Temperature Monitoring for Quality Prediction and Inventory Control in Cold Chain: a Case of 18℃ Ready-to-eat Food in Taiwan

The aim of the study was the development of a quality prediction model combined with the incoming analysis for temperature control in 18 degree ready-to-eat food during logistics flows. And analyzed how temperature monitoring improves inventory decision. Base on the growth of Pseudomonas sp., the model was developed by mathematical model with Gompertz model. The model predicts for quality as well as shelf life in the monitoring temperature is about 19.5 h. On the other hand, the incoming analysis shows that the inventory quantities at 7 ℃ and 18 ℃ is more than at 25 ℃.The model can be considered to be an effective tool (in combination with temperature monitoring) for improvement of quality management with the incoming consideration. Moreover, our results suggest that temperature-controlled food companies could share temperature information with its chain partners which emphases a food quality and logistics cost balance in supply chain

RFID-Enabled Management of Highly-Perishable Inventory: A Markov Decision Process Approach for Grocery Retailers

We address the challenge of managing perishable inventory. One study was conducted to analyze the effects of recapturing unsatisfied demand, and another to estimate improvements in operational metrics through delaying order placements. Our results indicate that significant profit improvements can be achieved under these scenarios, as evidenced by a greater than 30% median increase in profit margin

The Value of Demand Information in Omni-Channel Grocery Retailing

As e-commerce reaches one of the last strongholds of traditional fulfillment, how can grocers leverage the omni-channel trend and stay competitive in today’s changing market landscape? To improve operating outcomes and address food waste concerns, this study investigates various scenarios in which the grocery retailer accepts online orders in advance. We examine the value of advance demand information through a Markov Decision Process-based model, in terms of changes to expected profits, outdating, freshness, and several inventory and service performance metrics. Our results indicate that when the demand lead time is longer than the replenishment lead time, close to 20% safety stock reduction on average can be achieved, leading to a 15% decrease in product deterioration and 26% less outdating. In some cases, we also find that it is possible to profitably offer discounted prices in exchange for the customer’s future demand information

A Stochastic Dynamic Programming Approach to Revenue Management in a Make-to-Stock Production System

In this paper, we consider a make-to-stock production system with known exogenous replenishments and multiple customer classes. The objective is to maximize profit over the planning horizon by deciding whether to accept or reject a given order, in anticipation of more profitable future orders. What distinguishes this setup from classical airline revenue management problems is the explicit consideration of past and future replenishments and the integration of inventory holding and backlogging costs. If stock is on-hand, orders can be fulfilled immediately, backlogged or rejected. In shortage situations, orders can be either rejected or backlogged to be fulfilled from future arriving supply. The described decision problem occurs in many practical settings, notably in make-to-stock production systems, in which production planning is performed on a mid-term level, based on aggregated demand forecasts. In the short term, acceptance decisions about incoming orders are then made according to stock on-hand and scheduled production quantities. We model this problem as a stochastic dynamic program and characterize its optimal policy. It turns out that the optimal fulfillment policy has a relatively simple structure and is easy to implement. We evaluate this policy numerically and find that it systematically outperforms common current fulfillment policies, such as first-come-first-served and deterministic optimization

Lateral transshipment of slow moving critical medical items

This research studies lateral transshipment of critical medical items that have low demands. Due to the high prices of medical items and their limited shelf lives, the expirations contribute significantly to the current prohibitively high cost of the healthcare system. Lateral transshipment between hospitals in a medical system provides opportunities to reduce the expiration costs. This paper studies the decision rule for lateral transshipment in a two-hospital system and extends the rule for the multiple-hospital cases. The decision rule takes the myopic best action by assuming no transshipments will be performed in the future. Numerical experiments demonstrate significant cost savings and the decision rule has a small gap from the upper bound of the total saving. The savings are more considerable when the difference of demand rates at different locations is large and the life time of the medical item is not too long or too short

Developing decision support for Foodbank South Africa's allocation system: an application of operational research techniques to aid decision-making at a not-for-profit organization

There is a dearth of research on the application of hard Operational Research (OR) techniques (simulation, linear programming, goal programming, etc.) in determining optimal ordering, inventory and allocation policies for goods within distribution systems in developing countries. This study aims to assist decision making at a not-for-profit organization (NPO), Foodbank South Africa (FBSA), within its allocation system through a combined ‘soft-hard’ OR approach. Two problem-structuring tools (soft OR), Causal Mapping (CM) and Soft System Methodology’s Root Definitions (RDs), are used to structure the organization's goals (in order to gain a comprehensive understanding of the decision-context) and gain a better understanding of the ‘decision-issues’ in the allocation system at its Cape Town warehouse

AN INVENTORY ROUTING PROBLEM FOR DETERIORATING ITEMS WITH DYNAMIC DEMAND AND SPOILAGE RATE

Inventory routing problems (IRP) are among important tools to be used for implementing vendor manage inventory. Many researchers try to develop methods for solving inventory routing problem, however, only a few developed methods for inventory routing problems for spoilage items. In reality, many items are deteriorated and spoiled during transportation and storage period. In this paper, we developed a model and methodsto solve the inventory routing problem for deteriorating items with dynamic demand and spoilage rate, i.e., demand varies and items spoil during planning periods. Those cases are more realistic since many commodities such as fruits and vegetables have dynamic demand and spoilage rate. A Genetic Algorithm and Particle Swarm Optimization are developed to solve the problem with various demands in a specic planning period since the problem is Np-hard. A numerical example and sensitivity analysis are conducted to verify the model, and to get management insight it. The result is interesting and support general hypothesis that dynamic demands result in higher inventory cost than the static demands, and the increasing demand results in increasing inventory cost.mAlso, the results show that increasing demand and deteriorating rates signicantly affect the total cost, therefore, the developed model is important and signicantly useful to be used for solving IRP with dynamic demand and spoilage items

Dynamic lot size problems with one-way product substitution

Department of Logistics2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

A replenishment policy for a perishable inventory system based on estimated aging and retrieval behavior

So far the literature on inventory control for perishable products has mainly focused on (near-) optimal replenishment policies for a stylized environment, assuming no leadtime, no lot-sizing, stationary demand, a first in first out retrieval policy and/or product life time equal to two periods. This literature has given fundamental insight in the behavior and the complexity of inventory systems for perishable products. In practice, many grocery retailers have recently automated the inventory replenishment for non-perishable products. They recognize they may need a different replenishment logic for perishable products, which takes into account e.g. the age of the inventory in the system. Due to new information technologies like RFID, it now also becomes more economically feasible to register this type of information. This paper suggests a replenishment policy for perishable products which takes into account the age of inventories and which requires only very simple calculations. It will be shown that in an environment, which contains important features of the real-life retail environment, this new policy leads to substantial cost reductions compared with a base policy that does not take into account the age of inventories