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