Search

ABSTRACTIn the context of internationalization, the supply chain (SC) becomes complex with a profusion of decisions to take. SC modeling and performances have been widely discussed by researchers. The emergence of new technologies impacts the running and key performance indicators (KPI) of SC. There are several models of SC but none of them are oriented towards SC operations management, given importance of Industry 4.0 technologies. This paper presents a research methodology using a reference model called GRAILOG to capture decisions and related KPI’s to control the SC. Then, the methodology PPTechIP is described and applied to accompany the company on the relevant digital transformation to improve performances. PPTechIP is based on a set of radars divided into different decision levels and SC functions based on the GRAILOG model. The calculated potential of progress helps in decision-making. This complete method was applied to PSA which is entering the Industry 4.0 era. The obtained results provide several interesting insights into PSA's control indicators. Big Data, augmented reality, collaborative robots and Cloud Computing are receiving a lot of attention from PSA and are considered as priorities for the control of its processes.

ABSTRACTOver the last decade, additive manufacturing (AM) has received an increased attention as many manufacturing companies have increasingly adopted new technologies to capture new opportunities. This research identifies the impacts of AM on the supply chain when compared to the case of conventional manufacturing. Through an empirical investigation conducted with 51 multinational companies in the manufacturing sector, the impacts of AM are analysed by focusing on post-processing operations, lead times, cost implications, flow management strategies and maintenance. The empirical investigation reveals two major benefits of AM, namely: the ability to produce complex parts in low volumes and good design and the prototyping capability. However, the empirical results are mixed for some other impacts of AM. In fact, although many experts agreed on the general benefits of AM, a significant number did not see much difference from the conventional manufacturing. We also provide empirical evidence that, under AM, maintenance complexity and duration do not reduce as opposite to what is reported in the literature, which might be due to the novelty of AM technology and the complexity of the machines themselves. Moreover, our findings reveal that a reduction of suppliers, a reduced need for huge stockpiles and a reduced need for transportations which are often acknowledged as advantages of AM are not confirmed.

ABC classification is a technique widely used by companies to deal with inventories consisting of very large numbers of distinct stock keeping units. Single-criterion ABC classification methods are often used in practice and recently multi-criteria methods have attracted the attention of academics and practitioners. Several models have been developed to deal with the multi-criteria ABC inventory classification (MCIC). To the best of our knowledge, very few researches have used the unsupervised machine learning methods to address the MCIC problem despite their attractive theoretical and practical properties. Therefore, in this paper, the Gaussian mixture model (GMM) is proposed to deal with the multi-criteria ABC inventory classification problem. GMM is a simple optimization model that can be used for classification purposes with a low computational time. A numerical investigation of the cost-service inventory of the GMM model is presented in this paper. The performance of the model is also compared to some mathematical programming-based MCIC models. The numerical study is conducted by means of a theoretical dataset, consisting of 47 stock keeping units, which has been commonly used in the literature. The numerical results show that the proposed model can have a promised performance along with the existing MCIC classification models in the literature in terms of the cost-service inventory efficiency.

The topic of Performance Measurement and Management has been investigated for more than twenty five years leading to more than thirty methods around the world, developed either by researchers or more pragmatically by practitioners, in order to define and implement indicators. Most of them are more oriented for the definition and few for the implementation. Other are simply a list of recommended PI’s. Several studies have been done to compare some of these methods and to explain the reasons of PI’s implementation failures. The objectives of this paper is to go deeper in detail in the comparison and in a second time to define a generic framework that could help to detect what should contain a generic method for Performance Indicator System definition and implementation and what is the knowledge that must be included in this kind of method to be more efficient

Market competition is pushing companies to differentiate themselves from competitors by developing customised services in addition to their original production (either physical or digital). It drives the emergence of service process modelling to describe precisely the composition of services. To this end, this paper presents the key concept of the transformation of EA* and BPMN models into simulation workflows. Then, it introduces the implementation done with the SLMToolBox that is a service graphical modeller, model transformer, and simulation engine. In addition, enterprises are facing situations where future (undeveloped yet) enterprise services need to be integrated with existing ones. To go further and for a better integration and deployment of service models in the enterprise, we propose to combine service process M&S with service calls execution workflow. To achieve that goal, we are mashing up simulation of services modelled with existing enterprise web services calls.

Risk is an inherent property of every project. In many cases, project management and risk management are applied quite independently. The traditional tools of project management do not include the notion of risk and the tools of risk management focus on the representation of risks without explicitly representing the project, which leads to implement the risk management process independently of the project management process. This paper demonstrates the need to develop an integrated approach to project risk management and presents our approach which is able to represent the risks, but also the project, its components and its environment.

This article proposes a framework and methodology to evaluate and improve the interoperability for each partner of an inter-enterprises collaboration. The framework is based on the decomposition of business processes into non-value-added (NVA) and business activities and it is also based on process performance indicators (PIs) to measure interoperability. The developed methodology is then founded on this framework and is aimed to provide support for validating interoperability solutions. A causal performance measurement model (CPMM) is used to define how interoperability improvement may influence the achievement of all of the partners’ objectives. An application of the methodology to an illustrative example has been presented.

Vehicle routing problem (VRP) is widely dealt within the last four decades. We seek to understand the state of the art in VRP in the context of sustainable supply chain and proposed new six VRP variants encountered in sustainable supply chain routing (SSCR) context but have not been given attention to hitherto. These variants of the classical VRP are complex and pertaining to six domains of SSCR. Constraints relevant to sustainable aspect/implications are considered in all the variants. This paper is devoted to the development of framework and comprehensive review on these six variants of SSCR problems.

An extended variant of mixed capacitated arc routing problems (MCARP) with upper bound on number of vehicle is considered in this study for the application of waste collection problem and it is termed as integrated resource allocation and routing problem with upper bound ( IRARPUB ) problem. Composite heuristic leveraging Dijkstra’s algorithm and local search inherent genetic algorithm (DIALING) is proposed to solve both MCARP and IRARPUB. Extensive comparisons of the proposed heuristic with the existing benchmarks or the available lower bound show that it can produce excellent results, outperforming the existing best or yielding results with minimal deviations.

Supply chain excellence has a real impact on business strategy. Manufacturing is an integral part of this strategy, represents one of the most exciting opportunities to create value and one of the most challenging tasks for policy makers. In this paper, we consider a performance criterion for the flowshop scheduling problem that aims to minimise a complex cost function, i.e., the sum of weighted tardiness and weighted flow-time costs. A heuristic and hybrid genetic algorithms are proposed and experimental results are provided. We address this trade-off and propose solution techniques that are easy for the shop-floor manager to implement. As scheduling function is an integral part of supply chain, the proposed solution minimises the opportunity losses and improves cost based supply chain performance. This paper addresses this interesting and challenging domain.

The public health system in India is vibrant in meeting the healthcare demands of the rural people through primary health centres (PHC). This study proposes a conceptual framework that examines the impact of healthcare professionals’ perception about work place experiences which encompasses HRM practices, PHC infrastructure and social support in the work place on pro-social organisational behaviour. The study is proposed to be conducted in selected PHC located in the state of Tamil Nadu. When empirically tested, this study would highlight the human resource issues so that the system could respond effectively to the demands in healthcare workforce.

In this paper we present approach for simulation of supply chain using a simulation package. The objective is to develop general modules for the simulation of supply chain, which can be used for simulation of complex supply chain. Simulation model has flexibility of using different ordering policies and different set of parameters for different nodes of supply chain. Numbers of simulation experiments are performed to generate knowledge. In particular, based on a cost and time performance analysis, the different configurations are analysed in order to support the selection of suitable polices and parameters of the operations network.

A typical Flexible Manufacturing System (FMS) has been studied under Planning Design and Control (PDC) strategies. The chief objective is to test the impact of design strategy (routing flexibility) on system performance under planning strategy (alternate system load condition) with control strategies (sequencing and dispatching rules). A computer simulation model is developed to evaluate the effects of aforementioned strategies on the make-span time, which is taken as the system performance measure. Shortest Processing Time (SPT), Maximum Balance Processing Time (MBPT) are the sequencing rules for selecting the part from the input buffer whereas for machine selection the dispatching rules are Minimum Number of parts in the Queue (MINQ), and Minimum queue with Minimum Waiting Time of all parts in the Queue (MQMWT). In this paper, the same manufacturing system is modeled under four different system load conditions. These load conditions are Full Balanced Load (FBL), Balanced Machine Load and Unbalanced Processing Time (BMLUPT), Unbalanced Machine Load and Balanced Processing Time (UMLBPT) and Unbalanced Load (UBL) with respect to machine load and processing time. The result of the simulation shows that there is continuous reduction in make-span with increase in routing flexibility when both machine load and processing times are unbalanced i.e., under UBL system condition.

Supply chains are increasingly needed to work with core competencies of a flexible system enriched with Knowledge Management (KM). An integrated decision making amongst autonomous chain partners focused on suitable knowledge sharing is required to derive superior operational performance from flexibility based core competency in supply chains. Evolving competitive challenges demand a time based competition and an increasing variety of customer needs. In this context, Knowledge Management (KM) can be used as an effective approach to achieve Decision Knowledge Sharing (DKS) leading to improved operational competence. Flexible Supply Chains (FSCs) are more complex and involve multiple autonomous players with varying technical cultures (affects knowledge mindsets), managerial background (affects decision knowledge) and SCM exposures (affects knowledge sharing attitudes). This includes the implementation of decision knowledge enriched supply chains by judicious use of information technology (IT). The development of knowledge based supply chain depends on the nature of knowledge flow in the entire chain. Timely sharing of decision knowledge amongst the chain partners can be very useful. However it requires change in managerial mindsets. Thus there is a need to develop demo models that can encourage chain managers towards greater collaborative-knowledge sharing in the supply chains. This paper presents the application of one such model based on decision knowledge sharing (DKS) for improved supply chain performance. By exploiting Decision Knowledge Sharing (DKS) and flexibility in supply chain structures better operational performance can be achieved. This paper presents a study on the role of different decision knowledge sharing options (i.e. no DKS, partial DKS and full DKS) in a flexible supply chain model based on key dynamic parameters and performance measures. DKS in flexible supply chains has significant potential and needs greater research attention. We attempt to advance the DKS in the context of flexible supply chains. A simulation model of a flexible supply chain based on DKS framework is developed for demo purposes. The key results are highlighted along with industry implications. Here each supply chain node involves decision-making. A seemingly good decision at a stage can be obtained based on local and global knowledge sharing. The cost based performance of alternative DKS with different levels of flexibility is studied. The observations are important for the designers and managers of the FSCs to arrive at appropriate levels of flexibility and judicious level of decision knowledge sharing to attain significant benefits towards operational competence. It is suggested that flexibility and KM are important core-competency that can improve operational performance in supply chains.

Performance is an issue today in production management. Thus, every decision-maker within the production management system must make his own decisions in relation to local objectives which must be defined and valued consistently within the global objectives of the system. Based on this assumption, every decision-maker must have performance indicators available in order to evaluate his own decisions in relation to the objectives that he must meet and to react according to his action means (decision variables). The first part of this paper aims to present the GRAI approach, an enterprise modelling method within which the notion of performance indicators appears. More precisely, two kinds of performances are considered. The first one is operation-oriented and related to the mastery of production processes. The second is structure-oriented and supports the management of evolution. The second part of the paper deals with the presentation of ECOGRAI, a method dedicated to design and to implementing those performance indicators arising from the operation-oriented approach. The paper will present the main concepts of this method, the techniques used for management system modelling and design and for the definition of objectives and the decision variables of each decision-maker. It will also present the structured approach supporting the definition and the implementation of the performance indicator system. Throughout the exposition of ECOGRAI, a case study concerning the maintenance function will illustrate the description.