Key aspects of inventory management in manufacturing operations

Inventory management is an ongoing process (as opposed to a project which has a beginning and an end) of monitoring the constant flow of stock keeping units (SKUs) into and out of supply. The goal is to prevent the inventory from becoming too high (cost), or so low that the operations of the company are in jeopardy (service levels.) (Barcodes, 2011)

Elemental management of inventory requires balancing the three key aspects of stock control: lead time, buffer stock and record keeping.

Lead Time

An inventory manager needs to know how long it takes for a vendor to process an order and make a delivery of goods. he/she also needs to understand how long it will take for those materials to transfer out of inventory in the normal course of business flow. With these two time frames determined, the manager can calculate when to place an order and how many units to order to keep production running smoothly. (Barcodes, 2011)

Buffer Stock

Buffer stock is the maintenance of a few additional units above the minimum number required for normal production flow. This cushion serves to minimize the probability of interrupted production due to a lack of essential parts at various stages of production. Maintaining buffer stock for raw materials, work in progress, and finished goods inventory helps keep the production lines flowing. (Barcodes, 2011)

Record Keeping

A well-run manufacturing enterprise will keep accurate records of inventory at all stages of production for control and tax reporting purposes. From an operational standpoint, the most important of these is the accurate maintenance of the finished goods inventory which facilitates informing sales personnel of available merchandise ready to ship to customers. On a basic level, this requires posting the production of newly completed goods to the inventory totals as well as subtracting the most recent shipments of finished goods to buyers. From these basic concepts, the inventory management craft has developed into a highly complex process. (Barcodes, 2011)

Demand Planning

Other analysts take an opposite view believing that inventory optimization can only advance the ball so far and propose that further gains in inventory management will require a comprehensive demand planning strategy. In that regard, the latest thinking is shifting away from planning based on historical sales and shipment data and toward real-time analysis and visibility which is called "demand sensing." (Essex, 2009)

The latest trend is to capture on-going demand at the point-of-sale. Companies that have tried to sense changing levels of demand using order or shipment data have realized superior results by examining POS data on a frequent basis, most often weekly. Other software vendors have concentrated on introducing demand planning modules for analyzing special situations, such as product promotions and new product launches. (Essex, 2009)

Essex's research leads him to consider that the next step is to bring everything under the purview of a sales and operations planning (S&OP) framework. S&OP extends the reach of demand planning and analysis to include inventory as well as the supply chain personnel that handle it. But basic S&OP ought to be the first step, experts say, because the technology won't work well unless underlying business processes are brought under control. "A lot of the benefit that an S&OP process provides is having some kind of discipline in your work process. That discipline needs to exist before you put in a software product that's designed to optimize it." (Essex, 2009)

S&OP is nothing new; it was in vogue in the late 1970s. "All these things that are old are becoming new again." Another "old friend" that is making the rounds is vendor managed inventory (VMI.) In the VMI process, the vendor assumes responsibility for managing the replenishment of stock as needed rather than requiring the customer to submit orders. The VMI renaissance is being aided by new software products that provide real-time inventory visibility and management across stores, data centers and plants and include what-if scenarios and risk analysis. (Essex, 2009)

"Everyone's moving to more of an executive S&OP process and business intelligence." Companies are increasingly feeding S&OP data into business-intelligence software from the likes of SAS and IBM's Cognos, analysts say. Modeling and simulation are also becoming more common, as is the use of business analytics and real-time event management in S&OP. (Essex, 2009)

Demand planning is also being driven forward by broader movements in IT and supply chain technology. Cloud computing seems poised to light a fire to the fuse of an ongoing trend of demand planning software-as-a-service. Demand planning software is also being offered in modular form, allowing companies to roll out S&OP gradually on a department-by-department basis. Finally, radio frequency identification (RFID) technology is likely to become an increasing source of demand data by automatically tracking products through every stage of manufacturing and distribution. Essex wonders whether these innovations "could lead to the day when video ads in storefront windows will change to suit the interests of passersby." (Essex, 2009)

Inventory Optimization

Eugenio Cornacchia defines inventory optimization as "an emerging practical approach to balancing investment and service-level goals over a very large assortment of stock-keeping units (SKUs)... In contrast to traditional one-at-a-time marginal stock level setting, inventory optimization simultaneously determines all SKU stock levels to fulfill total service and investment constraints or objectives." (Cornacchia, 2004)

Inventory optimization requires the simultaneous solution of two different problems.

The first problem focuses on the need to find the optimal balance among the various SKUs stocked at a single location at a single point in time ("optimal mix." ) The second problem is concerned with the optimal allocation of inventories along the links of the supply chain ("optimal staging." ) The intricate interaction between these two problems makes the task of finding a global solution a formidable effort. "Determining the right amount of inventory to hold in which locations -- without excesses or shortages - has been an intractable supply-chain problem. Now new tools, based on breakthrough academic research, promise to reduce inventories across the chain with the same or better service." (Cornacchia, 2004)

Taking the task to the next level of optimizing the entire supply chain raises the degree of difficulty substantially. Any supply chain contains a large number of individual resources that need to be managed in the most efficient way through the stages of the process. The key stages are production, warehousing and transportation, each of which has resources whose utilization must be optimized. Inventory is just one of these resources. Supply chain optimization problems have been addressed through a variety of different approaches. There are now a wide number of available solutions and software packages addressing individual problems and "local" optimization processes which work well. Recent attempts have been made to provide global optimization tools and solutions, but so far, all have fallen short of delivering the expected benefits. The complex interactions between various elements in a supply chain are indeed formidable. History shows that solutions that don't fully take these interactions into account will most often result in failure. (Cornacchia, 2004)

Looking specifically at inventory optimization, the problem must account for a series of constraints including cost, time and performance requirements that are derived from other resources. Also, stocking decisions made at other stages and/or stocking decisions made on other SKUs must be taken into consideration. The ultimate goal is to build a model that correctly represents these interactions. A workable solution to the supply chain optimization problem is to deal serially with sub-problems of a unique nature, rather than attempting to solve the whole problem. Within the current state of the art, that is the best that the "science" can currently offer. (Cornacchia, 2004)