Supply-chain optimization (SCO) aims 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. Optimization often involves the application of mathematical modelling techniques using computer software. It is often considered to be part of supply chain engineering, although the latter is mainly focused on mathematical modelling approaches, whereas supply chain optimization can also be undertaken using qualitative, management based approaches.
Applications
Typically, supply-chain managers aim 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, trading off the costs of inventory, transportation, distributing and manufacturing. In addition, optimizing storage and transportation costs by means of product / package size is one of the easiest and most cost effective initial implementations available to save money in product distribution.
Supply-chain optimization has applications in all industries manufacturing and/or distributing goods, including retail, industrial products, and consumer packaged goods (CPG).
Approaches and solutions
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. Universally accepted statistical methods such as Standard Deviation and Mean Absolute Deviation are often used for calculating safety stock levels.
Then, using this forecast demand, a supply-chain manufacturing Production Planning 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?