Lean Six Sigma: SKU / Item Reduction - Rationalization
Author: Mitch Millstein, CFPIM, C.P.M., CQM, CQE
Project Outline To reduce SKUs, this company used multiple sales variables (sales $, unit sales and margin $) to determine which items to discontinue. This project was called a triage project by the CEO of the company because our job was to quickly determine which SKUs to cut and which to keep. The project was meant to provide quick answers on how to return to the historic SKU count. Conducted Sales Pareto Analysis to confirm that low performing SKUs exist (We nicknamed the project team The Biggest SKLUsers Identified what variables define a high performing SKU Calculated an Overall Performance Factor for all items using Multi-Variable Pareto analysis based on Sales $, Unit Sales & Gross Margin $ Sorted the SKUs based on this Overall Performance Factor from best to worst Identified the bottom 25% as poor performing (These items were The Biggest SKLUsers) While our Value Stream Mapping Study showed that SKU count had grown and sales remained flat, we wanted to know the full extent of the problem. We conducted a quick Sales Pareto analysis. The drop-off from the best 25% to the worst 25% was dramatic. The top 25% of SKUs account for 70.6% of sales The bottoms 25% (or 700 items) account for only 1.5% of sales We had SKLUsers (our nickname for low performing SKUs). Then we used Multi-Variable Pareto analysis to identify which items were the SKLUsers based on a mix of measures. Multi-Variable Pareto was used, versus just a straight sales analysis, because of the importance of different sales variables. Mutli Variable Pareto Analysis Sales $ is the most commonly used performance measure. Others argued that Gross Margin $ should be used as this represents cash flow. Still others thought that if an item is a low per unit price, but had high movement this should be considered because volume is a big driver for this business. As we looked at the data, we saw that different SKUs were strong or weak in various measures. For instance, this company loses money on some items to drive sales (loss-leaders). These items had negative margin $s, but drove significant sales and units (and traffic into the store). What defines a great or poorly performing SKU is multi-dimensional, which drove the use of Multi-Variable Pareto analysis. Our Multi-Variable Pareto Analysis created an Overall Performance Factor. We calculated this factor for all SKUs and sorted from highest performing to lowest. Below are the top 10 and bottom ten SKUs. Top Ten Items by Overall Performance Factor SKU # 1, OPF = 7.46, Sales $3,013,442, Units 1,117,009, Margin $313,514 SKU # 2, OPF = 5.61, Sales $1,015,888, Units 953,248, Margin $453,229 SKU # 3, OPF = 5.29, Sales $1,398,854, Units 1,252,197, Margin $288,231 SKU # 4, OPF = 5.02, Sales $2,882,798, Units 8,642,193, Margin -$113,737 SKU # 5, OPF = 4.43, Sales $1,569,511, Units 1,586,938, Margin $81,269 SKU # 6, OPF = 4.36, Sales $1,566,789, Units 585,275, Margin $215,773 SKU # 7, OPF = 4.21, Sales $1,172,974, Units 520.190, Margin $293,701 SKU # 8, OPF = 3.76, Sales $1,165,273, Units 1,790,616, Margin $29,906 SKU #9, OPF = 3.45, Sales $820,379, Units 509,906, Margin $ 253,890 SKU#10, OPF = 3.43, Sales $689,507, Units 817,707, Margin $226,552 *Note: SKU# 4 was a top selling item, but was sold at a loss. This item was a strategic loss-leader for this retail company. Bottom Ten Items by Overall Performance Factor SKU # 2791, OPF = 0, Sales $1.99, Units 1, Margin $0 SKU # 2792, OPF = 0, Sales $1.99, Units 1, Margin $0 SKU # 2793, OPF = 0, Sales $1.89, Units 1, Margin $0 SKU # 2794, OPF = 0, Sales $1.59, Units 1, Margin $0 SKU # 2795, OPF = 0, Sales $1.49, Units 1, Margin $0 SKU # 2796, OPF = 0, Sales $1.49, Units 1, Margin $0 SKU # 2797, OPF = 0, Sales $1.29, Units 1, Margin $0 SKU # 2798, OPF = 0, Sales $0.99, Units 1, Margin $0 SKU # 2799, OPF = 0, Sales $0.69, Units 1, Margin $0 SKU # 2800, OPF = 0, Sales $3.01, Units 3, Margin -$0.94 What was most surprising was not the performance of the top 10 items, but the extremely poor performance of the bottom 10. These SKLUsers sold about 1 unit each over the last 12 months. In stores and the warehouse they were just gathering dust, and eventually would have to be thrown away or deeply discounted. Results Labor costs have gone down by $1.3 million due to eliminating the handling of these low volume, slow moving, items. Inventory will be down $5 million after these slow moving items are dispositioned out of the distribution centers, and not replaced. About the Author:
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Mitch Millstein, CFPIM, C.P.M., CQM, CQE
President
Supply Velocity, Inc.
314-406-4962
mitch@supplyvelocity.comhttp://supplyvelocity.com
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