Measuring the Value of a Responsive Supply Network
Thesis Presentation byJaime GarzaMani Suryanarayanan
AdviserDr. Chris Caplice, Executive Director – Center for Transportation & Logistics, MIT
On20th May 2009
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Agenda
Key Insights on Responsiveness in Supply Chains
Supply Chains and Responsiveness
LargeCo and its Supply Chain
Measuring the Value of Responsiveness in Supply Chains
Findings and Conclusions on Measuring the Value of Responsiveness
Future Research Topics
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What insights did our research work uncover?
Marginal increase in high Fill Rates does not contribute to measurable growth in Sales
3
Performance Improvements
Sales
1 Performance improvements in responsive supply chains and growth in Sales are linked
Sales Inventory
2 Increase in Sales, counterintuitively, drives reduction in overall inventory
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Organizations adopt a supply chain strategy focused on efficiency or responsiveness
Efficient Supply Chain StrategyFocus: Minimize costProducts: Functional, Groceries or Small Variety in SKUs
Responsive Supply Chain StrategyFocus: Increase sales, Minimize lost salesProducts: Innovative, Fashion or Large Variety in SKUs
Sales
Costs
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LargeCo relies on a responsive supply chain
LargeCo manufactures and distributes wide variety of consumer products
LargeCo focuses on high service levels to maximize sales
LargeCo executes initiatives to improve responsiveness of its supply chain
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LargeCo uses five Key Performance Indicators (KPIs) to measure supply chain performance
Fill Rate = % of Demand Fulfilled
Logistics Cost = Procurement and Distribution Transportation Costs
Supply Chain Cycle Time = Procurement + Production + Distribution Cycle Time
Time Between Production Runs = Period between two Production Runs of a Product
Days of Inventory = Days of Sales held as InventoryService
Cost
Efficiency
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Measuring the value of responsive supply network
Does improvement in Responsiveness drive improvement in Sales and can it be measured?
LargeCo believes improved responsiveness is reflected in improvements in KPIs
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Does improvement in KPIs drive improvement in Sales?
Sales
1. Days of Inventory
2. Fill Rate
3. Logistics Cost
4. Supply Chain Cycle Time
5. Time Between Production Runs
Scope: US markets and five product lines
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Two-Stage Analytical Framework
Stage 1: Identify relationships between pairs of data (KPI and Sales) Stage 2: Explain causality of relationships
Analytical Framework
Is there a relationship between KPI
and Sales
How are KPI and Sales
related
1
2
Econometric Model
Causal Model
Correlation Analysis
Regression Analysis
Causal Diagram
Mathematical Formulation
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Preparation for Analysis
Identify and use Instrumental and Dummy variablesInstrumental: Effects from Marketing, PromotionsDummy: Seasonality
Use weekly data at SKU level
Standardize data to magnify effect of small variations
Apply time lag because improvements in some KPIs affect Sales with a lag
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Strength and Direction of Relationship
Summary of Correlation Analysis
Three of five KPIs – Days of Inventory, Supply Chain Cycle Time and Time Between Production Runs, appear to have a relationship (with or without time lag) with Sales
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Probability of Relationship
Summary of Econometric Analysis
Relationship between KPIs and Sales for ProductLine4 needs review
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Correlation and Causality
Econometric Analysis identifies correlation but does not explain causality
Causal Analysis describes causality of relationshipCausal DiagramMathematical Formulation
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Mathematical Formulation
Days of Inventory and SalesDays of Inventory ∝ 1
Sales
Logistics Cost and SalesLogistics Cost ∝ Sales
Fill Rate and SalesSales = Demand * Fill Rate => Sales ∝ Fill Rate
Supply Chain Cycle Time and SalesFill Rate ∝ 1 => Sales ∝ 1
SCCT SCCT
Time Between Production Runs and SalesTBPR ∝ SCCT => Sales ∝ 1
TBPR
KPIs driven by Sales
KPIs that drive Sales
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Findings from Analysis
Summary of Analysis
Econometric and Causal Analyses show two of five KPIs – Days of Inventory and Supply Chain Cycle Time, have a relationship (with or without time lag) with Sales
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Key Conclusions
Days of Inventory
Sales
Fill Rate Logistics Cost Supply Chain Cycle Time
Time Between Production Runs
Lag Lag
Inference from Causal Analysis aloneInference from Econometric Analysis and Causal Analysis
KPIs that drive Sales: Fill Rate, Supply Chain Cycle Time and Time Between Production Runs
KPIs driven by Sales: Days of Inventory and Logistics Costs
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Caveats
Instrumental variables and dummy variables: Addition of instrumental and dummy variables helps draw more meaningful conclusions
Aggregation of data: Aggregated monthly data for product families used in analysis; disaggregated data helps draw better inferences
Volume of data : About 24 to 32 months of data was available and used; additional data influences analysis
Responsiveness and KPIs: Link between initiatives to improve responsiveness and improvements in KPIs is assumed as given
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Findings for LargeCo
Sales drives Days of Inventory: Initiatives to improve Days of Inventory cannot be shown as improving Sales
Fill Rate drives Sales: Fill Rates are high and improvements are marginal; difficult to obtain measurable Sales growth by improving high Fill Rates
Sales drives Logistics Cost: Initiatives to improve Logistics Costs cannot be shown as improving Sales
Supply Chain Cycle Time drives Sales with a lag: Initiatives to reduce Supply Chain Cycle Time will have an effect on Sales but with a time lag
Time Between Production Runs drives Sales with a lag: Data aggregation mutes effect on Sales
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Future Research
Use instrumental and dummy variables to obtain additional inferences
Assess relationship between Sales and KPIs for other product lines
Assess relationships between Sales and KPIs other than those tested
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Questions
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Analytical Framework
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Causal Diagram