The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Dampening variability by using smoothing replenishment rules.

A major cause of supply chain deficiencies is the bullwhip effect which can be substantial even over a single echelon. This effect refers to the tendency of the variance of the replenishment orders to increase as it moves up a supply chain. Supply chain managers experience this variance amplification in both inventory levels and replenishment orders. As a result, companies face shortages or bloated inventories, run-away transportation and warehousing costs and major production adjustment costs. In this article we analyse a major cause of the bullwhip effect and suggest a remedy. We focus on a smoothing replenishment rule that is able to reduce the bullwhip effect across a single echelon. In general, dampening variability in orders may have a negative impact on customer service due to inventory variance increases. We therefore quantify the variance of the net stock and compute the required safety stock as a function of the smoothing required. Our analysis shows that bullwhip can be satisfactorily managed without unduly increasing stock levels to maintain target fill rates.Bullwhip effect; Companies; Cost; Costs; Impact; Inventory; Managers; Order; Replenishment rule; Rules; Safety stock; Supply chain; Supply chain management; Variability; Variance; Variance reduction;

Periodic review base-stock replenishment policy with endogenous lead times.

In this paper, we consider a two stage supply chain where the retailer's inventory is controlled by the periodic review, base-stock level (R,S) replenishment policy and the replenishment lead times are endogenously generated by the manufacturer's production system with finite capacity. We extend the work of Benjaafar and Kim (2004) who study the effect of demand variability in a continuously reviewed base-stock policy with single unit demands. In our analysis, we allow for demand in batches of variable size, which is a common setting in supply chains. A procedure is developed using matrix analytic methods to provide an exact calculation of the lead time distribution, which enables the computation of the distribution of lead time demand and consequently the safety stock in an exact way instead of using approximations. Treating the lead time as an endogenous stochastic variable has a substantial impact on safety stock. We numerically show that the exogenous lead time assumption may dramatically degrade customer service.Production/inventory systems; Base-stock replenishment policy; endogenous lead times; Safety stock; Phase-type distribution; Matrix-analytical methods;

Analysing divergent logistic networks with local (R, S) inventory control

This paper deals with divergent logistic networks where the inventory at each node is controlled using a periodic review strategy with order-up-to level. An approximate method is presented to analyse the network performance (service levels, mean physical stock). The method is tested on a range of 2-echelon and 3-echelon networks by comparison to results from Monte Carlo simulation. We conclude that the approximation accuracy is sufficient for global network design in many practical situation

Inventory control for a non-stationary demand perishable product: comparing policies and solution methods

This paper summarizes our findings with respect to order policies for an inventory control problem for a perishable product with a maximum fixed shelf life in a periodic review system, where chance constraints play a role. A Stochastic Programming (SP) problem is presented which models a practical production planning problem over a finite horizon. Perishability, non-stationary demand, fixed ordering cost and a service level (chance) constraint make this problem complex. Inventory control handles this type of models with so-called order policies. We compare three different policies: a) production timing is fixed in advance combined with an order up-to level, b) production timing is fixed in advance and the production quantity takes the age distribution into account and c) the decision of the order quantity depends on the age-distribution of the items in stock. Several theoretical properties for the optimal solutions of the policies are presented. In this paper, four different solution approaches from earlier studies are used to derive parameter values for the order policies. For policy a), we use MILP approximations and alternatively the so-called Smoothed Monte Carlo method with sampled demand to optimize values. For policy b), we outline a sample based approach to determine the order quantities. The flexible policy c) is derived by SDP. All policies are compared on feasibility regarding the α-service level, computation time and ease of implementation to support management in the choice for an order policy.National project TIN2015-66680-C2-2-R, in part financed by the European Regional Development Fund (ERDF)

Optimization of Simulated Inventory Systems: OptQuest and Alternatives

This article illustrates simulation optimization through an (s, S) inventory management system.In this system, the goal function to be minimized is the expected value of specific inventory costs.Moreover, specific constraints must be satisfied for some random simulation responses, namely the service or fill rate, and for some deterministic simulation inputs, namely the constraint sOptQuest software;Response Surface Methodology;Karesh-Kuhn-Tucker conditions;service constrained inventory system