Forecasting model selection through out-of-sample rolling horizon weighted errors

Demand forecasting is an essential process for any firm whether it is a supplier, manufacturer or retailer. A large number of research works about time series forecast techniques exists in the literature, and there are many time series forecasting tools. In many cases, however, selecting the best time series forecasting model for each time series to be dealt with is still a complex problem. In this paper, a new automatic selection procedure of time series forecasting models is proposed. The selection criterion has been tested using the set of monthly time series of the M3 Competition and two basic forecasting models obtaining interesting results. This selection criterion has been implemented in a forecasting expert system and applied to a real case, a firm that produces steel products for construction, which automatically performs monthly forecasts on tens of thousands of time series. As result, the firm has increased the level of success in its demand forecasts. © 2011 Elsevier Ltd. All rights reserved.Poler Escoto, R.; Mula, J. (2011). Forecasting model selection through out-of-sample rolling horizon weighted errors. Expert Systems with Applications. 38(12):14778-14785. doi:10.1016/j.eswa.2011.05.072S1477814785381

Green supply chain quantitative models for sustainable inventory management: A review

[EN] This paper provides a systematic and up-to-date review and classification of 91 studies on quantitative methods of green supply chains for sustainable inventory management. It particularly identifies the main study areas, findings and quantitative models by setting a point for future research opportunities in sustainable inventory management. It seeks to review the quantitative methods that can better contribute to deal with the environmental impact challenge. More specifically, it focuses on different supply chain designs (green supply chain, sustainable supply chain, reverse logistics, closed-loop supply chain) in a broader application context. It also identifies the most important variables and parameters in inventory modelling from a sustainable perspective. The paper also includes a comparative analysis of the different mathematical programming, simulation and statistical models, and their solution approach, with exact methods, simulation, heuristic or meta-heuristic solution algorithms, the last of which indicate the increasing attention paid by researchers in recent years. The main findings recognise mixed integer linear programming models supported by heuristic and metaheuristic algorithms as the most widely used modelling approach. Minimisation of costs and greenhouse gas emissions are the main objectives of the reviewed approaches, while social aspects are hardly addressed. The main contemplated inventory management parameters are holding costs, quantity to order, safety stock and backorders. Demand is the most frequently shared information. Finally, tactical decisions, as opposed to strategical and operational decisions, are the main ones.The research leading to these results received funding from the Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe". It was also funded by the National Agency for Research and Development (ANID) / Scholarship Program/Doctorado Becas en el Extranjero/2020 72210174.Becerra, P.; Mula, J.; Sanchis, R. (2021). Green supply chain quantitative models for sustainable inventory management: A review. Journal of Cleaner Production. 328:1-16. https://doi.org/10.1016/j.jclepro.2021.129544S11632

Optimising location, inventory and transportation in a sustainable closed-loop supply chain

[EN] Operations management researchers and practitioners have shown increasing interest in incorporating sustainability into supply chain (SC) design models. This means that sustainability must be considered in all aspects of the SC, including location, inventory and transportation (LIT) decisions. Hence the aim of this article is to propose an optimisation model that incorporates: (i) LIT decisions in an integrated manner; (ii) the three sustainability (3S) aspects, i.e. economic, environmental and social, into each named decisions; and (iii) a closed-loop supply chain (CLSC) structure. The proposed formulation is a multi-objective mixed integer non-linear programming (MO-MINLP) model whose objectives consider minimisation of economic and social costs (economic aspect) and carbon emissions (environmental aspect), and maximisation of the social impact of SC operations (social aspect). A transformation technique is applied to one of the objective functions, which results in an MO-MILP model solved by the lexicographic method. This article focuses on commodity industries where only one finished product is manufactured. Hence the 3S-LIT model is validated with a randomly generated dataset and against a recently published alternative model applied to the copper mining industryThe research leading to these results received funding from the project "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; from the grant PDC2022-133957-I00 funded by MCIN/AEI/10.13039/501100011033 and by European Union Next Generation EU/PRTR; and from the European Union's Horizon Europe research and innovation programme under grant agreement No. 101070076 "Optimizing Production and Logistic Resources in the Time-critical Bio Production Industries in Europe" (CLARUS). It was also funded by the National Agency for Research andDevelopment (ANID)/Scholarship Program/Doctorado Becas en el Extranjero/2020 -72210174 and sponsored by the Universidad de Atacama as part of an academic improvement plan.Becerra, P.; Mula, J.; Sanchis, R. (2023). Optimising location, inventory and transportation in a sustainable closed-loop supply chain. International Journal of Production Research. 1-24. https://doi.org/10.1080/00207543.2023.219751512

Quantitative modelling approaches for lean manufacturing under uncertainty

[EN] Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the opera-tions that do not add value to the product or processes to increase the value of each performedactivity. Here the main motivation is to study how quantitative modelling approaches can supportLM tools even under system and environment uncertainties. The main contributions of the articleare: (i) providing a systematic literature review of 99 works related to the modelling of uncertaintyin LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifyingLM works under uncertainty; and (iv) identify quantitative models and their solution to deal withuncertainty in LM environments by identifying the main variables involved. Hence this article pro-vides a conceptual framework for future LM quantitative modelling under uncertainty as a guide foracademics, researchers and industrial practitioners. The main findings identify that LM under uncer-tainty has been empirically investigated mainly in the US, India and the UK in the automotive andaerospace manufacturing sectors using analytical and simulation models to minimise time and cost.Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce wastein a context of system uncertainty.The research leading to these results received funding fromthe project 'Industrial Production and Logistics Optimizationin Industry 4.0' (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and grant PDC2022-133957-I00 funded by MCIN/AEI /10.13039/501100011033 and by European Union Next Generation EU/PRTR.Rojas, T.; Mula, J.; Sanchis, R. (2023). Quantitative modelling approaches for lean manufacturing under uncertainty. International Journal of Production Research. 1-27. https://doi.org/10.1080/00207543.2023.229313812

Reporting on Internationalization of Operations, GLOBOP: Design and Management of Global Supply Chains

[EN] The research project entitled Design and Management of Global Supply Chains (GLOBOP) focuses on the specific model configuration stages with productive multi location and new productive implantations, which affect the design of productive and logistic systems, and the associated supplier network design. This research project aims to offer a methodological and technical solution for those companies that have begun an internationalization process in recent years (late movers).This research is funded by a Spanish Ministry of Science and Education Project entitled ‘Operations Design and Management in Global Supply Chains (GLOBOP)’ (Ref. DPI2012-38061-C02-01).Mula Bru, J. (2014). Reporting on Internationalization of Operations, GLOBOP: Design and Management of Global Supply Chains. Industrial Engineering and Management. 3(3):1-3. https://doi.org/10.4172/2169-0316.1000131S133

SIMULATION OPTIMIZATION FOR THE INVENTORY MANAGEMENT OF HEALTHCARE SUPPLIES

[EN] This article covers the inventory management of healthcare supplies problem. Based on the mathematical programming model set out by [1], a causal model and a flow chart were developed to outline the simulation model, which was to be later applied to a highly specialized medical institution that performs high-risk heart surgery, such as catheterizations and angioplasties. With this simulation model, a purchases plan with 21 healthcare supplies was obtained that contemplates all the problem¿s restrictions: purchasing policy (safety stock, available budget); the warehouse¿s physical reality (warehouse capacity); characteristics of supplies (useful life, service level); and suppliers (price, capacity and size of lots or rounding value). Different indicators were also considered, such as service levels, costs of purchases, stockouts costs and inventory maintenance costs. The results obtained with the simulation model came very close to the mathematical programming results, but the computing times were considerably shorter.This work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)' (RTI2018-101344-B-I00) and the Operational Program of the European Regional Development Fund (ERDF) of the Valencian Community 2014-2020 IDIFEDER/2018/025.Buschiazzo, M.; Mula, J.; Campuzano-Bolarin, F. (2020). SIMULATION OPTIMIZATION FOR THE INVENTORY MANAGEMENT OF HEALTHCARE SUPPLIES. International Journal of Simulation Modelling. 19(2):255-266. https://doi.org/10.2507/IJSIMM19-2-514S25526619

Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach

[EN] Incidents like the COVID-19 pandemic or military conflicts disrupted global supply chains, causing long-lasting shortages in multiple sectors. This so-called ripple effect denotes the propagation of disruptions to further elements of the supply chain. Due to the severity of the impact that the ripple effect has on revenues, service levels, and reputation among supply chain entities, it is essential to understand the related implications. Given the unpredictable nature of disrupting events, this study emphasises the value of a reactive development of effective recovery policies on an operational level. In this article, a system dynamics model for a supply chain is used as framework to investigate the ripple effect. Based on this model, recovery policies are generated using reinforcement learning (RL), which represents a novel approach in this context. As main findings, the experimental results demonstrate the applicability of the proposed approach in mitigating the ripple effect based on secondary data from a major aerospace and defence supply chain and furthermore, the results indicate a broad applicability of the approach without the need for complete information about the disruption characteristics and supply chain entities. With further refinement and real-world implementation, the presented approach provides the potential to enhance supply chain resilience in practice.The research leading to these results received funding from the project 'Industrial Production and Logistics Optimization inIndustry 4.0' (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and the grant PDC2022-133957-I00 funded by the Spanish Ministry of Science, Innovation and Universities (MCIN/AEI /10.13039/501100011033) as part of the European Union Next Generation EU/RTRP programme. DAS:The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. The source code leading to the findings of this study is available from the corresponding author upon request.Bussieweke, F.; Mula, J.; Campuzano Bolarín, F. (2024). Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach. International Journal of Production Research. https://doi.org/10.1080/00207543.2024.238329

Digital twin for supply chain master planning in zero-defect manufacturing

[EN] Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC¿s master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0.The research leading to these results received funding from the EuropeanUnion H2020 Program with grant agreement No. 825631 Zero Defect Manufacturing Platform(ZDMP) and with grant agreement No. 958205 Industrial Data Services for Quality Control inSmart Manufacturing (i4Q) and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 Optimisation of zero-defects productiontechnologies enabling supply chains 4.0 (CADS4.0).Serrano, JC.; Mula, J.; Poler, R. (2021). Digital twin for supply chain master planning in zero-defect manufacturing. IFIP Advances in Information and Communication Technology. 626:102-111. https://doi.org/10.1007/978-3-030-78288-7_1010211162

INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN

[EN] The general objective of this paper is to simulate a supply chain to assess the effects that different inventory management policies and transport capacity systems have on costs (transport) and service levels (stockouts). This paper specifically aimed to facilitate the decision-making process about planning distribution capacities, particularly when contracting a transport fleet in a supply chain under uncertainty with a 1-year time horizon by evaluating different types of scenarios, which vary depending on availability of vehicles and obtaining vehicles. The system dynamics simulation model was applied to a real-world food supply chain and can be adopted by chains related to diversified cropping systems. The results provide the best decision alternative in terms of costs and inventory levels by considering the transport capacity life cycle, the time to acquire additional transport capacity, the reorder point in days of stock and the target inventory.This work was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: from practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003.Freile, A.; Mula, J.; Campuzano Bolarin, F. (2020). INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN. International Journal of Simulation Modelling. 19(3):434-445. https://doi.org/10.2507/IJSIMM19-3-52343444519