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

Optimal production planning for a multi-product closed loop system with uncertain demand and return

We study the production planning problem for a multi-product closed loop system, in which the manufacturer has two channels for supplying products: producing brand-new products and remanufacturing returns into as-new ones. In the remanufacturing process, used products are bought back and remanufactured into as-new products which are sold together with the brand-new ones. The demands for all the products are uncertain, and their returns are uncertain and price-sensitive. The problem is to maximize the manufacturer\u27s expected profit by jointly determining the production quantities of brand-new products, the quantities of remanufactured products and the acquisition prices of the used products, subject to a capacity constraint. A mathematical model is presented to formulate the problem and a Lagrangian relaxation based approach is developed to solve the problem. Numerical examples are presented to illustrate the model and test the solution approach. Computational results show that the proposed approach is highly promising for solving the problems. The sensitivity analysis is also conducted to generate managerial insights

Investigation On The Influence Of Remanufacturing On Production Planning And Control – A Systematic Literature Review

Production planning and control (PPC) is one of the focal operational tasks of a company, and it is used to design logistics services in a target-orientated manner so that individual customer requirements can be fulfilled. However, existing PPC framework models are still based on the prevailing linear economic procedure (take - make - dispose). Due to customers' increasing interest in sustainability and growing regulatory pressure, the Circular Economy (CE) meets these changing conditions by closing material cycles, improving resource efficiency and extending product life cycles. However, for a company to guarantee a high logistics performance, the operational PPC must be adapted to this new economic model. To this end, it needs to be investigated whether and how the adaptation of circular strategies influences existing PPC processes. This paper focuses on the circular strategy of remanufacturing and its influence on different PPC-main tasks. The latter will be examined using a systematic literature review. Finally, the results of this analysis are compared with the Hanoverian Supply Chain Model as a PPC framework model. This comparison shows which PPC tasks are affected and which existing approaches have already been developed. Ultimately, these results provide the basis for developing a framework model for operational PPC regarding the CE

Modeling a Remanufacturing Reverse Logistics Planning Problem: Some Insights into Disruptive Technology Adoption

Remanufacturing is the process to restore the functionality of high-value end-of-life (EOL) products, which is considered a substantial link in reverse logistics systems for value recovery. However, due to the uncertainty of the reverse material fow, the planning of a remanufacturing reverse logistics system is complex. Furthermore, the increasing adoption of disruptive technologies in Industry 4.0/5.0, e.g., the Internet of things (IoT), smart robots, cloud-based digital twins, and additive manufacturing, has shown great potential for a smart paradigm transition of remanufacturing reverse logistics operations. In this paper, a new mixed-integer program is modeled for supporting several tactical decisions in remanufacturing reverse logistics, i.e., remanufacturing setups, production planning and inventory levels, core acquisition and transportation, and remanufacturing line balancing and utilization. The model is further extended by incorporating utilization-dependent nonlinear idle time cost constraints and stochastic takt time to accommodate diferent real-world scenarios. Through a set of numerical experiments, the infuences of diferent demand patterns and idle time constraints are revealed. The potential impacts of disruptive technology adoption in remanufacturing reverse logistics are also discussed from managerial perspectives, which may help remanufacturing companies with a smart and smooth transition in the Industry 4.0/5.0 era

A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Supply planning for a closed loop system with uncertain demand and return

This proposed model considers a supply network consisting of a manufacturer, its external suppliers, and a remanufacturing facility. The manufacturer, facing an uncertain market demand and return, has two options for supplying parts: either ordering the required parts to external suppliers or remanufacturing used products and bringing those back to \u27as new\u27 conditions. We propose a general framework for this multi product, closed loop system and develop a non-linear programming (NLP) model to maximize the total expected profit by optimally deciding quantity of parts to be remanufactured and quantity of parts to be purchased from external suppliers. We solved the mathematical model using two different solution techniques to find optimal or near optimal solution values. With a numerical example we compared the results from both solution techniques and introduced sensitivity analysis to illustrate the interacting effects among critical parameters in the model

Coordinating production and recycling decisions with stochastic demand and return

In this paper, the joint production and recycling problem is investigated for a hybrid manufacturing and remanufacturing system where brand-new products are produced in the manufacturing plant and recycled products are remanufactured into as-new products in the remanufacturing facility. Both the brand-new products and remanufactured products are used to satisfy customer demands. Returns of used products that are recycled from customers are assumed to be stochastic and nonlinearly price-dependent. A mathematical model is proposed to maximize the overall profit of the system through simultaneously optimizing the production and recycling decisions, subject to two capacity constraints — the manufacturing capacity and the remanufacturing capacity. Based on Lagrangian relaxation method, subgradient algorithm and heuristic algorithm, a solution approach is developed to solve the problem. A representative example is presented to illustrate the system, and managerial analysis indicates that the uncertainties in demand and return have much influence on the production and recycling policy. In addition, twenty randomly produced examples are solved, and computational results show that the solution approach can obtain very good solutions for all examples in reasonable time

Decision makings in key remanufacturing activities to optimise remanufacturing outcomes : a review

The importance of remanufacturing has been increasing since stricter regulations on protecting the environment were enforced. Remanufacturing is considered as the main means of retaining value from used products and components in order to drive a circular economy. However, it is more complex than traditional manufacturing due to the uncertainties associated with the quality, quantities and return timing of used products and components. Over the past few years, various methods of optimising remanufacturing outcomes have been developed to make decisions such as identifying the best End-Of-Life (EOL) options, acquiring the right amounts of cores, deciding the most suitable disassembly level, applying suitable cleaning techniques, and considering product commonality across different product families. A decision being made at one remanufacturing activity will greatly affect the decisions at subsequent activities, which will affect remanufacturing outcomes, i.e. productivity, economic performance effectiveness, and the proportion of core that can be salvaged. Therefore, a holistic way of integrating different decisions over multiple remanufacturing activities is needed to improve remanufacturing outcomes, which is a major knowledge gap. This paper reviews current remanufacturing practice in order to highlight both the challenges and opportunities, and more importantly, offers useful insights on how such a knowledge gap can be bridged

Production planning mechanisms in demand-driven wood remanufacturing industry

L'objectif principal de cette thèse est d'étudier le problème de planification de la production dans le contexte d'une demande incertaine, d’un niveau de service variable et d’approvisionnements incontrôlables dans une usine de seconde transformation du bois. Les activités de planification et de contrôle de production sont des tâches intrinsèquement complexes et difficiles pour les entreprises de seconde transformation du bois. La complexité vient de certaines caractéristiques intrinsèques de cette industrie, comme la co-production, les procédés alternatifs divergents, les systèmes de production sur commande (make-to-order), des temps de setup variables et une offre incontrôlable. La première partie de cette thèse propose une plate-forme d'optimisation/simulation permettant de prendre des décisions concernant le choix d'une politique de planification de la production, pour traiter rapidement les demandes incertaines, tout en tenant compte des caractéristiques complexes de l'industrie de la seconde transformation du bois. À cet effet, une stratégie de re-planification périodique basée sur un horizon roulant est utilisée et validée par un modèle de simulation utilisant des données réelles provenant d'un partenaire industriel. Dans la deuxième partie de cette thèse, une méthode de gestion des stocks de sécurité dynamique est proposée afin de mieux gérer le niveau de service, qui est contraint par une capacité de production limitée et à la complexité de la gestion des temps de mise en course. Nous avons ainsi développé une approche de re-planification périodique à deux phases, dans laquelle des capacités non-utilisées (dans la première phase) sont attribuées (dans la seconde phase) afin de produire certains produits jugés importants, augmentant ainsi la capacité du système à atteindre le niveau de stock de sécurité. Enfin, dans la troisième partie de la thèse, nous étudions l’impact d’un approvisionnement incontrôlable sur la planification de la production. Différents scénarios d'approvisionnement servent à identifier les seuils critiques dans les variations de l’offre. Le cadre proposé permet aux gestionnaires de comprendre l'impact de politiques d'approvisionnement proposées pour faire face aux incertitudes. Les résultats obtenus à travers les études de cas considérés montrent que les nouvelles approches proposées dans cette thèse constituent des outils pratiques et efficaces pour la planification de production du bois.The main objective of this thesis is to investigate the production planning problem in the context of uncertain demand, variable service level, and uncontrollable supply in a wood remanufacturing mill. Production planning and control activities are complex and represent difficult tasks for wood remanufacturers. The complexity comes from inherent characteristics of the industry such as divergent co-production, alternative processes, make-to-order, short customer lead times, variable setup time, and uncontrollable supply. The first part of this thesis proposes an optimization/simulation platform to make decisions about the selection of a production planning policy to deal swiftly with uncertain demands, under the complex characteristics of the wood remanufacturing industry. For this purpose, a periodic re-planning strategy based on a rolling horizon was used and validated through a simulation model using real data from an industrial partner. The computational results highlighted the significance of using the re-planning model as a practical tool for production planning under unstable demands. In the second part, a dynamic safety stock method was proposed to better manage service level, which was threatened by issues related to limited production capacity and the complexity of setup time. We developed a two-phase periodic re-planning approach whereby idle capacities were allocated to produce more important products thus increasing the realization of safety stock level. Numerical results indicated that the solution of the two-phase method was superior to the initial method in terms of backorder level as well as inventory level. Finally, we studied the impact of uncontrollable supply on demand-driven wood remanufacturing production planning through an optimization and simulation framework. Different supply scenarios were used to identify the safety threshold of supply changes. The proposed framework provided managers with a novel advanced planning approach that allowed understanding the impact of supply policies to deal with uncertainties. In general, the wood products industry offers a rich environment for dealing with uncertainties for which the literature fails to provide efficient solutions. Regarding the results that were obtained through the case studies, we believe that approaches proposed in this thesis can be considered as novel and practical tools for wood remanufacturing production planning