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Capacity is generally considered only in one sense – to provide the means for producing a product or service. Defines capacity as serving two functions – to provide the means for producing a long‐run, stable level of a good or service, and to provide the means to adapt to fluctuations in demand over the short run and intermediate runs. Given this definition, develops the implications for strategic capacity planning and offers a model for firms to carry out this planning. Presents examples of where this model has been followed and discusses the implications.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

The purpose of this paper is to present a literature review that aims to provide insight into the characteristics and effectiveness of supply chain visibility (SCV), as well as to identify metrics that capture these aspects in business processes.

A systematic review of the supply chain literature is conducted to identify the characteristics and the effectiveness of SCV. The synthesis of SCV effectiveness and its metrics are based on the process-oriented approach which relates the effectiveness of SCV to improved business performance.

This study reveals that the characteristics of SCV can be captured in terms of the accessibility, quality, and usefulness of information. The benefits of SCV are found to extend beyond improvements to operational efficiency of business processes or to the strategic competencies of an organization.

This study underlines that clear agreements between all players involved in the SC can help to solve problems caused by information completeness (type and amount of information), and unlock the full potential of SCV projects.

By using a process-oriented approach, this review provides a comprehensive explanation of the functions of SCV, as well as its first-order effects, in terms of automational, informational, and transformational characteristics.

The food industry is directly related to the health of humans and society and also that little attention has been paid to the assessment of sustainable supply chain risk management in this area, this will be qualified as an important research area. This study aims to develop a framework for assessing the sustainable supply chain risk management in the realm of the food industry (confectionery and chocolate) with a case study of three generic companies denotes as A1–A3. The proposed risk management was evaluated in three aforementioned manufacturing companies, and these three companies were ranked by the Fuzzy-Weighted Aggregated Sum Product Assessment (F-WASPAS) method in EXCEL.

The evaluation was carried out using integrated multi-criteria decision-making methods Best-Worst method (BWM)-WASPAS. Via an extensive literature review in the area of sustainable supply chain, sustainable food supply chain and risks in this, 9 risk criteria and 59 sub-criteria of risk were identified. Using expert opinion in the food industry, 8 risk criteria and 39 risk sub-criteria were identified for final evaluation. The final weight of the main and sub-criteria was obtained using the F-BWM method via LINGO software. Risk management in the sustainable supply chain has the role of identifying, analyzing and providing solutions to control risks.

The following criteria in each group gained more weight: loss of credibility and brand, dangerous and unhealthy working environment, unproductive use of energy, human error, supplier quality, quality risk, product perishability and security. Among the criteria, the economic risks have the highest weight and among the alternatives, A3 has obtained first ranking.

Modeling of risk for the food supply chain is the unique contribution of this work.

The purpose of this paper is to explore different aggregate production planning (APP) strategies (inventory levelling, validation of the workforce and flexible production alternatives: overtime and/or outsourcing) by using a system dynamics model in a two-level, multi-product, multi-period manpower intensive supply chain (SC). Therefore, the appropriateness of using systems dynamics as a research method, by focusing on managerial applications, to analyse APP policies is proven. From the combination of systems dynamics and APP, recommendations and action strategies are considered for each scenario to understand how the system performs and to improve decision making on APP in the SC context.

The research design analyses a typical factory setting with representative parameter settings for five different conventional APP policies – inventory levelling, workforce variation, overtime, outsourcing and a combination of overtime and outsourcing – through deterministic systems dynamics-based simulation. In order to validate the simulation model, the results from published APP models were replicated. Then, optimisation is conducted for this deterministic setting to determine the performance of all these typical policies with optimal parameter settings. Next, a Monte Carlo stochastic simulation is used to assess the robustness of such performances in a variety of demand settings. Different aggregate plans are tested and the effect that events like demand variability and production times have on the SC performance results is analysed.

The results support the assertion that the greater the demand variability, the higher the flexibility costs (overtime, outsourcing, inventory levelling, and contracts and firings). As greater inter-month oscillations appear, which must be covered with additional alternatives, the optimum number of employees must be determined by analysing the interchanges and marginal costs between capacity oversizing costs (wages, idle time, storage) and the costs to undersize it (penalties for lowering safety stocks, delayed demand, greater use of overtime and outsourcing). Accordingly, controlling the times to avoid increased costs and penalties incurred by delayed demand becomes an essential important task, but one that also depends on the characteristics of this variability.

This paper has developed a modelling approach for APP in a manpower intensive SC by applying system dynamics. It includes a simulation model, the analysis of several scenarios, the impact on performance caused by variability events in the parameters, and some recommendations and action strategies to be subsequently applied. The modelling methodology proposed can be employed to design-specific models for each SC.

This paper proposes an APP system dynamics approach in a two-level, multi-product, multi-period manpower intensive SC for the first time. This model bridges the gap in the literature relating to simulation, specifically system dynamics and its application for APP. The paper also provides a qualitative description of the various pros and cons of each analysed policy and how they can be combined.

Develops a linear programming model for integrated production planning based on the practice of a major Canadian steel making company. Considers the entire planning activity in the company as an integrated process involving a number of closely related sub‐functions, such as raw material purchasing, semi‐finished product purchasing and production, and capacity allocation, as well as finished product production and distribution. The mathematical programming model takes into account production costs, product throughput rates, customer demands, sales prices and facility capacities for optimal production planning. Presents a numerical example based on realistic system structure and practical planning data to illustrate the model. Computation results and analysis show that the integrated methodology is a feasible and practical approach for steel production planning.

The purpose of this study is to explore the link between information systems (IS) capabilities, supplier integration and cost performance in the service context. Specifically, it empirically investigates how supplier integration meditates the relationship between three dimensions of IS capabilities and cost performance in service firms.

A survey of 156 UK service firms was conducted and the data analyzed to determine the role of supplier integration in mediating the effects of IS capabilities on firms' cost performance. The research model was tested using structural equation modeling (SEM), and the neural network model was used to rank the relative influence of significant predictors obtained from SEM.

The results confirmed that supplier integration fully mediates the effects of information technology (IT) for supply chain activities and flexible IT infrastructure on cost performance and partially mediates the effect of operations manager's IT knowledge on cost performance. The results showed that operations manager's IT knowledge is the strongest predictor of supplier integration.

This study takes a step toward quelling concerns about the business value of IS, contributing to the development and validation of the measurement of IS capabilities in the service operations context. Additionally, it adds to the emerging body of literature linking supplier integration to the operational performance of service firms.

The main purpose is to describe and analyse the impact that the implementation of an advanced planning system (APS) has on the tactical planning level at a steel processing company. This is done in terms of analysing changes in the tactical planning processes, effects on company performance, and how the APS is used in a practical planning context.

This research is based on a longitudinal case study in the process industry. The case company, a high‐end steel producer, has been studied during several years using a combination of data sources: literature reviews, interviews, archival records, and also attendance at meetings, workshops, seminars, etc.

This case study points to the fact that implementing an APS and reorganizing the planning department and the planning processes are mutually dependent. The positive effects at the tactical planning level (in terms of service levels, fast and reliable order promises, more accurate forecasts) could not have been realized without the APS. On the other hand, the APS could not have been effectively utilized without the organizational change.

The results presented in this paper are based on a single case study, but in the context of our literature review and other case studies the findings are still valid and an important step towards better understanding of the practical use of APSs.

The process descriptions, lessons learnt, and issues encountered in case studies like this should be helpful to practitioners on their way to implement APSs, and companies seeking new ways to improve their planning can use this research to investigate the use of an APS.

Studies on the practical use of standard APS software are still scarce. As such this paper provides enhanced knowledge and understanding on the use of APSs in industry settings.

This paper presents the results of a panel of experts, made up of academics in the field of operations and supply chain management, enterprise requirement planning software developers, and end‐users, whose work has aimed at defining the main features that characterize an enterprise modeller for the fashion industry. The characteristics required by the enterprise requirement planning enterprise modeller have been identified, with specific attention to the production planning and control module. Because of the peculiarities of this line of business, it is widely recognized that both vendors and buyers would benefit from such a tool. For the formers, the availability of a pre‐customized reference model would represent a competitive advantage in the marketplace, for the latter, on the other hand, it would enhance the effectiveness, the efficiency and the likelihood of success of the enterprise requirement planning implementation project.

Risks can easily disrupt the demand–supply match targeted by sales and operations planning (S&OP). As surprisingly little is known of how organizations identify, assess, treat and monitor risks through tactical planning processes, this paper zooms in on the S&OP set-up and process parameters to explore how risks are managed through S&OP.

A multiple case study analyzes the S&OP processes of seven organizations in the process industry, drawing on 17 in-depth interviews with high-ranking representatives, internal and external documents, and a group meeting with participating organizations.

The study finds that organizations proactively design their S&OP based on their main risk focus stemming from the planning environment. In turn, such designs proactively support organizations' risk identification, assessment, treatment and monitoring through their S&OP execution. Reactively, a crisis S&OP meeting – making use of the structure of S&OP – can be used as a risk-treatment tool, and S&OP design can be temporarily adapted to deal with emerging risks.

This study is among the first to empirically elucidate risk management through S&OP. S&OP design, execution and adaption are identified as three interconnected strategies that allow organizations to manage risks. The design enables risk management activities in the monthly execution of S&OP. The reactive role of S&OP in risk management is particularly novel.