An agent-based simulator for quantifying the cost of uncertainty in production systems

Product-mix problems, where a range of products that generate different incomes compete for a limited set of production resources, are key to the success of many organisations. In their deterministic forms, these are simple optimisation problems; however, the consideration of stochasticity may turn them into analytically and/or computationally intractable problems. Thus, simulation becomes a powerful approach for providing efficient solutions to real-world productmix problems. In this paper, we develop a simulator for exploring the cost of uncertainty in these production systems using Petri nets and agent-based techniques. Specifically, we implement a stochastic version of Goldratt’s PQ problem that incorporates uncertainty in the volume and mix of customer demand. Through statistics, we derive regression models that link the net profit to the level of variability in the volume and mix. While the net profit decreases as uncertainty grows, we find that the system is able to effectively accommodate a certain level of variability when using a Drum-Buffer-Rope mechanism. In this regard, we reveal that the system is more robust to mix than to volume uncertainty. Later, we analyse the cost-benefit trade-off of uncertainty reduction, which has important implications for professionals. This analysis may help them optimise the profitability of investments. In this regard, we observe that mitigating volume uncertainty should be given higher consideration when the costs of reducing variability are low, while the efforts are best concentrated on alleviating mix uncertainty under high costs.This article was financially supported by the State Research Agency of the Spanish Ministry of Science and Innovation (MCIN/AEI/ 10.13039/50110 0 011033), via the project SPUR, with grant ref. PID2020–117021GB-I00. In addition, the authors greatly appreciate the valuable and constructive feedback received from the Editorial team of this journal and two anonymous reviewers in the different stages of the review process

A continuous review policy for e-commerce inventory management in darkstores

The demand placement in e-commerce retail provides a lot of research opportunities. The delay between the order request and depletion from the inventory (reffered to as “ordering window”) allows the retailer to take advantage in order to reduce holding and stockout costs. In this research we want to assess the potential advantages of using a customized inventory policy to be implemented in the darkstores that takes into account this flexibility. This study presents an (s;Q) inventory policy that explicitly accounts for the ordering window. We consider that the customer demand as well as the customer ordering window are stochastic, and we focus in the products that are fulfilled through the darkstore. Considering the flexibility provided by the ordering windows we are able to find the optimal parameters for the policy, applying an iterative procedure that uses analytical expressions. The study provides a numerical experiment using simulation to validate the policy. It also incorporates a numerical comparison of the total cost between the traditional policy and the adapted policy to e-commerce retail that this study develops. Our policy provides significant savings. Nevertheless, this is just a first step in exploring inventory policies that account for the ordering window of e-commerce customers