Sensitivity Analysis of the Optimal Inventory-Pooling Strategies According to Multivariate Demand Dependence

Inventory-pooling (IP) is an effective tool to mitigate demand uncertainty and variability, to reduce operational costs, and consequently to increase the profit. The major assumptions of the previous works in literature on IP include the following: (1) Independents demand, which satisfy the typical normal independent and identically distributed (iid) random variables; (2) dependents (correlated) symmetric demands, which follows to a multivariate normal distribution. The effect of the dependent asymmetric demand is not yet studied. The aim of this paper is to consider this more realistic case. Indeed, the contribution of this paper is twofold. Firstly, it analyzes both the sensitivity of dependence structure and the levels of skewness of distributions on IP policies in terms of optimal total cost and demand satisfaction constraint. Secondly, both symmetric and asymmetric demand distributions are modeled using various beta distribution and the dependance between demands are modeled using various copulas. A newsvendor problem inspired by the literature, with two decentralized locations and two centralized locations, is considered the empirical study. For each dependance situation, three IP models are considered: inventory centralization, regular transshipments, and independent systems. The results suggest divergences in the decisions in about 9% of cases. Bad choice of marginal distributions given that the copula is appropriate can lead to divergences that vary between 2.2% and 4%, depending on whether the demand distributions are symmetric or asymmetric.

Shipment sizing for autonomous trucks of road freight

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.

How to Fill the Holes in Mining Inventory?

JoAnn put down the inventory reports of four Mosaic mines that she had been contemplating. Much of the raw material for the company’s phosphate fertilizer production was mined and processed at these Florida facilities. Keeping track of the vast array of equipment, parts, and supplies required to operate the mines had become an issue. JoAnn Speaks, Purchasing Manager at Mosaic Company, was tasked in 2015 to implement a comprehensive system to identify, control and track the company’s inventories. A recent Sarbanes–Oxley (SOX) audit had found weaknesses in Mosaic’s inventory practices leaving it unable to adequately account for the disposition of millions of dollars of assets annually. Working closely with technology companies Motion Industries and CribMaster®, JoAnn had successfully implemented a radio-frequency identification (RFID) system at five Mosaic chemical facilities. The implementation experienced hiccups, but problems had been resolved and the benefits had quickly become apparent. Within two months of going live, losses were down 35% and time spent locating items was reduced by 160 man-hours per month at the pilot chemical facility. Once the chemical facility implementation process approached completion, JoAnn turned her attention to the mines. RFID was great for the static and centralized chemical facility operating structure, but would it be successful in the mines? Although each mine had a central warehouse facility, equipment and numerous inventoried, critical-to-operations components were also spread across many square miles to minimize downtime in the event of a failure. This created environmental and connectivity issues that would have to be overcome to use RFID. With all the additional options available outside of RFID including vending, inventory centralization and even manpower additions, what was going to be the right single or combination of solutions? Or, was there something she hadn’t yet considered? The only sure thing was the SOX audit meant the status quo was not an option. JoAnn picked up the reports and leaned back in her chair as she considered the strengths and weaknesses of the possible solutions and decided what to do next.