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ABSTRACTSystem engineering methods for the performance evaluation of manufacturing systems require the estimation of input parameters, which is either based on real data or experts’ knowledge. In both cases, the input parameters are subjected to uncertainty. However, most of the systems engineering methods, which are based on analytical models assume that machine reliability parameters such as Mean Time to Failure and Mean Time to Repairs are precisely known. In order to overcome this limitation, this paper proposes an approach for the performance evaluation of unreliable manufacturing systems that considers uncertain machine reliability estimates. The method enables to calculate the distribution of the output performance, given the distribution of the input parameters’ uncertainty. The evaluation procedure is based on the combined use of Bayesian estimation, probability density function discretization and existing decomposition-based techniques for analyzing transfer lines composed of unreliable machines and capacitated buffers. Experimental results obtained by using the method show that neglecting uncertainty in the input parameter estimates generates significant errors in the output performance measure estimation, thus making the subsequent system operation and reconfiguration decisions sub-performing. Finally, a real case study is presented to demonstrate the potential benefits of the proposed method for real industrial applications.

Remanufacturing is recognized as one of the most profitable and environmentally conscious options of the circular economy. A remanufacturing process chain includes disassembly, cleaning, inspection, reconditioning and reassembly stages to recover the functionality and value of post-use products. However, the efficiency and profitability of remanufacturing are significantly affected by the variability of post-use product conditions. Consequently, the disassembly tasks times are highly uncertain, and this leads to a lack of robustness in disassembly lines designed without considering these challenges. This paper aims at finding the optimal disassembly line design under uncertainty of tasks times to support remanu-facturing. A mathematical optimization model with the objective of profit maximization is proposed which jointly optimizes and determines (1) the sequence of components to be disassembled and the assignment of disassembly tasks to workstations and (2) the allocation of buffers in order to provide a disassembly line design which has the maximum profit and satisfies the desired cycle time. The benefits of the proposed model are validated within a real case study dedicated to the remanufacturing of mechatronic components in the automotive industry.

The design phase of assembly systems under uncertain product-mix and product volume settings is challenging for both manufacturers and researchers. One of the design paradigms to deal with uncertain and dynamic environments is through reconfigurable system designs. This paper presents the formulation and a solution approach for supporting the design of assembly systems that are based on reconfigurable and modular resources. The method assists manufacturers in the search of optimal design solutions and future reconfiguration actions. Systems designed this way can efficiently respond to external changes, such as evolving product types and uncertain demand scenarios. The proposed methodology has been used to solve a real use-case in the ReCaM project, and preliminary results demonstrate its potential benefits for industrial settings.

Tailoring products for emerging regional markets under a frugal innovation perspective is a strategic opportunity for companies’ competitiveness. However, this entails managing the co-evolution of adapted products and production systems for affordable manufacturing costs. In this paper, a modular design process is proposed to support the configuration of production systems adapted to regional market needs. It includes the matching of product and equipment modules, the line balancing, and the system design into an integrated problem. This modular approach supports the propagation of requirements in the product and system co-design process. The results are demonstrated in an automotive real case study.

In recent years, European manufacturing companies are gradually applying innovative PSS (Product Service Systems), as strategic opportunity for differentiating from competitors, offering an integrated bundle of products and services, targeted on specific needs of different customers. At the same time, frugal innovation has also surged as a new business concept based upon an intelligent use of resources to fulfill region-dependent customers’ needs. Both approaches bring forth rethinking of established business models, which in turn asks for an in-depth analysis of the implications on the company organization and infrastructure, at supply chain and plant levels, urging towards manufacturing networks and reconfigurable assembly lines. This paper presents a formalized framework to support product-service design and the related business model characterization, in the context of frugal innovation. The methodology is applied to three real industrial scenarios respectively in the aeronautics, the domestic appliances and the machinery industry, which are analyzed within the framework of the H2020 European funded project ‘ProRegio’.

Uncertainty affecting manufacturing systems or networks asks for approaches able to smoothing the negative effects of disturbances coming both from the plant and supply chain levels. Thus, the objective of this paper is to investigate the capability of product-services business models of increasing companies robustness, towards internal and external disturbances in highly dynamic environments. This work is part of the EU project RobustPlaNet, which investigates this topic in the context of three industrial areas, i.e. automotive, aerospace and industrial automation. The analysis is carried out on a case study in the field of equipment reliability in order to demonstrate the formalization of business models for industrial product-services that support systems’ robustness.

The early-stage design of automotive assembly lines is a critical, multi-objective task. This design process is typically carried out in industry by continuous interactions between the process design and the simulation and systems engineering departments, resulting in a time consuming and cost-ineffective procedure. This paper presents a novel approach and a software platform to support the early stage design of hybrid assembly lines including multiple assembly technologies, such as Resistance Spot Welding (RSW) and Remote Laser Welding (RLW). This platform relies on the integration of an Assembly Layout and Process Estimator and a Configuration Optimization, based on analytical performance evaluation models. With the platform, it is possible to reduce drastically the overall cost and time required by the design procedure. A real door assembly line in the automotive industry shows the effectiveness of the proposed approach in industrial settings, analyzed within the EU FP7 funded project RLW Navigator

Hybrid assembly lines include multiple assembly technologies, such as RSW (Resistance Spot Welding) and RLW (Remote Laser Welding). The early-stage design of these automotive assembly lines is a critical, multi-objective task. The design process is typically carried out in industry by continuous iterations between the process design department and the simulation and systems engineering department. This results in a time consuming and cost-inefficient procedure. This paper presents a novel approach and a software platform to support the early stage design of hybrid assembly lines. It relies on the integration of a Process Concept Generatorand a System Configuration Module, which is based on analytical performance evaluation models, thus drastically reducing the overall time and cost of the design procedure. The effectiveness of the proposed approach in industrial settings is shown by a real door assembly line in the automotive industry, analyzed within the EU FP7 funded project RLW Navigator.

Manufacturing companies are continuously facing the challenge of operating their manufacturing processes and systems in order to deliver the required production rates of high quality products of increasing complexity, with limited use and waste of resources. This aspect is particularly critical in emerging sectors, such as the e-mobility industry, where state of the art quality and process control technologies show strong limitations. This paper proposes new solutions for implementing in-line product repair strategies in the production of electric drives for the automotive industry. Moreover, it develops an innovative quantitative tool to estimate the impact of the proposed strategies on the overall process-chain performance. The benefits of the approach are validated within a real industrial context.

Decision making in the design and operation of advanced multi-stage manufacturing systems is more and more supported by digital manufacturing tools. In order to be effective in their scope, such tools have to be based on high-fidelity virtual representations of the real system. To achieve this goal, they are continuously fed with process and system data directly collected from the field. Once validated, these digital tools can be used to evaluate and generate alternative system improvement actions and optimized re-designs of the system, based on scenario analysis. Traditionally, manufacturing systems engineering methods suitable to this scope include analytical methods and simulation. While evaluating the performance of the system under a given configuration, they typically assume that machine reliability parameters (Mean Time to Failure and Mean Time to Repair) are precisely known. However, in practical situations, these parameters are either estimated from real life data or based on experts’ knowledge. In both cases, they are subject to estimate uncertainty. This paper investigates the risks and the potential performance losses due to design and operation decisions derived by neglecting machine reliability uncertainty in the digital manufacturing tools. The proposed method paves the way to the on-line adoption of digital models for manufacturing system continuous improvements.

Traditional systems engineering methods for the performance evaluation of manufacturing systems assume that machine reliability parameters (Mean Time to Failure and Mean Time to Repair) are precisely known. However, in practical situations, these parameters are either estimated from real life data or based on experts’ knowledge. In both cases, they are subject to uncertainty. This paper proposes for the first time an approach for the performance evaluation of unreliable manufacturing systems that considers uncertain machine parameter estimates. The proposed method is based on the combined use of Bayesian estimation, probability density function discretization and existing decomposition-based techniques for analyzing manufacturing lines composed of unreliable machines and capacitated buffers. Numerical results show that neglecting uncertainty in the input parameter estimates generates consistent errors in the output performance measure estimates, thus making the consequent system design and operation decisions sub-performing. An industrial case is proposed to show the benefits of this method in real production settings.

Selective and Adaptive Production Systems (SAPS) are widely used in the mechanical component manufacturing to deliver high precision products from relatively low precision sub-assemblies. By involving component inspection and partitioning, SAPS increase the product quality at the cost of decreasing the logistics performance of the system. In this paper, the integrated analysis of quality and production logistics performance in SAPS is addressed. A method to derive the optimal number of process mean shifts to meet desired levels of component matching is presented. Moreover, a system level simulation model is developed to validate the impact of the proposed shift design on the overall system performance. The proposed method outperforms existing shift design approaches and paves the way to the efficient design and control of SAPS.

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general theory and a methodology to analyze the cumulated output and the lot completion time variability of unreliable machines and systems characterized by general Markovian models. Both discrete models and continuous reward models are considered. We then discuss two simple examples that show how the theory developed in this paper can be applied to analyse the dependency of the output variability on the system parameters.

The configuration phase of a production system is characterized by the need of evaluating a large number of system alternatives. In this phase, a fast and reliable performance evaluation tool able to analyze and explain the behaviour of the system is necessary. The paper proposes an approximate analytical method based on decomposition techniques for modelling and evaluating the performance of production systems involving split and merge of production flows, multiple products, buffers with finite capacity and manufacturing/assembly/disassembly operations. The paper also reports the application of the method to the performance evaluation and reconfiguration of a real system producing white goods.