. • Demand Latency: The time to translate channel purchase data pattern to an order through replenishment processes. • Process Latency: The time for the organization to make a decision. Market Latency: 3-6 months Demand Latency: 2- 12 weeks Process Latency: 2- 6 weeks
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  Potential for NineNew Advanced Planning Applications 5 Supply Chain Insights LLC. Copyright © 2022 1. Market-driven Demand Management 2. Demand Visibility 3. Procurement Buyer Workbench 4. Planning Master Data 5. Unified Planning Data Model 6. Form and Function of Inventory 7. Revenue Management Effectiveness (Test & Learn): Demand Shifting Versus Shaping/ Balanced Scorecard Impact 8. Bi-directional Orchestration 9. S&OP Playbook Execution
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  Bi-directional Orchestration Orchestration Channel Sensing SensingSupply Sensing Variability: Cycles, conversions, and grades Growth Customer Service Margin Inventory Turns Safety Return on Invested Capital Balanced Scorecard Listening Post Pattern Recognition of Channel Data Interest Social Graph Mining Contract Manufacturing Alternate Sourcing Alternate Bill of Materials Bi-Directional Orchestration Architect/Design Planning Master Data Baseline Demand Plan Feasibility Events Risk Sensing Demand Shaping Portfolio Shifts Quality Sensing Logistics Sensing Leadtime Variability Commodity Price Shifts Platform Changes Social Sentiment Weather Conversion Rates Yield Maintenance Schedules Reverse Bill of Materials Alternate Routing Factory Reliability/Capabilities Alternate Distribution Alternate Channel Images Market Shifts Weather Pattern Recognition Images Postponement Calendar(s) Replenishment Policies Allocation Unified Data Model
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  BSH Case Study
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  BSH Latency Distortion Market Latency Demand Latency Process Latency 49%7 months 2 weeks 4 weeks
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  •About LKQ LKQ Corporationis an eCommerce provider of alternative and specialty parts to repair and accessorize automobiles and other vehicles. The Company is global, with operations in North America, Europe, and Taiwan. LKQ offers its customers a broad range of Original Equipment (OE) recycled and aftermarket parts, replacement systems, components, equipment, and services to repair and accessorize automobiles, trucks, and recreational and performance vehicles. Currently, the Company has 44,000 Employees and 1,600 Locations in 31 Countries.
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  The Problem Orchestration Channel Sensing SensingSupply Sensing Variability: Cycles, conversions, and grades Growth Customer Service Margin Inventory Turns Safety Return on Invested Capital Balanced Scorecard Listening Post Pattern Recognition of Channel Data Interest Social Graph Mining Contract Manufacturing Alternate Sourcing Alternate Bill of Materials Unified Data Model Planning Master Data Baseline Demand Plan Feasibility Events Risk Sensing Demand Shaping Portfolio Shifts Quality Sensing Logistics Sensing Leadtime Variability Commodity Price Shifts Platform Changes Social Sentiment Weather Conversion Rates Yield Maintenance Schedules Reverse Bill of Materials Alternate Routing Factory Reliability/Capabilitie s Alternate Distribution Alternate Channel Images Market Shifts Weather Pattern Recognition Images Postponement Calendar(s) Replenishment Policies Allocation The goal was to build an “inventory buy plan” in each S&OP cycle with a playbook based on inflation, supply-side variation and risk, and changes in demand.
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  Network Overview Number ofSKUs: 19 The distribution Network scope consists of  1 CDC  150+ Branches and covers the Austrian, German and Slovakian market. Locations are grouped under:  Garage, E-Commerce, Wholesale  Garage, E-Commerce  Garage, Wholesale, E-Commerce, Key Account  Garage  Garage, Wholesale Supplier Central Warehouse Varying LT LT= 0 days Branch1 Branch2 Branch3 SS SS Demand Demand Demand Buy Plan SS SS
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  FVA Analysis Forecast ValueAdd (FVA) analysis by comparing the accuracy against the shipment (at CDC level for all items) data. The analysis includes the forecast generated by LKQ, o9 Stat model and o9 machine learning model by comparing against the naive method (moving average of 5 months) as base forecast. The period of validation is between M01-2022 to M05-2022
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  Insights ▪ LKQ branchand CDC replenishment were out of sync. Neither was right. The branch demand plan is structurally too low, whereas the CDC forecast is too high. ▪ The COV at a branch level was too high to forecast. ▪ Before the pilot, the organization was unaware of the issues. ▪ Thirteen market factors tested. Four were selected—COVID levels, GDP, car registrations, and inflation.
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  Leadtime Variability
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  LKQ Demand Insights LKQO9 Statistical Enterprise o9 Market Driven Inputs Shipments Sales Invoices Sales Invoices + Market Signals Demand Latency Unknown Unknown ~ 6 weeks Forecast Value Added (FVA) 8% 9.9% 17.3% Forecast Bias Positive Negative / Balanced Balanced Bullwhip Amplification 48% 52% 37% COV Analysis Insights: ● Item/Branch locations are not forecastable ● o9 enterprise forecast slightly improves FVA ● Market driven forecast significantly improves FVA
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  Scenario overview SCOPE: Modelingof 2022 Data | Jan thru’ Jun | Weekly Buckets Data inputs 1. Enterprise Sync LKQ Forecast 2. Enterprise Sync o9 Statistic Model 3. Market Signal Sensing Market Driven 4 Market Signal Sensing & Supply lead time o9 Machine Learning 5 Bi-directional: Market Driven Demand & Supply lead time & price Forecast LKQ Stat Fcst sales history o9 Stat Fcst sales history o9 Order + Market Signals o9 ML Fcst (Order Invoices + Market Signals) o9 ML Fcst (Order Invoices + Market Signals) Supply Lead Time Lead Time Purchase Price Safety Stock calculation Using Actual lead time from 2021 and variability Assumptions on improved supplier reliability Assumptions on improved supplier reliability Purchase price Increase by 30% from Feb
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  Scenario Comparison Scenario 1. LKQ Forecast 2.o9 Statistic Model 3. o9 Market-driven 4. o9 Market-Driven (MD) + Stable Lead Times (LT) 5. o9 ML + Stable LT + Buy ahead Demand Quantity Baseline (-40%) (-21%) (-21%) (21%) Gross Revenue (€) (-38%) (-20%) (-20%) (-20%) Gross Margin% 17% (-3%) 5% 47% COGS (€) (-39%) (-19%) (-20%) (-24%) COGS % (-2%) 0.3% (-1%) (-6%) Inventory Holding cost (€) (-35%) (-21%) (-48%) (-7%) Turns (-3%) 6% 61% 61%
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  © 2022 CorningIncorporated OPTICAL COMMUNICATIONS CORNING’S LARGEST SEGMENT • Over 25,000 Employees • Over $4 billion in Revenue • Key Segments: Fiber to the home, Wireless technologies, Hyper Scale Data Centers
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  Corning Demand Insights Corningo9 Statistical Enterprise o9 Market Driven Inputs Customer Orders Customer Orders Customer orders + Market Signals Market Driver Lags Unknown Unknown ~ 3-4 Months Forecast Value Added (FVA) -4.16% 0.55% 5.43% Forecast Bias Negative Negative / Balanced Balanced Pilot Insights: ● Outside-In forecasts consistently outperform inside-out statistical forecasting approaches ● Correlation does not equal causality – but can be helpful in the model (Temperature) ● COV analysis confirmed forecastability at the product family level
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  Corning - SummaryObservations • The division has a negative FVA (~4.2%). The FVA technique is new for the group. • ML Drivers that showed strong correlation and ability to enhance FVA by ~+10% • Selling Price • Temperature • GDP • Covid • The organization looks at price management and demand in disconnected processes. • There is a need to enhance insights on pricing communication lead times, and effective dates • Inflation Rate • Housing Starts • 5G Subscriptions • 5G Sites
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  Supply Chain InsightsLLC. Copyright © 2022 Founded in February 2012 by Lora Cecere, Supply Chain Insights LLC is in its eighth year of operation. The Company's mission is to deliver independent, actionable, and objective advice for supply chain leaders. Our goal is to help leaders understand supply chain trends, evolving technologies, and which metrics matter. 21