The art of SKU rationalization: Getting accurate costing data

Many companies are all too willing to create new products at a client’s request. Over time, unchecked SKU proliferation can lead to more business complexity through smaller production runs, more setups in the manufacturing process, and supplementary administration. This added complexity increases costs and lowers overall profitability. If this sounds familiar, it may be time to look at SKU rationalization in your business. 

At its most basic meaning, SKU rationalization is the inventory management process of deciding which products to keep (or improve) and which ones to discontinue — all with the goal of streamlining your product offerings and improving your organization’s bottom line. The key drivers are typically space prioritization, innovation time, and price optimization with an overall business goal to improve cash flow, increase return on invested capital, and boost gross margins.

So, where do you start? The key to successful SKU rationalization is to begin with accurate product cost information and sales data. All too often, SKU rationalization is suboptimal or ultimately fails because critical decisions are based on poor product costing data — the “garbage-in, garbage-out” effect. To be successful, you need detailed margin data for each product offering that’s based on correct and accurate item master data and integrated within multichannel analytics. 

All too often, SKU rationalization is suboptimal or ultimately fails because critical decisions are based on poor product costing data — the “garbage-in, garbage-out” effect.

Here are five key costing data items to consider when planning your SKU rationalization initiative.

1. Confirm bill of material and process router accuracy

Bills of material and process routers are the drivers of cost in a manufacturing environment, and inaccuracy can impact staffing, decision-making, quoting and inventory valuation. For staffing, because of poor data in the ERP system, they are often forced to use shadow IT, or spreadsheets to schedule the plant, purchase materials, and perform capacity utilization analysis. This could cause you to buy equipment when not needed or buy too much inventory, or hire more staff than needed to manage the shadow IT. When considering SKU rationalization, we’ve seen poor assumptions for scrap, yield, offal, material costs, inbound freight and tariffs have a direct impact on material cost assumptions. High volume parts often subsidize the OEE impact of changeovers on low volume parts when no change-over time is considered. Inaccurate cycle times, pieces per cycle and crewing can lead to grossly over- or under-stating SKU cost and margin. Altogether, inaccuracy here can cause increased cost and lead to poor decisions on SKU rationalization.

2. Determine labor cost

Labor cost is another big piece of the SKU cost puzzle. It’s often the largest variable component of cost, and due to the wide range of methods used to calculate it, there’s a lot that can go wrong when factoring it into overall product cost. Is your labor cost a blended cost for the plant? If so, what if a particular operation requires a specialized skill with a higher wage? Are your labor fringe costs accounted for as labor or included in overhead? How productive is the labor force, and is productivity being driven by product mix or other factors? Is overtime driven by high demand or customer order patterns? These are all key questions that can significantly swing your labor cost for a SKU. 

3. Assign overhead costs

How you assign overhead costs to SKUs is critical. Most overhead expenses can be assigned by causality or correlation, and ideally directly assigned to a SKU or process. Is everything driven by labor hours? Are machine-driven costs applied separately from labor-driven expenses? Are SKU proliferation-related costs, i.e., the supply chain, inventory storage costs, set-up time, and related costs of increasing your product catalog based on changes in customer, channel, or industry being “peanut butter spread” across the entire factory? Assigning these costs based on appropriate drivers results in far more accurate SKU cost and margin visibility. 

4. Measure variable contribution and total gross margins

It’s important to understand your variable costs versus fixed costs. For example, if you eliminate a SKU or channel without changing your fixed costs, it could appear that you’re saving more cost than you actually are. Look at an SKU from a variable-contribution-margin basis, and then look at the fixed support, and equipment and facility costs and determine underperforming products that can be eliminated. In some cases, you may free up capacity, while the cost doesn’t go away; however, those resources can be redirected to other products/projects. An SKU with a negative gross margin but positive contribution margin may be a keeper in the short term, but only until it can be replaced by a more profitable product.

5. Analyze cost to serve

Many costs aren’t driven by the SKU at all but rather by the cost of supporting the customer and the channel. So, whether you’re selling to automotive OEMs, hospitals, big-box or grocery stores, or directly to consumers, each channel has different layers of channel management cost to support. The same holds true for individual customers — each is unique and will impact costs differently based on ordering windows, turnaround time, and delivery costs. Are your channel and customer management costs simply accounted for as a general selling and administrative expenses? Or, do you separate channel and customer management costs and consider them when determining the profitability of an individual SKU or overall customer relationship? For individual customers — each is unique and will impact costs differently based on ordering windows, turnaround time, support and delivery costs.

When done right, SKU rationalization has the potential to increase profit margin significantly. Once you’ve gotten good data, you can leverage your data analytics tools to reap the benefits of the cost information.

SKU profitability data needs to be analyzed not solely on cost, but also on what’s best for the market. 

Cost margin intelligence and data analytics will help you understand SKU, channel, and customer margin quickly in the context of market-based scenarios. This will free up time to help you identify where to reprice, eliminate products, or invest further in product, channel, or customer development. But to reap the full benefits of your rationalization, SKU profitability data needs to be analyzed not solely on pure cost, but also on what’s best within the context of the market. For example, it may make sense to keep a money-losing or low-margin product for strategic reasons such as filling out a portfolio of products, securing a sales foothold with a prospective customer, selling capacity to maintain contribution margin, or testing product potential. In these situations, utilizing good data produced in the rationalization process is very useful for making informed decisions and ongoing monitoring of SKU performance and evaluating the ongoing impact on profits.

For further insights into your SKU, product, customer, or channel rationalization, give us a call.