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Do SKU and Network Complexity Drive Inventory Levels? JOSEPH MCCORD & DAVID NOVOA GARNICA SCM 2015 Take-aways 1. Greater complexity does not translate into higher inventory levels. 2. Inventory quantities mirror simulated inventory management heuristics rather than traditional optimal inventory models. 3. Two potential measurements of complexity: number of SKUs per brand and number of stocking locations per SKU. Outline Introduction Methodology Results Conclusion The Challenge of Complexity in the CPG Industry • Products are added to categories faster than they are removed • Many production and stocking locations • Wide product variety How does this affect inventory requirements? Project Sponsor: Unilever • Two billion interactions per day • +400 brands in 190 countries Introduction Methodology Results Conclusion Two Specific Forms of Complexity Stock-Keeping Unit (SKU) Complexity • How many SKUs are in each brand? • The more SKUs in a brand, the greater the forecast error for each SKU • How many stocking locations are used for each SKU • The more customer-facing locations for an SKU, the greater the forecast error for that SKU Mechanism Weighted Days of Stock in Brand Network Complexity 14 12 10 8 6 4 2 0 0 • “Square root law” – going from 1 to n locations, IOH increases by sqrt(n) (under several assumptions) Introduction Methodology 100 200 300 SKUs in Brand Results Conclusion 400 Research Question Do increases in SKU or network complexity result in anticipated increases in observed inventory levels? Introduction Methodology Results Conclusion Overall Study Design Ordinary least-squares regression against a power curve Replicated across geographies, product categories, and time periods Weighted Days of Stock in 14 R² = 0.9495 12 10 8 6 4 2 0 0 100 200 300 400 SKUs in Brand Also: developed a simulation exercise to model inventory levels under varying hypothetical inventory management approaches Introduction Methodology Results Conclusion Data Applied Source Content Use “MIO” Inventory Optimization Software Ave daily demand quantities, forecast error per SKU and SKU location Calculate complexity, demand data Inventory Records Inventory quantities per SKU location Calculate inventory days on hand Sales Records Sales quantities per SKU Eliminate obsolete SKUs Introduction Methodology Results Conclusion Analysis Process For each iteration of the analysis: 1. Removed discontinued and obsolete SKUs 2. Linked inventory and demand datasets 3. Generated pivot tables 4. Created scatterplots and calculated regression statistics using spreadsheet software Simulation process: created database with similar characteristics to actual datasets, calculated inventory quantities according to various common inventory models: •Base-stock policy •“ABC” (sales-based inventory control) Introduction Methodology Results Conclusion Data Summaries – Daily Demand Cluster 1: 7,000 •Strong skew toward a daily demand of zero units. 5,000 • 10% of SKUs have a daily demand of 500 units or more, 4,000 3,000 • More than 2000 SKUs have a daily demand of 25 units or less 2,000 1,000 4001 3801 3601 3401 3201 3001 2801 2601 2401 2201 2001 1801 1601 1401 1201 801 1001 601 401 201 - 1 Average Daily Demand (units) 6,000 Other cluster present the same characteristics SKU rank by demand Introduction Methodology Results Conclusion Nodes in the network Cluster 1: Cluster 2: 800 3000 700 2500 2000 500 No. SKUs No. SKUs 600 400 300 1500 1000 200 500 100 0 0 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 11 Nodes in Network Nodes in Network Network complexities differ across clusters. Geographies and markets dictate network design Introduction Methodology Results Conclusion Regression Results – SKU complexity Cluster 1: •Strong skew toward a daily demand of zero units. • 10% of SKUs have a daily demand of 500 units or more, • More than 2000 SKUs have a daily demand of 25 units or less Other cluster present the same characteristics Introduction Methodology Results Conclusion Regression Results – Network complexity Cluster 1: • No correlation • Network complexity does not appear to act as an influencing factor. • Very wide range of inventory levels at each discrete network size Introduction Methodology Results Conclusion Simulation results 14 R² = 0.9495 Weighted Days of Stock in Brand 12 10 y = 3.1149x0.2058 Expected outcomes if inventory levels were in fact managed according to various known inventory control policies: 8 • Inventory managed safety Stock Equation (CSL 95%) 6 • Inventory Managed over Lead Time Using Safety Stock Equation 4 • “ABC” Method 2 0 0 100 200 300 400 SKUs in Brand Introduction Methodology Results Conclusion Simulation results (2) 18 R² = 0.5483 16 - Base level driven by SKU complexity and lead time 14 Weighted Days of Stock in Brand Inventory managed using: 12 y = 5.9533x0.1486 10 - Safety Stock Equation (CSL 95%) 8 6 4 2 0 0 100 200 300 400 SKUs in Brand Introduction Methodology Results Conclusion Simulation results (3) 80 “ABC” Method Weighted Days of Stock in Brand 70 60 • A - Fast movers: low inventory levels (managed) 50 • B – Intermediate movers: higher inventory (managed) 40 • C – Slow movers: high inventory levels 30 20 10 Similar results to the actual results 0 0 100 200 300 400 SKUs in Brand Introduction Methodology Results Conclusion Interpretation Do increases in SKU or network complexity result in anticipated increases in observed inventory levels? Introduction Methodology Results Conclusion Limitations Several factors limit the ability to extend the results of this research: 1. Data analyzed only represents the experiences of one firm within the consumer packaged goods industry. 2. This study assumes independence of demand between products. 3. Other unexplored sources of complexity which could drive inventory levels could be production or sourcing lead time variance, frequency of product mix change, and overall variance of demand within brands. Introduction Methodology Results Conclusion Take-aways 1. Greater complexity does not translate into higher inventory levels. 2. Inventory quantities mirror simulated inventory management heuristics rather than traditional optimal inventory models. 3. Two potential measurements of complexity: number of SKUs per brand and number of stocking locations per SKU. Introduction Methodology Results Conclusion Thank you! Questions?