Do SKU and Network Complexity Drive Inventory Levels? Good morning, thanks for coming, name is, over next few minutes we’ll share our thesis research on the question of whether Joseph mccord & David novoa Garnica Scm 2015

Take-aways Greater complexity does not translate into higher inventory levels. Inventory quantities mirror simulated inventory management heuristics rather than traditional optimal inventory models. Two potential measurements of complexity: number of SKUs per brand and number of stocking locations per SKU. In case your coffee hasn’t kicked in yet and you only catch the next 30 seconds of our presentation, we wanted to bookend our presentation with our key take aways. These are the very high level conclusions which we’ll explain more in depth through this presentation, and they are the following:

Outline Introduction Methodology Results Conclusion Using an outline that will become

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 Network Complexity 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 “Square root law” – going from 1 to n locations, IOH increases by sqrt(n) (under several assumptions) Introduction Methodology Results Conclusion

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 Also: developed a simulation exercise to model inventory levels under varying hypothetical inventory management approaches This assumes in each case that the complexity factor is the sole source of forecast error. Obviously it isn’t, but this approach allows us to see what the level of correlation is In several cases, where possible, we averaged inventory levels across several near points in time to get something closer to average stock levels 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: Removed discontinued and obsolete SKUs Linked inventory and demand datasets Generated pivot tables 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: 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 10 % of SKUs represent the majority of sales Introduction Methodology Results Conclusion

Nodes in the network Cluster 1: Cluster 2: Different markets 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 No correlation and similar across clusters 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 Expected outcomes if inventory levels were in fact managed according to various known inventory control policies: Inventory managed safety Stock Equation (CSL 95%) Inventory Managed over Lead Time Using Safety Stock Equation “ABC” Method Graph: avg expected inventory + sqrt (sku complexity) Introduction Methodology Results Conclusion

Simulation results (2) Inventory managed using: - Base level driven by SKU complexity and lead time - Safety Stock Equation (CSL 95%) Graph: base(avg expected + lead time)+ sqrt (sku complexity) Introduction Methodology Results Conclusion

Simulation results (3) “ABC” Method A - Fast movers: low inventory levels (managed) B – Intermediate movers: higher inventory (managed) C – Slow movers: high inventory levels Similar results to the actual results Categirize sku depending on their average demand ignoring lead time and variability Introduction Methodology Results Conclusion

Interpretation Do increases in SKU or network complexity result in anticipated increases in observed inventory levels? Answer NO Left: real Middle: expected taking into account variability Right: similar to what we had ABC method – local planners did not always relied in methods driven by variability Introduction Methodology Results Conclusion

Limitations Several factors limit the ability to extend the results of this research: Data analyzed only represents the experiences of one firm within the consumer packaged goods industry. This study assumes independence of demand between products. 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. Other firms in the same industry or firms in other industries may operate more centralized inventory control methods which would affect the relationship between complexity and inventory levels in more direct ways. All products have same average demand Introduction Methodology Results Conclusion

Take-aways Greater complexity does not translate into higher inventory levels. Inventory quantities mirror simulated inventory management heuristics rather than traditional optimal inventory models. Two potential measurements of complexity: number of SKUs per brand and number of stocking locations per SKU. Introduction Methodology Results Conclusion

Thank you! Questions?