The standard economic order quantity model assumes that stocks arepaid for when delivered. Supplier credit is widely available and canhave considerable impact on holding…
The standard economic order quantity model assumes that stocks are paid for when delivered. Supplier credit is widely available and can have considerable impact on holding costs and on the optimal order quantities. A simple extension to the standard economic order quantity model yields significant savings for items which have order cycles that are short relative to the period allowed for payment. The analysis also increases the attractiveness of joint replenishment models and highlights some difficulties of applying discrete demand models.
Addresses a number of issues relating to determining whetherproducts should be ordered independently and therefore shipped as asingle‐product order, or co‐ordinated and…
Addresses a number of issues relating to determining whether products should be ordered independently and therefore shipped as a single‐product order, or co‐ordinated and shipped as a group, or multiproduct, order from a single source. Factors which might influence the decision include the level or volume of demand, the distribution of demand across products, the weight of items and the attractiveness of the quantity discount offered. Uses an optimal inventory‐theoretic model, that incorporates transport weight breaks and quantity discounts, to assess when product orders should be combined and what products should be ordered separately. The effects of these decisions on the order interval, the number of order groupings, the proportion of items ordered independently, the proportion of attractive discounts forgone in favour of consolidation, and the relative cost savings, are examined using an extensive set of simulated data that are based on a firm in the automobile industry supply chain.
Organisations either keep spares for their own use, or‐for‐sale to other organisations. In either case, the ultimate need is to be able to replace worn or defective parts in operational machinery or equipment. In an economic sense, spares are kept to meet the needs of the situation in the cheapest way.
The inventory management system of a discount retail store was examined. A just‐in‐time inventory management model and a quantity discount model were used to determine the…
The inventory management system of a discount retail store was examined. A just‐in‐time inventory management model and a quantity discount model were used to determine the appropriateness of each model for the retail outlet. Based on the calculations performed, it was determined that utilizing a retail just‐in‐time (JIT) policy is unrealistic. Customer demands constantly change, and shortages due to stock‐outs can cause huge losses in profits, especially when customers are lost to competitors. Additionally, the quantity discount model provides the lowest total cost for a retail outlet. Not only are the prices cheaper when inventory is bought in large quantities, but shortages or stock‐outs are rare. The optimal solution for a retail store is implementing the quantity discount method.
The ability of traditional economic order quantity (EOQ) models tosuccessfully determine optimum purchase and process batch sizes hasdeclined in recent years. These models…
The ability of traditional economic order quantity (EOQ) models to successfully determine optimum purchase and process batch sizes has declined in recent years. These models are simplistic in nature in that they make assumptions that are no longer valid in practice, in addition, they cannot take into account the wide variety of cost and other factors that influence inventory control decisions. Presents an alternative method for identifying economic batch quantities that uses genetic algorithms (GA) based on the underlying mathematical processes that control the reproduction of genes within biological species. Using genetic algorithms it is possible to successfully deal with complex inventory situations and there are no limits on the type and number of variables that can be used to influence the batch‐sizing decision.
This two-part case illustrates the use of economic order quantity to manage conflicting performance measures across different siloed functions in an organization. Part A…
This two-part case illustrates the use of economic order quantity to manage conflicting performance measures across different siloed functions in an organization. Part A requires students to assess the costs of various order quantities and quantify the concept of “robustness.“ Part B emphasizes managing the variables of annual demand, ordering cost, inventory carrying cost, and unit price to achieve strategic goals. The student must determine how to lower ordering costs to compensate for increases in the other variables as well as to help guide Just-In-Time implementation efforts.
The purchasing function is growing in importance in today's industrial economy. In many purchasing situations there are a number of conflicting considerations that influence the final purchasing decision. The professional purchasing person must make profitable buying decisions under these circumstances. The single item purchase lot sizing literature in the past has served as the foundation for developing and studying the requirements planning based models and techniques. The purchasing manager's methods for making quantity (lot size) decisions are examined. Significant literature on the subject is classified and a taxonomy provided. Variations within the purchase lot sizing literature are discussed. Purchase lot sizing literature has important limitations by focusing exclusively on lot sizing as the relevant criterion for making economic order size decisions. A logical extension would be to include, the economic performance of alternative lot size procedures in a capacitated Materials Requirement Planning (MRP) environment. Another extension should consider the economics of jointly ordering from one vendor.
To develop just‐in‐time (JIT) purchasing threshold value (JPTV) models for ready mixed concrete (RMC) suppliers to decide whether or not to switch from an economic order…
To develop just‐in‐time (JIT) purchasing threshold value (JPTV) models for ready mixed concrete (RMC) suppliers to decide whether or not to switch from an economic order quantity (EOQ) approach to a JIT purchasing approach for the purchase of their raw materials, when a price discount is offered.
The existing economic order quantity (EOQ) with a price discount versus the JIT purchasing cost comparative models neglect some important cost components under the inventory management systems, for example, the out‐of‐stock costs and the impact of inventory policy on product quality and production flexibility. In addition, these models do not empirically study the capability of an inventory facility to hold the EOQ‐JIT cost indifference point's amount of inventory. These models suggest that the JIT purchasing approach is always preferred to the EOQ approach when the JIT purchasing approach can capitalize on physical plant space reduction. The JPTV models developed in this study overcome the two limitations of the existing EOQ and JIT purchasing cost comparative models.
By developing the JPTV models, this study suggests that the theoretical advantages of JIT purchasing may have been overstated.
The field studies conducted in the RMC industries in Chongqing, China and Singapore supported the propositions in this study. The JPTV models, if adopted, would help to enhance performance in the RMC industries in other cities as well.
Overview All organisations are, in one sense or another, involved in operations; an activity implying transformation or transfer. The major portion of the body of knowledge concerning operations relates to production in manufacturing industry but, increasingly, similar problems are to be found confronting managers in service industry. It is only in the last decade or so that new technology, involving, in particular, the computer, has encouraged an integrated view to be taken of the total business. This has led to greater recognition being given to the strategic potential of the operations function. In order to provide greater insight into operations a number of classifications have been proposed. One of these, which places operations into categories termed factory, job shop, mass service and professional service, is examined. The elements of operations management are introduced under the headings of product, plant, process, procedures and people.
The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the…
The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the well-established economic order quantity (EOQ) model.
The authors develop an EOQ-type model to investigate the operational cost impact of the order quantity with backroom storage. Because of the discrete and discontinuous nature of the problem, a modification of an existing algorithm is applied to obtain an optimal order quantity. Numerical experiments derived from a leading retailer in Thailand are used to study the cost impact of the backroom.
The paper shows that the backroom storage will significantly affect the decision regarding the order quantity. If its effect is ignored, the cost increase can be as high as 30 per cent. The costs and operations of additional shelf-refill trips from the backroom must be carefully analyzed and included in the decisions of replenishment operations.
The model is a simplified version of the actual replenishment process. Validation from a real-world setting should be used to confirm the results. There are many additional opportunities to further integrate other issues in this problem such as shelf space decisions or joint order quantity between vendors and retailers.
The insights gained from the model will help managers, both retailers and vendors or manufacturers, make better decisions with regard to the order quantity policy in the supply chain.
Problems with backroom storage have been qualitatively described in the literature in the past decade. This paper is an early attempt to develop a quantitative model to analytically study the cost impact of backroom on order quantity decisions.