Supply chain optimization

Supply Chain Optimization is the application of processes and tools to ensure the optimal operation of a manufacturing and distribution supply chain. This includes the optimal placement of inventory within the supply chain, minimizing operating costs (including manufacturing costs, transportation costs, and distribution costs). This often involves the application of mathematical modelling techniques using computer software.

What need is being addressed?

Typically, supply chain managers are trying to maximize the profitable operation of their manufacturing and distribution supply chain. This could include measures like maximizing gross margin return on inventory invested (GMROII)( balancing the cost of inventory at all points in the supply chain with availability to the customer ), minimizing total operating expenses (transportation, inventory and manufacturing), or maximizing gross profit of products distributed through the supply chain. Supply chain optimization addresses the general supply chain problem of delivering products to customers at the lowest total cost and highest profit. This includes trading off the costs of inventory, transportation, distributing and manufacturing.

Supply chain optimization has applications in all industries manufacturing and/or distributing goods, including retail, industrial products, and consumer packaged goods (CPG).

What approaches and solutions exist?

The classic supply chain approach has been to try to forecast future inventory demand as "accurately" as possible, by applying statistical trending and "best fit" techniques based on historic demand and predicted future events. The advantage of this approach is that it can be applied to data aggregated at a fairly high level (e.g. category of merchandise, weekly, by group of customers), requiring modest database sizes and small amounts of manipulation. Unpredictability in demand is then managed by setting safety stock levels, so that for example a distributor might hold two weeks of supply of an article with steady demand but twice that amount for an article where the demand is more erratic.

Then, using this forecast demand, a supply chain manufacturing and distribution plan is created to manufacture and distribute products to meet this forecast demand at lowest cost (or highest profitability). This plan typically addresses the following business concerns:- How much of each product should be manufactured each day?- How much of each product should be made at each manufacturing plant?- Which manufacturing plants should re-stock which warehouses with which products?- What transportation modes should be used for warehouse replenishment and customer deliveries?

The technical ability to record and manipulate larger databases more quickly has now enabled a new breed of supply chain optimization solutions to emerge, which are capable of forecasting at a much more "granular" level (for example, per article per customer per day). Some vendors are applying "best fit" models to this data, to which safety stock rules are applied, while other vendors have started to apply stochastic techniques to the optimization problem. They calculate the most desirable inventory level per article for each individual store for their retail customers, trading off cost of inventory against expectation of sale. The resulting optimized inventory level is known as a model stock. Meeting the model stock level is also an area requiring optimization. Because the movement of product to meet the model stock, called the stock transfer, needs to be in economic shipping units such as complete unit loads or a full truckload, there are a series of decisions that must be made. Many existing distribution requirements planning systems round the quantity up to the nearest full shipping unit. The creation of for example, truckloads as economic shipment units requires optimization systems to ensure that axle constraints and space constraints are met while loading can be achieved in a damage-free way. This is generally achieved by continuing to add time-phased requirements until the loads meet some minimum weight or cube. Optimization solutions are typically part of, or linked to, the company's replenishment systems distribution requirements planning, so that orders can be automatically generated to maintain the model stock profile. The algorithms used are similar to those used in making financial investment decisions; the analogy is quite precise, as inventory can be considered to be an investment in prospective return on sales.

Supply chain optimization may include refinements at various stages of the product lifecycle, so that new, ongoing and obsolete items are optimised in different ways: and adaptations for different classes of products, for example seasonal merchandise.

Whilst a few software vendors are offering supply chain optimization as a packaged solution, others are running the software on behalf of their clients as application service providers.

What are the claims for supply chain optimization?

Firstly, the techniques being applied to supply chain optimization are claimed to be "academically credible". Most of the specialist companies have been created as a result of research projects in academic institutions or consulting firms: and they point to research articles, white papers, academic advisors and industry reviews to support their credibility.

Secondly, the techniques are claimed to be "commercially effective". The companies publish case studies that show how clients have achieved reductions in inventory whilst maintaining or improving availability. There is limited published data outside of these case studies, and a reluctance for some practitioners to publish details of their successes (which may be commercially sensitive), therefore hard evidence is difficult to come by.

Recent Developments

* The trend to provide "software as a service" is a new business model that is now being applied to building and designing optimization solutions. Services are charged as used, rather than through licensing installed or hosted software.

See also

*Demand Optimization
*Forecasting
*Inventory
*Safety stock
*Service level
*Supply chain