Spare support model based on gamma degradation process

Spare parts ordering is very important in the domain of system support based on condition-based maintenance. For a single-unit system with condition monitoring, a joint degradation and spare parts ordering model is established in this paper to achieve the lowest total cost rate as the objective. The degradation process of system is assumed to be followed a gamma process. A decision on optimal spare ordering time by the improved cost rate model based on the proposed degradation model is made. Finally, a case analysis is implemented to demonstrate the effectiveness of the proposed model in this paper. Analysis results show that the proposed model can reduce the cost rate effectively

Optimization of Quantity Discounts Using JIT Technique under Alternate Cost Policies

In traditional economic order quantity modeling technique, as per the storage in a warehouse, the rate of demand is considered to be fixed, whereas in real world practice rate of demand may be dependent on time, price and stock. This paper studies problems based on allocation of order quantity under quantity discounts by revising mathematical models already studied in this area. For example, in a multi warehouse system like a super departmental store, the rate of demand is mostly subjective on the basis of stock demand. In industry, the maintenance of large stock of goods in warehouses has a higher probability of consumers as compared to an industry with small quantity of stock. Such procedures implied in single warehouses systems may be logical for level of stock that is dependent on demand. Hence, a good and large stock level mostly results in a higher profits and larger sales. The objective is to optimize profit under the effect of price variations in the form of quantity discounts based on an alternative cost functions, with the help of JIT inventory technique and analyzing a mathematical model based on it

Characterizations of Logistic Distribution through Order Statistics with Independent Exponential Shifts

The paper presents some distributional properties of logistic order statistics subject to independent exponential one-sided and two-sided shifts. Utilizing these properties, we extend several known results and obtain some new characterizations of the logistic distribution

Fill rate in a periodic review order-up-to policy under auto-correlated normally distributed, possibly negative, demand

We investigate the inventory service metric known as the fill rate—the proportion of demand that is immediately fulfilled from inventory. The task of finding analytical solutions for general cases is complicated by a range of factors including; correlation in demand, double counting of backlogs, and proper treatment of negative demand. In the literature, two approximate approaches are often proposed. Our contribution is to present a new fill rate measure for normally distributed, auto-correlated, and possibly negative demand. We treat negative demand as returns. Our approach also accounts for accumulated backlogs. The problem reduces to identifying the minimum of correlated normally distributed bivariate random variables. There exists an exact solution, but it has no closed form. However, the solution is amenable to numerical techniques, and we present a custom Microsoft Excel function for practical use. Numerical investigations reveal that the new fill rate is more robust than previous measures. Existing fill rate measures are likely to cause excessive inventory investment, especially when fill rate targets are modest, a strongly positive or negative autocorrelation in demand is present, or negative demands exist. Our fill rate calculation ensures that the target fill rate is achieved without excessive inventory investments

An Investigation of Buyers’ Forecast Sharing and Ordering Behavior in a Two-Stage Supply Chain

Profitably balancing demand and supply is a continuous challenge for companies under changing market conditions, and the potential benefit of collaboration between supply chain partners cannot be overlooked by any firm who strives to succeed. One of the key elements to successful collaboration is sharing of forecast information between supply chain partners. However, when supply shortage is expected, buyers may inflate order quantities and/or order forecasts to secure sufficient supply. An important question that arises is how the supplier should allocate inventory to customers when shortage exists. Literature shows that certain allocation policies can reduce buyers’ order inflation behavior. However, this has not yet been empirically shown for order forecast inflation behavior, nor incorporating the behavioral aspects of decision makers. In this dissertation, through behavioral experiments using a supply chain simulation game, we investigate the impact of different capacity allocation mechanisms and information disclosures of a supplier on buyers’ forecast sharing and ordering behavior. We first investigate the buyers’ order forecast sharing behavior in a single-suppliertwo- buyer supply chain. Our behavioral study shows that forecast-accuracy based allocation, where the supplier allocates more capacity to the buyer with better forecast accuracy, can significantly improve order forecast accuracy relative to uniform allocation, where the supplier equally allocates capacity to the buyers. Under both policies, particularly uniform allocation, the order forecast accuracy is improved with the supplier’s information disclosure on the policy. Next, we focus on buyers’ ordering behavior, and formulate a single-supplier-single-buyer base-stock inventory model under constrained supply. We validate our analytical results through numerical simulation, which is then extended to the single-supplier-two-buyer case. We next compare the buyers’ optimal decisions from the simulation with the actual decisions in our behavioral study, and find that buyers in the experiment show a significantly lower profit performance ranging from 0.8% to 14.1%. Using structural estimation modeling techniques, we estimate the buyers’ perceived overage/underage cost ratios from the experiment, and conclude by conducting a detailed analysis on the factors that affect buyers’ ordering decisions. In addition to academic contributions, our results provide insights for practitioners to understand buyers’ strategic behavior and help with designing capacity allocation strategies