Author: Mitch Millstein, CFPIM, C.P.M., CQM, CQE
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.
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.