With information revolution, increased globalization and competition, supply chain has become longer and more complicated than ever before. These developments bring supply chain management to the forefront of the managements attention. Inventories are very important in a supply chain. The total investment in inventories is enormous, and the management of inventory is crucial to avoid shortages or delivery delays for the customers and serious drain on a companys financial resources. The supply chain cost increases because of the influence of lead times for supplying the stocks as well as the raw materials. Practically, the lead times will not be same through out all the periods. Maintaining abundant stocks in order to avoid the impact of high lead time increases the holding cost. Similarly, maintaining fewer stocks because of ballpark lead time may lead to shortage of stocks. This also happens in the case of lead time involved in supplying raw materials. A better optimization methodology that utilizes the Particle Swarm Optimization algorithm, one of the best optimization algorithms, is proposed to overcome the impasse in maintaining the optimal stock levels in each member of the supply chain. Taking into account the stock levels thus obtained from the proposed methodology, an appropriate stock levels to be maintained in the approaching periods that will minimize the supply chain inventory cost can be arrived at.
Inventory takes many forms, ranging from raw materials to finished goods. While holding large amounts of inventory enables a company to be responsive to fluctuations in customer demand, the associated costs can be excessive. In order to operate in a lean environment at maximum efficiency levels, companies must minimize all unnecessary expenses, including those associated with production and storage of inventories.
Inventory control is typically a key aspect of almost every manufacturing and/or distribution operation business. The ultimate success of these businesses is often dependent on its ability to provide customers with the right goods, at the right place, at the right time. The right goods are those that the customer wants; the right place is your “available” inventory, not the supplier’s warehouse, and in today’s economy the right time is immediately.
Failure to have the right goods in the right place at the right time often leads to lost sales and profits and, even worse, to lost customers. Today’s reality is that there is very little differentiation between commodity products of the same type, and customers will, more often than not, choose to return to businesses that meet all three conditions, even choosing relatively unknown brands over known brands.
The role of inventory management is to coordinate the actions of all business segments, particularly sales, marketing and production, so that the appropriate level of stock is maintained to satisfy customers’ demands. The goal of inventory management is to balance supply and demand as closely as possible in order to keep customers satisfied and drive profits.
Inventory management is a fundamental requisite to supply chain optimization. The processes and controls of effective inventory management are critical to any successful business. Since it is rarely the case that any business has the luxury of unlimited capital, inventory management involves important decisions about what to buy or produce, how much to buy or produce and when to buy or produce within the capital limits. These are “value decisions.” Excessive inventory investments can tie up capital that may be put to better use within other areas of the business. On the other hand, insufficient inventory investment can lead to inventory shortages and a failure to satisfy customer demand. A balance must be struck and maintained.
The aim of inventory management is to reduce inventory holdings to the lowest point without negatively impacting availability or customer service levels. This can be done while still maximizing the business’ ability to exploit economies of scale to positively impact profitability.
Inventory optimization takes inventory management to the next level, enabling businesses to further reduce inventory levels while improving customer service levels and maximizing capital investments.
Inventory management is an ongoing process that relies on inputs from forecasts and product pricing, and should be executable within the cost structure of the business under an overall plan. Inventory control involves three inventory forms of the flow cycle:
Basic Stock -The exact quantity of an item required to satisfy a demand forecast.
Seasonal Stock -A quantity buildup in anticipation of predictable increases in demand that occur at certain times in the year. Safety Stock -A quantity in addition to basic inventory that serves as a buffer against uncertainty.
The challenge is to weigh the balance in favor of basic stock so that the business holds as little safety stock as possible and provides ‘just the right amount’ of seasonal stock. However, the predictability of demand has a direct impact on how much safety stock a business must hold. When demand is unpredictable, higher levels of safety stock must be maintained. Therefore, the search for the optimal inventory levels to achieve a lean manufacturing environment becomes a key objective.
The primary function of an Inventory Optimization solution is to allow companies to effectively fulfill demand and identify how to gain additional profits from their inventories. Improved efficiencies through effective resource management and optimization lead to an increase in service level, improved performance against customer request dates and improved return on equity. These gains are derived in three ways: a) System Benefits b) Value-Added Benefits and c) Strategic Benefits.
In 1995, Kennedy and Eberhartin, inspired by the choreography of a bird flock, first proposed the Particle Swarm Optimization (PSO). In comparison with the evolutionary algorithm, PSO, relatively recently devised population-based stochastic global optimization algorithm, has many similarities and the robust performance of the proposed method over a variety of difficult optimization problems has been proved [1]. In accordance with PSO, either the best local or the best global individual affects the behavior of each individual in order to help it fly through a hyperspace [2]
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