Multistage Inventory Management with Expediting

Adaptive Order Release Planning with Dynamic Lead Times

Adaptive lead time quotation in a pull production system with lead time responsive demand

We examine the effect of stochastic lead times on Just-in-Time (JIT) delivery. We find that with stochastic lead times there is a possibility of order crossover, and what order crossover does is to transform the original lead times into effective lead times, which is an AR(1) process that is an autoregressive process of Order 1. The mean of this process is the same as the mean of the original lead time, but its variance could be much smaller. The implication is that when we consider order crossover in the analysis, the cost could be less than otherwise (but never less than that with deterministic lead times). The literature on JIT with stochastic lead times has never considered order crossover, which produces the effective delivery times (EDT). Here, we demonstrate some important properties of the EDT: that it is a Cauchy sequence, and hence it converges; that it is an AR(1) process; and that it stochastically dominates the parent lead time distribution.

Lead time reduction and process enhancement for a low volume product

This paper explain single inventory problem operated by a base stock policy. The item demand is explained by Poisson process and supply lead time is a random variable follow exponential distribution. The supply lead time is considered to depend on the quality of the product accessible with the supplier. Whenever the desired quality is not available on shelf, the lead time distribution under goes a drift in mean. The optimum bas stock level is determined with the help of M/M/1 queue and the effect of drifted lead time distribution is applied to the cost function. The aftereffect of the model is shown with the assistance of spread sheet format.

We study a joint inventory and pricing problem in a single-stage system with a positive lead time. We consider both additive and multiplicative demand forms. This problem is, in general, intractable due to its computational complexity. We develop a simple heuristic that resolves this issue. The heuristic involves a myopic pricing policy that generates each period’s price as a function of the initial inventory level and a base-stock policy for inventory replenishment. In each period, the firm monitors its so-called price-deflated inventory position and places an order to reach a target base-stock level. The price-deflated inventory position weights the on-hand and pipeline inventory according to a factor that reflects the sensitivity of price to the net inventory level. To assess the effectiveness of our heuristic, we construct an upper bound to the exact system. The upper bound is based on an information-relaxation approach and involves a penalty function derived from the proposed heuristic. A numerical study suggests that the heuristic is near-optimal. The heuristic approach can be applied to a wide variety of inventory systems, such as systems with fixed ordering costs or fixed batch sizes. The heuristic enables us to explore the use of price as a lever to balance supply and demand. Our findings indicate that a responsive strategy (that effectively reduces the replenishment lead time) leads to a more stable pricing policy and that the value of dynamic pricing increases with lead time. This paper was accepted by Martin Lariviere, operations management.

Consider a single-item, periodic review, stationary inventory model with stochastic demands, proportional ordering costs, and convex holding and shortage costs, where shortages are backordered and Veinott's well known terminal condition holds. Orders can be scheduled for any period, but the actual inventory level is determined every T periods through an audit. This leads to a dynamic programming model where stage n contains periods (n − 1)T + 1 through nT. For both discounted and averaging criteria, a simple rule optimally describes the orders for the T periods of a stage as a function of the state (beginning inventory level) and the cumulative T-period order. The latter is optimally determined by a base stock policy with two base stock levels: one for the final stage, another for the rest. (The horizon may be finite or infinite.) Methods are presented for computing optimal policies, together with bounds on the costs of (suboptimal) myopic policies. Models with proportional costs and continuous demands are studied in detail. Computational experiments indicate that myopic policies perform quite well for such models. The selection of a best review period T is covered briefly. Applications of our model include just in time settings where audit decisions play a negligible role.

Fair and profitable: How pricing and lead-time quotation policies can help

PurposeUnprecedented endeavors have been made to take autonomous trucks to the open road. This study aims to provide relevant information on autonomous truck technology and to help logistics managers gain insight into assessing optimal shipment sizes for autonomous trucks.Design/methodology/approachEmpirical data of estimated autonomous truck costs are collected to help revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation lead time reduction.FindingsAutonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers using classic models that disregard the additional cost could underestimate the optimal shipment size for autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network.Research limitations/implicationsThe findings are based on information collected from trade articles and academic journals in the domain of logistics management. Other technical or engineering discussions on autonomous trucks are not included in the literature review.Practical implicationsLogistics managers must consider the latest cost information when deciding on shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal shipment size.Originality/valueThis study shows that some models in the literature might no longer be applicable after the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time reduction by adopting autonomous trucks.

As a result of previous studies [1-2], the warning performances without atmospheric electric field (EF) data are examined by using only intra-cloud (IC) discharges, and cloud-to-ground (CG) strikes data provided by a stand-alone lightning warning system (LWS) during two summers. The warning performances were evaluated by the warning statistics with probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI). Upon conclusion of this study, it was found that removal of EF data from warning conditions decreased FAR to 82%, and increased CSI to 171%, POD to 89% in comparison to the prior study[1]. Therefore, these results mean that IC and CG without EF data were beneficial to an increase in warning performances. However, the stand-alone LWS intended to trigger an only prior warning is not possible to make effective alerts (EA) because there is no effective lead time (LT). Hence, it has still been required to develop the new LWS proposed in the previous study [1] for triggering EA.

Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments

On the use of independent base-stock policies in assemble-to-order inventory systems with nonidentical lead times

Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time

In material management, the inventory systems may have good management aspects in terms of materials; however, this negatively affects the relationship between the facility and customers. In classical inventory models, arriving demands are satisfied immediately if there is enough on-hand inventory. Traditional inventory models consider optimization problems and find the optimal policy of decision variables without computing the stationary distribution of the inventory states for random demand. Hence, a detailed analysis of inventory management systems requires a joint distribution of system stock levels and the number of requests to be investigated thoroughly. This research provides a new stochastic mathematical model for inventory systems with lead times and impatient customers under deterministic and uniform order sizes. The proposed model identifies the performance measures in a stochastic environment, analyzing the properties of the inventory system with stochastic and probabilistic parameters, and finally, validating the model’s accuracy. To analyze the system, balance equations were derived from a mathematical characterization of the underlying queuing model dependent on the Markov chain formalism. The precise performance was achieved by examining the graphical representation of the service process in a steady-state as a function of both arrival distribution and the customer patience coefficient, while it was challenging to derive an optimal curve fit in a three-dimensional space that features two input variables and a single output variable.

This paper studies multiproduct inventory models with stochastic demands and a warehousing constraint. Finite horizon as well as stationary and nonstationary discounted-cost infinite-horizon problems are addressed. Existence of optimal feedback policies is established under fairly general assumptions. Furthermore, the structure of the optimal policies is analyzed when the ordering cost is linear and the inventory/backlog cost is convex. The optimal policies generalize the base-stock policies in the single-product case. Finally, in the stationary infinite-horizon case, a myopic policy is proved to be optimal if the product demands are independent and the cost functions are separable.