Optimal ordering policies for continuous review perishable inventory models

Simulation of different reordering policies for optimizing the inventory of perishable food: an Italian case study

Inventory management represents one of the main criticalities that companies have to face. The challenge is twofold: being able to satisfy a demand whose behavior can significantly vary and be unpredictable in order to avoid stock-out situations, whilst limiting excessive stocks and consequently costs. Some factors can complicate this already inherently difficult task. An example is the presence of constraints on the time for storage, which is the typical case for perishable products such as food and beverages, core business of the Italian company subject of the case study described in this paper. To optimize the reorder process in that context, a simulation model was developed for evaluating the potentials of three traditional reordering policies (i.e., Re-Order Point, Re-Order Interval, and (s, S)) to be applied to a set of products managed by the company, including perishable items, and for testing their performance. The items tested were selected through a preliminary ABC analysis, carried out on the volumes handled. A comparison with the current performance of the company’s and that achievable with the optimized policies is proposed.

Perishable Inventory System with N-Policy, MAP Arrivals, and Impatient Customers

In this study, we consider a perishable inventory system that has an (s, Q) ordering policy, along with a finite waiting hall. The single server, which provides an item to the customer after completing the required service performance for that item, only begins serving after N customers have arrived. Impatient demand is assumed in that the customers waiting to be served lose patience and leave the system if the server’s idle time overextends or if the arriving customers find the system to be full and will not enter the system. This article analyzes the impatient demands caused by the N-policy server to an inventory system. In the steadystate, we obtain the joint probability distribution of the level of inventory and the number of customers in the system. We analyze some measures of system performance and get the total expected cost rate in the steadystate. We present a beneficial cost function and confer the numerical illustration that describes the impact of impatient customers caused by N-policy on the inventory system’s total expected cost rate.

Model for blood collections management

BACKGROUND

A typical blood bank gets its major supply of blood from organizing blood donation camps. The quantity of blood collected in the blood donation drives is random and it might be sensible to cut off the supply at some level to reduce the wastages by overcollection.

STUDY DESIGN AND METHODS

In this paper we introduce two new cutoff level policies and compare their performance with the policy of collecting all the available supply at blood collection drives, which is quite prevalent among the blood banks. We develop a simulation model for collection that enables such comparison on the basis of shortages, wastages, and total costs.

RESULTS AND CONCLUSION

The results obtained from the simulation model for a real-life situation show that the cutoff level policies are better than the unrestricted collection policy in controlling total costs. They also result in less wastage in the system for a given level of shortages.