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Omnichannel sales have provided new impetus for the development of catering merchants. The authors thus focus on how catering merchants should manage capacities at the ordering, production and delivery stages to meet customers’ needs in different channels under third-party platform delivery and merchant self-delivery. This is of great significance for the development of the omnichannel catering industry.

This paper formulates the capacity decisions of omnichannel catering merchants under the third-party platform delivery and merchant self-delivery mode. The authors mainly use queuing theory to analyze the queuing behavior of online and offline customers, and the impact of waiting time on customer shopping behavior. In addition, the authors also characterize the merchant’s capacity by the rate in queuing model.

The authors find that capacities at ordering stage and food production stage are composed of base capacities and safety capacities, but the delivery capacities only have the latter. And in the self-delivery mode, merchants can develop higher safety capacities by charging delivery fees. The authors prove that regardless of the delivery mode, omnichannel sales can bring higher profits to merchants by integrating demand.

The authors focus on analyzing the capacity management of omnichannel catering merchants at the ordering, production and delivery stages. And the authors also add the delivery process into the omnichannel for analysis, so as to solve the problem of capacity decision-making under different delivery modes. The management of delivery capacity and its impact on other stages’ capacities are not covered in other literature studies, which is one of the main innovations of this paper.

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.

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.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

A comprehensive review of the literature for the problem of lot‐size scheduling (serial and assembly) considering the uncapacitated problem and complicated capacitated assembly manufacturing structure. Analyses the different solution techniques and findings for each product set.

This paper examines how target costing decisions can be impacted by product and production interdependencies.

Numerical examples are used to investigate the effect that product and production interdependencies have on target costing decisions. Mixed integer programming and simulation are used to model the interrelationships between a product’s cost reduction effort and related decisions such as product mix, pricing, and capacity acquisition. Product and production interdependencies are introduced by evaluating a product with multiple price and demand options, capacity is acquired in large discrete quantities, and resources have economies of scale. Analyses of choices made with and without considering product and production interdependencies are used to evaluate their effects on target costing decisions.

A product’s cost reduction effort cannot be determined independently of other production-related choices, such as product mix, capacity, and price, in the presence of product and production interdependencies.

The findings of this paper underscore the need for additional research to understand the conditions that impair target costing decisions and the economic consequences of suboptimal decisions.

Rather than assessing target costing decisions at the individual product level, these decisions must be evaluated at the portfolio level of the firm’s operations.

Suboptimal target costing decisions impact the products and product mix that the firm chooses to offer, which affects the ability of organizations to effectively achieve their strategic goals.

This paper identifies new limitations to target costing that can help managers understand the technique better and lead to improved target costing decisions.

With the rapid development of the sharing economy in manufacturing industries, manufacturers and the equipment suppliers frequently share capacity through the third-party platform. This paper aims to study influences of manufacturers sharing capacity on the supplier and to analyze whether the supplier shares capacity as well as its influences.

This paper deals with conditions that the supplier and manufacturers share capacity through the third-party platform, and the third-party platform competes with the supplier in equipment sales. Considering the heterogeneity of the manufacturer's earning of unit capacity usage and the production efficiency of manufacturer's usage strategies, this paper constructs capacity sharing game models. Then, model equilibrium results under different sharing scenarios are compared.

The results show that when the production or maintenance cost is high, manufacturers sharing capacity simultaneously benefits the supplier, the third-party platform and manufacturers with high earnings of unit capacity usage. When both the rental efficiency and the production cost are low, or both the rental efficiency and the production cost are high, the supplier simultaneously sells equipment and shares capacity. The supplier only sells equipment in other cases. When both the rental efficiency and the production cost are low, the supplier’s sharing capacity realizes the win-win-win situation for the supplier, the third-party platform and manufacturers with moderate earnings of unit capacity usage.

This paper innovatively examines supplier's selling and sharing decisions considering manufacturers sharing capacity. It extends the research on capacity sharing and is important to supplier's operational decisions.

This study aims to devise generalized unconstrained optimization models for ascertaining the optimal level of product quality and production capacity level by modeling both product price and production cost as a function of product quality. Further, interrelations among investment for quality, product quality and production volume are considered. This study contributes toward the extant research, in that nuances related to price, production volume, and product quality are fused together such that two broad operational strategies of product quality optimization and production capacity optimization can be contrasted.

To achieve the research objectives, the authors evolve unconstrained optimization models such that optimal product quality level and optimal production capacity level can be obtained employing the principles of differential calculus aimed at maximizing the manufacturer's profit. Specifically, nuances related to quality technology and efficiency, and quality loss cost has also been integrated in the integrated model. Thereafter, employing numerical analysis for a generalized product, the detailed workings of evolved models are demonstrated. The authors further carry out the sensitivity analysis to understand the impact of investment for quality onto the manufacturer's profit for both operational strategies.

The research demonstrates that the manufacturer would be better off adopting production capacity optimization strategy as an operational policy, as opposed to product quality optimization policy for the manufacturer's profit maximization. Further, considering the two operational strategies, the manufacturer does not obtain the highest possible theoretical profit when pertinent variables (product quality and production capacity) are set at highest possible theoretical level. This research discusses that in low-volume and high-margin products, it might be useful to adopt a product quality optimization strategy as a production capacity optimization strategy results in significantly high quality loss cost.

The findings of our study have a significant implication for industries such as steel-making, cement production, automotive industry wherein the conventional wisdom dictates that higher level of production capacity utilization always results in higher level of revenues. However, the authors deduce that beyond certain production capacity utilization, striving for higher utilization does not fetch additional profit. This work also adds to the extant research literature, in that it integrates the nuances of product quality, production volume and pricing in an integrative manner.

Co‐ordination of the activities of production units is necessary to realise the required delivery performance in the market. These should not conflict with reaching the production economics objectives of each of the units. Production structure is needed to reduce the complexity and should minimise the loss of potential flexibility. Any structure will have some elements in common — the definition of basic elements (e.g. capacities) as a first step in production control structure design; the introduction of product units and the decomposition of the total production control to Goods Flow Control and Production Unit Control; the relationship of sales and manufacturing and the interference of products and capacities as two main determining factors of the Goods Flow Control structure. The generality of these elements means it is possible to develop a small but relatively complete set of reference structures. A reference structure for Goods Flow Control in a repetitive manufacturing situation is discussed. Its main elements are master planning, material co‐ordination, workload control and work order release.

Modular capacity units enable rapid reconfiguration, providing tactical flexibility to efficiently meet customer demand during disruptions and ensuring sustainability. Moreover, the Physical Internet (PI) enhances the potential of modular capacity in addressing efficiency, sustainability, and resilience challenges. To evaluate the sustainability and resilience advantages of the PI-enabled reconfigurable modular system (PI-M system), this paper studies a PI-enabled sustainable and resilient production-routing problem with modular capacity.

We develop a multi-objective optimization model to assess the sustainability and resilience benefits of combining PI and modular capacity in a chemical industry case study. A hybrid solution approach, combining the augmented e-constraint method, construction heuristic, and hybrid adaptive large neighborhood search, is developed.

The experimental results reveal that the proposed solution approach is capable of obtaining better solutions than the Gurobi and the existing heuristic in a shorter running time. Moreover, compared with the traditional system, the PI only and traditional with modular capacity systems, PI-M system has significant advantages in both sustainability and resilience.

To the best of our knowledge, this study is the first to integrate the PI and modular capacity and investigate sustainability and resilience in the production-routing problem.