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The exogenous cycles and population dynamics of the community at Penrith, Cumbria, England, have been studied (1557-1812) using aggregative analysis, family reconstitution and time series analysis. This community was living under marginal conditions for the first 200 years and the evidence presented is of a homeostatic regime where famine, malnutrition and epidemic disease acted to regulate the balance between resources and population size. This provides an ideal historic population for an investigation of the direct and indirect effects of malnutrition. Throughout the period studied, a short wavelength oscillation in grain prices was apparently the major external factor that drove exogenous cycles in mortality, birth rate, and migration. In particular, the different responses of children to variations in food supply are emphasised; fluctuations in poor nutrition correlated significantly with the variations in mortality rates for infants (probably indirectly during pregnancy and directly during the first year of life) and for young children (via susceptibility to lethal infectious diseases). Migratory movements contributed to the maintenance of homeostasis in the population dynamics. A medium wavelength cycle in low winter temperatures was associated with a rise in adult mortality which, in turn, promoted an influx of migrants into this saturated habitat. A model incorporating these interacting associations between vital events and exogenous cycles is presented: grain prices were an important density-dependent factor and constituted the major component of the negative feedback of this population and drove the exogenous, short wavelength mortality cycles. Cycles of births and immigration provide a positive feedback for the build-up of susceptibles and the initiation of smallpox epidemics and increased population size.

"Theories of trade and migration explain the distribution of individuals among regions based on private good productivities. The theory of local public goods (LPG's) uses collective good consumption economies to explain the size and composition of communities. This essay combines the two theories, to explore regional population heterogeneity and stability. Assuming that individuals must consume and produce in the same jurisdiction, the paper examines the nature of efficient allocations, the tensions between the private and public incentives, the nature of the equilibrium (if any) which migration among jurisdictions will generate, and how such equilibrium will depend on tax rules for sharing the costs of the LPG.", (excerpt)

Purpose: This article focuses on the problem of deciding the annual investment that a company should allocate to the rehabilitation of its water distribution and sanitation networks. The objective is to find the investment amount necessary to maintain an adequate quality and sustainability of the infrastructure. It is not a simple decision, as there are different criteria that may be of interest to the managing company. In this paper, we consider four criteria related to the reliability of individual pipes and the complete network. These indicators are the infrastructure value index, the average age of network pipes, the risk index and the probability of failure. Design/methodology/approach: A methodology is proposed to estimate the best annual investment by analysing the evolution of these indicators. Concretely, two strategies are tested. The first one is a minimax-based approach that seeks a balanced solution for all the indicators. The second one is named as minimal deviation strategy and seeks to minimise the deviation of all the indicators in the last year of the time horizon compared to the initial year. Findings: In order to obtain a realistic sample of the performance of both strategies, 201 scenarios, i.e. 201 different annual investments have been tested. According to the first strategy, an annual investment of 55.5 M€ is the best option, while the minimal deviation strategy presents an annual investment of 39.5 M€ as the best decision. The study reveals that different evaluation functions lead to completely different annual investment. Concretely, the minimax evaluation function is more conservative than the minimal deviation strategy. Originality/value: This study proposes an original approach to address the decision problem of investments in asset management. To the best of the authors’ knowledge, it is the first attempt to treat that problem using this kind of evaluation functions. However, it is still a simple proposal and there are many options to continue this line of research.

Purpose: Automatic grouping of data according to certain characteristics is made possible by clustering algorithms, which makes them an essential tool when working with large datasets. However, although they are unsupervised tools, they generally require the specification of the number of clusters to be formed, k, a task that may be simple for a human, but quite complex to automate. Despite the most commonly used k-value selection techniques offer acceptable results, they are not without shortcomings, suggesting that there is ample room for improvement. This paper briefly introduces clustering techniques, discusses the main shortcomings of conventional k-value selection techniques and examines the advantages and limitations of nine promising alternatives presented in recent years. Design/methodology/approach: An evaluation of the main shortcomings of classic k-value estimation techniques has been carried out, and the newest proposals have been explained and compared. Findings: New k-value estimation indices and methodologies proposed by authors guarantee better results, extending the use of these techniques to large volumes of data, and complex shapes and structures. However, no generical methodology able to overcome all the described shortcomings has still been developed. Research limitations/implications: This research is limited to the newest developed techniques for k-value estimation, including proposals published since 2019. Older proposals have not been considered, as the newest ones overcome the former’s shortcomings. A k-value estimation techniques review published in 2019 is cited in the test as a base reference. Practical implications: Although the examples listed in the text apply to industry, the techniques described and discussed in this review are applicable to any area of science that can benefit from the use of clustering techniques. Originality/value: To date, there has been no paper comparing the new k-value estimation techniques. Although there are literature reviews comparing the classical methods, these methods are nowadays nearly obsolete due to the complexity of the data usually faced.

Purpose: This research develops a model integrating inventory with order pick-up that considers both the vendor’s capacities and the pick-up time for the multi-vendor single-buyer (MVSB) system. Design/methodology/approach: A model of integrating inventory with pick-up of order considering both the vendor’s capacities and the pick-up time for the MVSB’s system is formulated as a mixed integer non-linear programming, and a heuristic-metaheuristic algorithm is proposed to solve the problem. This model applies a decomposition approach to minimize total relevant costs, which consist of the buyer’s ordering, vendor’s set-up, vendor’s and buyer’s holding, and pick-up. Findings: This research proposes an inventory model integrated with the pick-up of orders, considering both the capacity of the vendor and the pick-up time for the MVSB system. This research also proposes a hybrid heuristic-metaheuristic algorithm that can obtain a solution in realistic computational time. Originality/value: This paper proposes a mathematical model for the inbound inventory routing problem, considering both the capacities of vendors and the pick-up time for the MVSB system.

Purpose: This paper aims to examine the role of collaboration, knowledge transfer, service partner development, information sharing and logistics integration on supply chain agility. Design/methodology/approach: Data were collected from 405 official motorcycle service partners in Indonesia using questionnaires and were analyzed using structural equation modelling. Findings: Collaboration has a significant impact on knowledge transfer and information sharing. Information sharing and logistics integration as mediation to improve supply chain agility. Likewise, knowledge transfers and service partner development also as mediation to enhance supply chain agility. As a result, collaboration has no significant direct impact to supply chain agility. Research limitations/implications: Given the state of the sampling refers to specific industry, so the generalization of the results will be limited. Practical implications: The proposed model provides insight for managers on how collaboration, knowledge transfer, service partner development, information sharing and logistics integration affect supply chain agility. Using measurement items of this study, managers can determine and evaluate the current state and formulate strategies to improve their supply chain capabilities. Originality/value: The contribution of this study lies in investigating the role of dynamic capabilities for PSS offering to improve supply chain agility. This study provides benefits for academics and industry by filling the gap of the nascent study in Product–Service Systems and supply chain agility.

Purpose: The objective of this work is to fulfil a strategic requirement in Egypt’s agriculture industry by establishing a fresh produce supply chain network (SCN) that manages the collection, processing, packaging, and distribution of products. Design/methodology/approach: A two-phase approach is proposed. In the first, a network of food aggregation hubs is strategically located across the country for the collection, consolidation, and distribution of products. This is accomplished by modeling and solving a cost minimization dynamic facility location-allocation (FLA) problem using a hybrid binary particle swarm optimization (BPSO) algorithm. The second phase of the approach is to complement the hub FLA decision with optimal fleet size, transportation schedules, and routing decisions. This is achieved by solving the split-delivery vehicle routing problem (SDVRP) using a hybrid ant-colony optimization (ACO) algorithm, considering positioning loading constraints, and shelf-lives of products. Findings: There is a strong correlation between the geographical locations and capacities of the established hubs, and the proximity of supply points and the populations in the demand areas. In addition, accounting for spoilage of products has a significant effect on network design, and collection and distribution decisions. Practical implications: Establishment of the intended SCN can reduce the proportion of wasted product during transit, and improve the quality of the delivered product. Social implications: Establishment of the SCN will increase the exposure of small farmers to wider markets, and hence their return and standard of living, and potentially reduce the prices for the final customer. Originality/value: This study is the first attempt to establish an efficient fresh produce supply chain network in Egypt. In addition, the proposed solution approach considered a multitude of problem characteristics, simultaneously for the first time.

Purpose: This paper investigates a batch scheduling problem where pre-processing is required for parts before processing, considering time-changing effects from part deterioration and operator learning-forgetting. Design/methodology/approach: A mathematical model was developed with the decision variables of the number of batches, the number of pre-processings, batch sizes, and the schedule of processes and pre-processings to minimize total actual flow time. Different numbers of batches were gradually tried and increased until the objective function stopped improving. The minimum number of pre-processings that resulted in a feasible solution was examined at each number of batches. Findings: Our experiment showed that: (1) A faster operator learning led to a lower optimal number of batches and a lower total actual flow time, (2) A faster part deterioration brought a higher number of batches and a higher total actual flow time, (3) The model minimized the number of pre-processings by only scheduling pre-processings before the operations at machine 1, and (4) The model divided the parts into small batches to prevent increased processing time due to part deterioration. Research limitations: The research did not consider multi-due date and multi-item system which require pre-processings with different times and capacities. Practical implications: Production managers should assign fast learning operators to shorter batches and faster deteriorating parts. Originality/value: This research was the first to consider pre-processing in batch scheduling.

Purpose: This paper investigates the impact of special replenishing modes within a 2-echelon inventory system under seasonal demand. Within this system, a periodic-review base-stock policy is employed. Two special replenishing modes are considered: emergency and transshipment in addition to a regular mode. The regular mode serves as the primary replenishment method, while emergency and transshipment modes, characterized by shorter lead times and higher costs, are reserved to prevent stockouts. It specifically examines the differences in outcomes between static and dynamic ordering policies for these special modes. Design/methodology/approach: Methodologies to determine static and dynamic policies of two special replenishments: emergency and transshipment are proposed. Both emergency and transshipment replenishments are based on. A simulation method was used to evaluate the proposed policies. Findings: Special modes can be used to maintain service level while utilizing a lower safety stock, thereby reducing overall holding cost. The 60% higher frequency of emergency orders under static policies compared to dynamic policies leads to a lower number of transshipment orders. For short cycles with high per-period demand variability, the gap between static and dynamic policies shrinks, making static policies a viable, less-complex alternative. Levels of demand fluctuations between periods impact a policy choice. While dynamic policies may not provide a distinct advantage over static policies in low-fluctuation scenarios, they can yield cost savings in high-fluctuation environments, albeit with increased effort. Practical implications: The result from this paper can be adopted to a 2-echelon inventory system with multiple replenishing modes under seasonal demand. It can help inventory managers choose the appropriate policy for their situation. Originality/value: This paper provides managerial insights regarding the circumstances in which static policy or dynamic policy should be applied and explores the relationship between regular and special replenishing modes in various circumstances.

Purpose: This paper presents the concept of an Employee Suggestion System (ESS) that integrates a strategy originated from Neuro-Linguistic Programming with application in Coaching (Disney Strategy) to face the human challenges of Industry 4.0. Design/methodology/approach: A four-phase methodology was followed, starting with a systematic literature review of the ESS to obtain a theoretical perspective about this concept and its characteristics. Subsequently, 30 interviews were carried out to recognize the ESSs of three partner companies and, as well as to perceive the receptivity of the new concept of ESS. Finally, the concept of the system was modelled, prototyped and tested and combines the Japanese (Kaizen Teian) and American ESS (Kaizen Teian adapted to the western industry) approaches. Findings: Given the existing systems in organizations, the platform presented brings more maturity to the suggestions made (through the Disney strategy applied in Coaching), greater visibility of their status and evaluation, and greater promotion of workforce engagement (through the promotion of voice behaviour). At the same time, it supports the collection of tacit ideas from employees, preserving organizational knowledge and, therefore, a source of competitive advantage. Originality/value: This paper presents a digital tool with Lean origins, which includes Coaching principles, essential in empowering the workforce (through the voice behaviour) and preserving organizational knowledge. It is a platform built in a way adapted to today's Lean shop floor and intends to prove itself as a resource to promote happy, engaged and committed employees.

Purpose: The fourth industrial revolution has pushed companies, and even seaports, to adapt the way they operate, to remain competitive in the marketplace. Thus, ports are going from analog to digital, and are on their way to achieving the status of smart ports. So, the purpose of this paper is to analyse what is already known about smart ports. Design/methodology/approach: A systematic literature review was conducted to identify the contributions of Industry 4.0 in seaport logistics (using the PRISMA method), based on studies present in the SCOPUS and Web of Science databases, both at the bibliometric and content level. Findings: Through this method it was possible to identify the most used technology of industry 4.o in the smart ports context, the benefits, and challenges of the digital transformations in seaports, and also the world’s smartest port cities. Curiously, from the universe of studies found, few articles are concerned about the impacts of smart ports on the human factor and on the cities and municipalities where the port is located. Originality: This study presents contributions both at the theoretical level (which increases knowledge about the reality of some of the ports considered smart), and at the practical level (which aims to help/encourage other ports to start/continue to become increasingly smart.

Purpose: Sustainability and resilience have emerged as two important topics. Consumers and governmental entities are becoming increasingly aware of the existing environmental and social issues, motivating companies to adopt more sustainable practices. In addition, the uncertainty and unpredictability that characterize today's competitive environment force companies to be able to handle disruptions efficiently. Despite this, when looking at some classes of problems in literature, cost is still the most, or even the only, objective addressed. This work analyses the integration of sustainability and resilience concerns in a class of problems that is often found in logistics, the Location-Routing Problem (LRP). Design/methodology/approach: A multi-objective LRP is addressed with the following objectives: minimization of the total cost of the logistics network, minimization of the environmental impact of CO2 emissions, and maximization of the resilience of the distribution network. Aiming to explore the effect that designing a more sustainable and resilient distribution network can have on its operating costs, the ?-constraint method is applied. Findings: Results show that resilience and environmental impacts, from a practical point of view, should not be considered in isolation, as they can have significant impacts on costs. Nevertheless, it is conceivable to design logistics networks that ensure a good compromise between operating costs, environmental impacts, and resilience to disruptions. Originality/value: This paper is an exploratory study addressing a new type of location-routing problem which, besides the usual cost objective, considers sustainability and resilience as objectives. The paper evaluates the sensitivity of the costs of logistics networks to the improvement of their resilience and sustainability. A new multi-objective formulation is proposed for this class of problems, tested in instances based on real-world data. The paper may provide important managerial insights for designing sustainable and resilient logistics networks.

Purpose: This study aims to empirically analyze the effect of absorptive capacity, green dynamic capability, and green manufacturing practices (GMP) on green innovation performance (GIP). Design/methodology/approach: A quantitative approach was employed in this study, with data collected through a questionnaire and analyzed for hypothesis using the partial least squares structural equation model. In order to enhance the depth of the analysis, in-depth interviews were conducted with experienced practitioners from three selected companies implementing green innovations. Findings: Empirical findings showed that reconfiguring capabilities positively affects GIP. Realized absorptive capacity positively affects GMP but does not affect GIP. Then, realized absorptive capacity positively impacts sensing and reconfiguration capabilities. GMP mediates the relationship between realized absorptive capacity and GIP. This study reveals the two pathways through which green dynamic capabilities affect realized absorptive capacity and GIP. Research limitations/implications: This study has yet to consider potential variations in company ownership, applied technology, and industry type that could lead to differences in green innovation characteristics. This study employed a small sample size and a Likert-scale questionnaire, possibly leading to respondent perception bias (although this is unlikely). Practical implications: The results of this study offered valuable insights into how companies developed dynamic capabilities to address essential green requirements in their product and processes. Thus, companies can prepare all available resources to adapt to various environment-related changes. Social implications: Companies with high green innovation performance will produce environmentally friendly products, conduct industrial activities without damaging the environment, and ultimately create a sustainable industry. Originality/value: What distinguishes this study is the division of green dynamic capabilities into three independent capabilities: SenCap, SeiCap, and Reconf, which had been described as a single capacity in previous studies. Furthermore, this study emphasizes GMP's position between absorptive capacity and green dynamic capability with GIP.

Purpose: Although the authors of the Demand Driven Material Requirements Planning (DDMRP) argue that the method DDMRP is the solution to the limitations of traditional production management methods, its capacity management system remains unclear. Since DDMRP operates at infinite capacity, it is important to consider a capacity management approach to avoid under- or overloading production workshops. Design/methodology/approach: We propose a new dynamic capacity management approach for the DDMRP method. Our approach is based on the calculation of the anticipated workload, using DDMRP stock buffers and considering customer order spikes. Considering a real industrial case, we compare the proposed approach to a static one and a dynamic approach from the literature. Findings: The analysis of the results, supported by a two-way ANOVA, indicates that the proposed capacity management approach outperforms the performance of the other two approaches by maximizing the resource loading rate while ensuring a high customer service level. Originality/value: The originality of the article comes on the one hand from the capacity adjustment module by calculating the anticipated workload, and on the other hand from the comparison of this approach with two others, one of which comes from the literature.

Purpose: This paper aims to identify complementary strategies for urban freight logistics in a low emission zone (LEZ) that can be implemented in downtown Medellin, Colombia. The strategies will be executed by identifying the characteristics and problems of the business establishments in the area and the entrepreneurs’ opinions. Design/Methodology/Approach: First, a literature review is performed in order to identify global and complementary strategies for the future LEZ, which is the study subject. Then, a “pilot” zone (a main corridor) of downtown Medellín is selected and the business establishment of the area are identified in order to spot problems and evaluate possible strategies for the future LEZ. This identification and evaluation phase is conducted through an interview with logistics managers in 105 establishments selected in the corridor for analysis. Findings: One of the outstanding findings concerns the specificity of the logistics and mobility problems in downtown Medellin, which is located in Colombia, a developing country. The main problems are traffic congestion, lack of regulation and control in loading and unloading areas, as well as inadequate infrastructure for these operations. These obstacles have a significant impact on business activities and air quality in the area. The proposed solutions to complement the LEZ strategy for freight transport focus on improving regulation, establishing specific loading and unloading areas, exploring sustainable transportation options such as the cargo bike, and supporting small and medium-sized businesses. This emphasizes the complexity of the situation and the need for a comprehensive approach to address these logistical and environmental challenges. Originality/Value: This study focuses on the analysis of urban freight logistics strategies that complement the traffic flow restriction policies imposed by low-emission zones. Unlike other studies that address these two issues independently, this paper specifically examines how to improve a LEZ as a comprehensive strategy to reduce the environmental impact of freight transport. In addition, it is noted that most publications tend to focus on passenger mobility strategies, which highlights the potential for research in the freight logistics field.

Purpose: This paper aims to explore and describe how companies manage the level of standardisation of improvement practices in a multisite context. It seeks to explain the managerial strategies applied to change the standardisation level in manufacturing companies with multiple production sites worldwide. Design/methodology/approach: This paper collects data through interviews, observations and company documents from a large multinational producing company and, specifically, from of the largest production sites in the company. The research design resembles a grounded theory approach by being reflexive and open to emerging themes. The standardisation strategy was analysed at a company that strived to increase the standardisation of problem-solving practices within about 20 production sites as part of their corporate lean programme. Findings: Several managerial tools were applied at the corporate level to increase the standardisation level of problem-solving practices, such as developing standards and a company-specific toolbox aligned with an in-house maturity model. In addition, deploying change leaders and global implementation targets enabled audits and progress. However, consequences at the production-site level became minor adaptations of standards, the design of training models as a "roll-out", and a resource-demanding implementation process. Originality/Value: This paper empirically demonstrates strategic tools that corporate management teams apply to influence the company's standardisation level of practices. The study describes the purpose and consequences of the design of the toolbox, maturity model, training model, and implementation targets, which aims to simplify the complex task of managing standardisation in a corporate group. By applying a knowledge-based view, four processes (i.e. adaptation, integration, upskilling, and learning) were identified to improve the management strategies in multisite contexts.

Purpose: This paper aims at introducing the importance and the relevant concepts and tools for including gender dimension in teaching, with a special focus in the Operations Management knowledge field. Design/methodology/approach: The methodology is based on a literature review and on the knowledge and experience of the authors in teaching in the field of Operations Management and designing and implementing trainings and pilot programs for including gender dimension in teaching and. Findings: Even if there already exist some general guidelines for including gender dimension in teaching and in some countries the law requires the inclusion of gender dimension in high education, the effective inclusion of gender dimension in teaching is still in a very preliminary stage. Moreover, there are not specific studies focused on the Operations Management field. Research limitations/implications: Even if the authors have wide experience in the field, the proposals have not been applied to many courses so there is no empirical evidence of the achieved results. Practical implications: The paper can be useful for people teaching in Industrial Engineering and Operations Management courses’, as it gives not only some general guidelines and concepts but also specific examples that can be directly applied to their courses. Social implications: Including gender dimension in Industrial Engineering and Operations Management courses, which are still male dominated, can help achieving the SDG 5, which is focused on achieving gender equality and empowering all women and girls. Specifically, including gender dimension in teaching allow breaking stereotypes, changing the image of the knowledge field, empowering female students and giving both men and women tools for including the gender dimension in their professional careers. Originality/value: There exist some general guidelines for including gender dimension in teaching but, to the best of our knowledge, there is no any specific work that is focused on the Operations Management knowledge field.

Purpose: This study presents a systematic literature review that provides a broad and holistic view of how machine learning can be used and integrated to enhance decision-making in various areas of the supply chain, highlighting its combination with other techniques and models. Design/methodology/approach: An exhaustive literature review used three sets of keywords in the Scopus and Web of Science (WoS) databases. Through a rigorous filtering process, 70 articles were selected from an initial total of 410, focusing on those that specifically addressed the intersection of machine learning and decision-making in supply chain management. Findings: Machine learning has proven to be an essential tool in the supply chain, with applications in inventory management, logistics, and transportation, among others. Its integration with other techniques has led to significant advances in decision-making, improving efficiency in complex environments. Combining machine learning methods with traditional techniques has been particularly effective, and integration with emerging technologies has opened up new application possibilities. Originality/value: Unlike previous studies that focused on specific areas, this study offers a broad perspective on the application of machine learning in the supply chain. Additionally, combining machine learning techniques with other models is highlighted, representing added value for the scientific community and suggesting new avenues for future research.

Purpose: This paper studies the problem that occurs on material purchase quantity in price uncertainty situation. Larger buying quantity when the price at high will increase the purchase amount while smaller buying quantity could risk the inventory level. The decision on the purchase quantity of a cycle takes future price as input from price prediction output. Design/methodology/approach: This paper examines five price prediction models, Classification and Regression Tree (CART), Random Forest Regressor (RFR), Support Vector Regressor (SVR), Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short Term Memory (LSTM) to predict four Petrochemical products, Linear Low Density Polyethylene (LLDPE), Low Density Polyethylene (LDPE), Biaxially Oriented Polypropylene (BOPP) and Cast Polypropylene (CPP) using dataset built from weekly datapoints from January 2020 to June 2023. The most performing model is validated with data from July 2023 to September 2023 where the prediction result is fed into Linear Programming Simplex method to minimize the amount of purchase by making advanced or postpone orders. Findings: Result that RFR performs higher at most products tested, while SVR performs higher in LDPE product. The fitting of RFR and SVR models prediction, as predicted price to Linear Programming that decides optimum purchase quantity, delivers a total 2.2% of purchase amount reduction compared to original purchase quantity reflecting base scenario issued by the planner. Research limitations/implications: This study does not include additional prediction factors such as freight cost and the hyperparameters tuning studies on the existing factors. Originality/value: The novelty of this paper is prediction value is followed up by an optimization model that would guide the Procurement team decisions for future anticipation because imported raw materials should be purchased ahead of time. This research will provide a scientific approach input that would counterbalance or strengthen decision making that is typically made by individuals owning years of experience. This combined approach is rarely researched and has not been done to polymer products.

Purpose: The buffer allocation problem (BAP) arises in the design of production systems; it involves analyzing and defining the optimal distribution of buffers within a production line. This paper presents a BAP formulation in a parallel series line from a cup sublimation process with unreliable operating conditions. The main objective of this study is to develop a new BAP solution proposal, considering the optimization of the OEE indicator used in Lean Manufacturing. Design/methodology/approach: The BAP was analyzed under an optimization approach from two different criteria: firstly, the maximization of the OEE indicator (Overall Equipment Effectiveness) utilized in Lean Manufacturing, as well as the maximization of the average production rate (Throughput). The case study involves unreliable operating conditions. Process times, and timeframes between failures and repairs, consider normal distribution functions. The evaluation method employed in the study includes the use of simulation meta-models built from experiment designs and production line simulations; on the other hand, the nonlinear GRG algorithm is used to solve the mathematical models. Findings: In the study carried out, it is shown that the OEE indicator can be affected when more buffers are allocated than necessary, hence it is important to calculate and establish the best configuration for them through an analysis such as the one proposed in this document. Research limitations/implications: The research is limited to a case study of an unreliable production line from a cup sublimation process. Practical implications: The proposed solution established in this study can be used in other production lines with configurations different from the one analyzed, considering the optimization criterion of the OEE indicator. Originality/value: Seeking that the allocation of buffers within the production line improves the OEE indicator is something new in the literature, therefore, the results achieved in this research become even more relevant.