Supply chain optimization is a very effective method of designing lean supply chains to save time and money. Progress in technology has focused on speed and scale of large, complete data sets, but not so much on market-driven optimization.

Optimization models focus mostly on a few data: inventory cost and units; capacity of the node to hold that volume (warehouse size, truck/container size); time between each node (this can be transport time, or in a manufacturing setting, processing times). The goal is to find the best choice to meet an objective such as a time goal, while saving money. Savings can come from postponing the purchase of inventory; reducing the number of nodes (or finding cheaper nodes by shutting down warehouses or outsourcing fulfillment); and reducing the time between nodes, if you can afford it (i.e. air vs. ocean). It sounds simple, but it's challenging to design these systems. However, we have had more than twenty years to get it right, and the companies that developed these systems have done a great job for their customers, saving them money. 

But saving money is not the only element of business management. Market-Driven Optimization needs to be taken into account.

What do we mean by Market-Driven Optimization? Rather than focusing on efficiency first, Market-Driven Optimization focuses on the requirements of markets. Markets can be segmented by individual customers, customer segments, channels, and even store clusters. Here we are putting in the full, rich context of product mix and market mix, if you will, to create a total view of the network from the market side. These unique requirements, then, can be modeled and then aggregated to complete the plan.

Now we get to ask some interesting questions:

Supply Side view

Traditionally, optimization is looked at from the manufacturer's or transporter's perspective, and they look at leanness as the very essence of success. But there are some questions they should ask: Are we spending too much for certain markets, overachieving the goal of service levels and therefore, potentially spending too much money on inventory positioning, transport costs, and warehouses? Conversely, could we use a faster but potentially more expensive mode to meet the needs of a special customer? The point is, certain customers are worth the extra effort because we gain more margin from them; whereas, other customers just don't merit the extra expense. The other scenario that can often occur is with the customer who does not plan very well and calls with last-minute orders. We can charge them for these additional expenses, but may not need to incur regular, on-going carrying costs for prepositioning inventory.

Market Side View

Rarely is optimization looked at from the market side, from the point of view of the retailer, for instance. Retailers tend to have a different perspective on inventory: turnover equals sales. Retailers want displays filled, and want shelves rapidly refilled until the moment the season or trend is over. In addition, in luxury goods, which depends on the allure of scarcity, retailers may want to display less, but may have a stockroom with more selection. High-priced items are a challenge, since many brands do not mark down, and a lost sale for just one item can equal quite a lot of money. Luxury merchants, if they train their staff well, can have a better chance at inventory stocking and store allocation, since often, customers are more likely to discuss item preferences and choices (such as, I like that bag or jacket), and needed sizes can be delivered to the store or customer quickly.

A retailer, or a brand company, may be seeking a different objective function. Many increase the number of deliveries to a store to achieve the balance of exclusivity and yet have inventory available. They can be more responsive to success or failure of a line or items. This increases transport and inventory stocking costs per item, but the economics may support it.

Collaborative View

Optimization practice has been very much an internal exercise. However, the above issues could become the foundation for a trading partner dialogue. Manufacturers are making choices about supply chain economics without input from customers and without knowing what their customers are actually willing to pay for. Further, different channel partners look at granular product data and make investment decisions for their markets and categories. These need to be understood and included in the planning to create the optimal supply chain model.

Change the Definition of "Strategic"

If you ask a CEO what she defines as strategic, it is not a time line, it is a focus, and that focus is customers, brand and shareholder value.

In the past, supply chain optimization proponents used the term strategic to define a place in time. The definition of strategic as long-term, and of tactical as short-term is dated from the last century.  This concept was derived from the inability of systems to handle the enormous amount of data that had to be crunched to support decision making. But today's powerful hardware and scalable software can handle extremely large data sets with ease.

Also, in the last century, establishing an infrastructure took many years of planning. In some industries, this is still the case, but even in these cases, strategy is not dealt with just as a long-term issue.

Customer acquisition and the economics of customer service are top issues for CEOs. The studies that show how CEOs spend their time indicate that, on average, CEOs spend 40% of their time on customer issues. Heads of large multinationals spend similar time commitments on market entry and growth in these markets. 

CEOs are market-driven in their focus. Therefore a strategic system should also be market-driven. So, how does your organization define strategic? And do your systems and analytics support this definition?

Recommendations and Conclusions:

1.      Make your optimization focus on unique markets' requirements first.

2.      Optimization should include the profitability of the market or product in order to design optimal supply chains.

3.      Create a collaborative dialogue with key customers to support your market-driven optimization.

4.      Build in assumptions and have a regular and automated method to re-address those assumptions as market conditions change.

Solution providers to look at:

·         ToolsGroup

·         Logility

·         JDA Software

·         4R Systems

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