Measuring the Value of a Responsive Supply Network

Transcription

1 Measuring the Value of a Responsive Supply Network Thesis Presentation by Jaime Garza Mani Suryanarayanan Adviser On Dr. Chris Caplice, Executive Director Center for Transportation & Logistics, MIT 20 th May 2009

2 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 6/10/2009 Jaime & Mani 2

3 What insights did our research work uncover? 1 Performance improvements in Performance Improvements responsive supply chains and growth in Sales are linked Sales 2 Increase in Sales, counterintuitively, drives reduction in overall inventory Sales Inventory 3 Marginal increase in high Fill Rates does not contribute to measurable growth in Sales 6/10/2009 Jaime & Mani 3

4 Organizations adopt a supply chain strategy focused on efficiency or responsiveness Efficient Supply Chain Strategy Focus: Minimize cost Products: Functional, Groceries or Small Variety in SKUs Costs Responsive Supply Chain Strategy Focus: Increase sales, Minimize lost sales Products: Innovative, Fashion or Large Variety in SKUs Sales 6/10/2009 Jaime & Mani 4

5 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 6/10/2009 Jaime & Mani 5

6 LargeCo uses five Key Performance Indicators (KPIs) to measure supply chain performance Days of Inventory = Days of Sales held as Inventory Service Fill Rate = % of Demand Fulfilled Logistics Cost = Procurement and Distribution Transportation Costs Cost Supply Chain Cycle Time = Procurement + Production + Distribution Cycle Time Time Between Production Runs = Period between two Production Runs of a Product Efficiency 6/10/2009 Jaime & Mani 6

7 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 6/10/2009 Jaime & Mani 7

8 Does improvement in KPIs drive improvement in Sales? 1. Days of Inventory 2. Fill Rate 3. Logistics Cost 4. Supply Chain Cycle Time 5. Time Between Production Runs Sales Scope: US markets and five product lines 6/10/2009 Jaime & Mani 8

9 Two-Stage Analytical Framework Stage 1: Identify relationships between pairs of data (KPI and Sales) Stage 2: Explain causality of relationships Analytical Framework 1 Is there a relationship between KPI and Sales Econometric Model Correlation Analysis Regression Analysis 2 How are KPI and Sales related Causal Model Causal Diagram Mathematical Formulation 6/10/2009 Jaime & Mani 9

10 Preparation for Analysis Identify and use Instrumental and Dummy variables Instrumental: Effects from Marketing, Promotions Dummy: 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 6/10/2009 Jaime & Mani 10

11 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 6/10/2009 Jaime & Mani 11

12 Probability of Relationship Summary of Econometric Analysis Relationship between KPIs and Sales for ProductLine4 needs review 6/10/2009 Jaime & Mani 12

13 Correlation and Causality Econometric Analysis identifies correlation but does not explain causality Causal Analysis describes causality of relationship Causal Diagram Mathematical Formulation 6/10/2009 Jaime & Mani 13

14 Mathematical Formulation Days of Inventory and Sales KPIs driven by Sales Logistics Cost and Sales Days of Inventory 1 Sales Logistics Cost Sales Fill Rate and Sales Sales = Demand * Fill Rate => Sales Fill Rate KPIs that drive Sales Supply Chain Cycle Time and Sales Fill Rate 1 => Sales 1 SCCT SCCT Time Between Production Runs and Sales TBPR SCCT => Sales 1 TBPR 6/10/2009 Jaime & Mani 14

15 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 6/10/2009 Jaime & Mani 15

16 Key Conclusions Sales Lag Lag Days of Inventory Fill Rate Logistics Cost Supply Chain Cycle Time Time Between Production Runs Inference from Causal Analysis alone Inference 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 6/10/2009 Jaime & Mani 16

17 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 6/10/2009 Jaime & Mani 17

18 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 6/10/2009 Jaime & Mani 18

19 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 6/10/2009 Jaime & Mani 19

20 Questions 6/10/2009 Jaime & Mani 20

21 Analytical Framework 6/10/2009 Jaime & Mani 21

22 Causal Diagram 6/10/2009 Jaime & Mani 22