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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.

The purpose of this paper is to describe and analyze the business processes of a bank by considering a graphical and a descriptive view in order to find critical processes and to improve them.

A methodology with which the business processes are modeled based on the flow diagram (FD) and integrated definitions (IDEF0) techniques.

Detects the descriptive and graphical view of the main critical business processes of a bank. Allows to propose correct and effective improvements to them and considers different views and degrees of detail.

This study considers a static view of the processes. A future line of research includes the simulation of the critical business processes of the bank detected with the modeling techniques herein proposed.

The business process modeling techniques apply to one subsidiary of a bank company, and can thus be extended to all subsidiaries to compare their performance and to apply the improvements by considering global objectives.

This paper proposes a structured modeling to capture the critical business processes of a bank through well‐known techniques, such as the FDs and IDEF0, and helps compare and detect these relevant aspects for the bank.

This paper aims to provide a social network‐based model for improving knowledge management in multi‐level supply chains formed by small and medium‐sized enterprises (SMEs).

This approach uses social network analysis techniques to propose and represent a knowledge network for supply chains. Also, an empirical experience from an exploratory case study in the construction sector is presented.

This proposal improves the establishment of inter‐organizational relationships into networks to exchange the knowledge among the companies along the supply chain and create specific knowledge by promoting confidence and motivation.

This proposed model is useful for academics and practitioners in supply chain management to gain a better understanding of knowledge management processes, particularly for the supply chains formed by SMEs.

The purpose of this research paper is to present a conceptual model for collaborative forecasting management (CFM) developed within a European project's context.

This research follows a constructivist approach. After analysing different frameworks relating to such a thematic, and once their main weaknesses were identified, it was decided to develop the CFM framework that provides collaborative enterprises with a simple, efficient, robust and useful framework.

The paper describes a CFM model, which finds a place in the extended collaborative supply chain context, where several supply chains collaborate to deliver a product or service pack to final customers, thus forming the so‐called meta‐value chain, creating an extended value proposition.

Even though this proposal has been tested in one of the project's pilots, obtaining good results in terms of achieved and potentially achievable advantages, it should be tested further by implementing it in other collaborative organisations with the main objective of enriching and extending it to other sectors.

The CFM proposal came up as the result of applying innovative ideas between collaborative organisations supported by e‐collaboration practices, creating new paths for other disciplines.

This paper seeks to provide a social network‐based model for improving knowledge management in multi‐level supply chains formed by small and medium‐sized enterprises (SMEs).

This approach uses social network analysis techniques to propose and represent a knowledge network for supply chains. Empirical experience from an exploratory case study in the construction sector is also presented.

This proposal improves the establishment of inter‐organizational relationships into networks to exchange knowledge among the companies along the supply chain and to create specific knowledge by promoting confidence and motivation.

This proposed model is useful for academics and practitioners in supply chain management to gain a better understanding of knowledge management processes, particularly for supply chains formed by SMEs.

This paper seeks to propose an overall model of collaborative forecasting for networked manufacturing enterprises.

Contributions by several authors to collaborative forecasting have been analysed from different viewpoints. A collaborative‐forecasting model for networked manufacturing enterprises has been proposed and validated by means of a simulation study.

This model significantly reduces the inventory levels of the whole network and improves customer service.

Simulation experiments were done with the enterprise network herein described. Future research will include the simulation of more complex enterprise network scenarios with different characteristics.

The model can be implemented node‐to‐node, since not all the companies in the network have to participate, thus facilitating implementation and propagation throughout the network.

The paper proposes a new structured planning and forecasting collaboration model for networked manufacturing enterprises.