Despite our egalitarian mindset in the U.S., when it comes to customers, let’s face it: They have never been ‘created equal.’ Certainly for decades, manufacturers and distributors have offered better pricing to some customers than others. We’re all familiar with quantity break pricing, column pricing with different discount levels for different categories of customers, and contract pricing. And who doesn’t visit the local supermarket today and notice the ‘buy 3 get 1 free’ offers to encourage us to increase our purchases?

Volume is valuable and warrants better pricing, we are in the habit of believing. And most often this is true. Not only does a high-volume customer drive our buying power with suppliers by helping us reach the next price break level on the purchasing side, but it can make each sale more profitable: The cost of servicing 10 orders that result in a sale of 100 units can be 10 times as great as the cost of servicing a single order for those 100 units.

This bias towards volume underlies traditional customer ranking methods. But many manufacturers today are taking a closer look at these policies and finding them lacking. Instead, they are engaging in a detailed cost analysis effort called ‘cost-to-serve.’ While cost-to-serve can be a very broad subject covering product costs, location costs, transportation costs and service costs, to name a few, this article will take a look primarily at customer costs.

It’s not that heretofore companies have ignored factors that shade the degree of profitability of a large client. Many firms, presented with the opportunity of doing business with, say, Wal-Mart or the federal government, may question whether it’s really worth doing. They’re thinking about the overhead of handling such a client and the cost of meeting client demands – with slim price margins.

What’s different today is that companies are trying to measure these costs precisely and to make informed, scientific decisions based upon them. Whether they engage consulting firms who have developed methods for tackling this measurement, purchase software to help them out, or devise their own internal approach, more and more manufacturers and wholesalers are gathering detailed costs and trying to apply them to decisions about their customers.

Consumer goods companies, for instance, are recording metrics such as the true cost of customer service. How much support time does this customer require of the customer service organization? How much sales time to we devote to him? Does the customer frequently return merchandise, and if so, what is the cost of processing that return? In the case of consumer goods manufacturers, we might also look at custom-branded merchandise: What is the true cost of providing private labeling for a retailer? Are we really capturing in the product cost all of the special handling required by the purchasing and distribution organizations? All of these costs are very important is assessing a customer’s true profitability.

On the other side of the equation, there may be some sales and marketing benefits that a customer brings, and these, too, should be weighed. Does the name ‘Wal-Mart’ on our client list provide positive benefit to the organization? Is another client who doesn’t seem to purchase very much an outstanding reference for us who sends other potential customers to us? If a business can establish a process and gain agreement across the organization on measuring true costs and benefits, it can define policies to more precisely control bottom-line revenue.
Certainly, one of the first decisions that can be made, once true costs are measured and accepted by an organization, is to eliminate customers who are really unprofitable. But cost-to-serve can also come into play in other ways. We may want to devise strategic programs that nurture our best clients to safeguard their business. We may hold special events for them or assign dedicated reps, for instance.

One of the situations where cost-to-serve becomes a critical tool is in inventory allocation, particularly in an inventory shortage situation. When there is insufficient inventory to meet demand, most manufacturers will want to serve the most valuable customers first.

This frequently comes into play in segments of the technology industry, such as computer peripherals, typically with the launch of a popular new consumer product. An extreme example of this might be the launch of a new Wii game player at the start of the holiday season. Armed with true cost-to-serve data, manufacturers could make allocation decisions scientifically to spread the available inventory across the order pool while maximizing profit.

You might ask whether this process can be automated today. The answer is ‘partially.’ Allocation can certainly be automated, but collecting cost-to-serve data on customers usually involves some manual steps, because most companies don’t have all the systems in place to collect this data automatically (and even with sophisticated systems, the data may not be collected in exactly the way you wish.) Some spreadsheet work may be required. Once the spreadsheet is in place, however, the process becomes straightforward.

Perhaps you want to rank customers sequentially from top to bottom, or group them into ‘profit’ segments. Once that is done, an algorithm can be designed to optimize the allocation of inventory according to the rules tied to those rankings or segments. The allocation algorithm might be designed to work directly from the spreadsheet, as well, automating even more of the process. In any case, executing the service decisions in accord with true costs ensures we are protecting our most valuable customers.

The application of cost-to-serve to inventory allocation takes on an even more interesting aspect for consumer goods manufacturers who ship to retailers. As those of us familiar with this industry are aware, most large retailers have very specific guidelines defining how suppliers must do business with them. The retailers specify how an order must arrive – shipped complete, packed by store, etc.; when it must arrive – ‘arrive by’ date; and a variety of paperwork details including design, content and placement of shipping labels and bills of lading. Associated with each of these requirements is a dollar penalty the supplier will incur, taken as a deduction from the supplier’s invoice, for violation of the guideline.

For a consumer goods manufacturer, these penalties or ‘chargebacks,’ can mean the difference between a profitable client and an unprofitable one. In this situation, the ability to allocate inventory defensively, to minimize chargebacks (or at least make an informed scientific decision to incur them) is critical. A powerful allocation engine, in an inventory shortage situation, can maximize profit by factoring potential chargeback costs for late or partial shipment into the equation. In this case, the allocation engine ensures that the cost to serve the retailer is as low as possible.

In addition to retailer penalties, another aspect of ‘allocation-according-to-true-cost’ involves inventory fulfillment location choices. If a company operates a single distribution center in Los Angeles and imports all its product from Asia, there may be only a single fulfillment option. But for the wide majority of consumer goods manufacturers who import from Asia, service clients nationwide, and operate either multiple distribution centers or a distribution center located in, for instance, the Midwest, there are several options and a variety of questions arise.

If inventory is constrained at the facility that would normally handle a particular customer’s order, should the order be fulfilled from an alternate facility? To make this decision, we need to factor in not only the additional shipping cost but also to weigh that cost against the value of the customer. There may be low profit customers, viewed from the perspective of cost-to-serve, for whom we do not want to make this investment. In the case of a retailer where a potential penalty is involved, the decision might be made dynamically based on a comparison of the chargeback incurred against the additional cost of shipping. If the chargeback fee would be higher than the additional shipping cost, it may be worthwhile to use the alternate distribution center.

This type of on-the-fly fulfillment decision is often called ‘dynamic allocation.’ Another example of dynamic allocation involves intercepting shipments in transit to, say, our hypothetical Midwest distribution center. Least cost fulfillment might dictate fulfilling west coast orders by pulling off inventory required to fulfill them at a deconsolidation facility near the port – before a shipment heads out to the distribution center in the Midwest. Under what conditions is this the least-cost choice? An inventory allocation algorithm based on cost-to-serve can make this decision mathematically, using rules the manufacturer defines.

It’s important to emphasize that the decisions on exactly how to apply cost-to-serve data to inventory allocation will depend on the philosophy of the individual company. For this reason, such allocation solutions are often unique and are adjuncts to the standard capabilities of order management systems. Leading-edge firms who are structuring allocation based on true costs typically do so via point solutions that supplement their central transactional systems.

Profit Point, as the name suggest, provides these point solutions and integrates them into SAP, Oracle, and other order management systems to help clients make the best, most profitable allocation and customer decisions. Our expertise in this area can help clients drive maximal profit to the bottom line.

This article was written by Cindy Engers, a Senior Account Manager at Profit Point.

To learn more about our supply chain data integration and business optimization services, contact us here or call (866) 347-1130.

Labels: Cindy Engers, Distribution, Enterprise Resource Planning, Inventory Management, Optimization, SAP Data Integration, Supply Chain Intelligence, Supply Chain Software, Supply Chain Technology