Industry 4.0 provides new paradigms for the industrial management of SMEs. Supported by a growing number of new technologies, this concept appears more flexible and less expensive than traditional ...enterprise information systems such as ERP and MES. However, SMEs find themselves ill-equipped to face these new possibilities regarding their production planning and control functions. This paper presents a literature review of existing applied research covering different Industry 4.0 issues with regard to SMEs. Papers are classified according to a new framework which allows identification of the targeted performance objectives, the required managerial capacities and the selected group of technologies for each selected case. Our results show that SMEs do not exploit all the resources for implementing Industry 4.0 and often limit themselves to the adoption of Cloud Computing and the Internet of Things. Likewise, SMEs seem to have adopted Industry 4.0 concepts only for monitoring industrial processes and there is still absence of real applications in the field of production planning. Finally, our literature review shows that reported Industry 4.0 projects in SMEs remained cost-driven initiatives and there in still no evidence of real business model transformation at this time.
•A solution to the unsolved problem of predicting the energy of a grain boundary given its misorientation is proposed.•An average error of 4% for the predicted grain boundary energies is ...obtained.•The capabilities of artificial intelligence methods and their applicability in the materials science domain are shown.
Artificial Neural Networks (ANNs) have been used in a few domains of materials science (Prechelt, 1997) 1, but never for the prediction of Grain Boundary (GB) energies. In the present article, an ANN is used to generate – for the first time – a function for the GB energy in terms of its five macroscopic degrees of freedom. The proposed approach is verified for GBs of body centred cubic iron. Part of the database calculated by Kim et al. (2011) 2 is used as training data for the ANN. After the ANN has been trained (i.e. after it has learned to replicate and predict the function), the magnitude of the errors in predicted GB energies for the remaining part of the database is about 4%, which is lower than the error of 10% that is typical for experimental GB energy measurements (Rohrer et al., 2010) 3.
SMEs, as prominent actors in industry, must meet more and more complex customer expectations. Recently, the concept of Industry 4.0 has emerged. This new approach enables the control of production ...processes by providing real-time synchronisation of flows and by enabling the production of unitary and customised products. Our research goal is to identify Industry 4.0 risks, opportunities and critical success factors with regards to the industrial performance of SMEs. The recent emergence of Industry 4.0 and the inherent difficulty of identifying detailed examples has not yet enabled a satisfactory statistical study to be conducted on Industry 4.0 cases in SMEs. To reach our research goal, we selected 12 experts to conduct a Delphi study supplemented by Régnier's abacuses. Our study demonstrates that the major risks facing the adoption of Industry 4.0 in SMEs include a lack of expertise and a short-term strategy mindset. Our research also indicates that training is the most important factor for success, that managers have a prominent role in the success and/or failure of an Industry 4.0 project, and that SMEs should be supported by external experts. Lastly, Industry 4.0 offers a unique opportunity to redesign SME production processes and to adopt new business models.
This paper presents an approach to model and solve the vehicle routing problem with random delivery locations and stochastic travel times (VRPRDL-S), a variant of the vehicle routing problem with ...random delivery locations that allows, for instance, the possibility of having a parcel delivered to the trunk of the customer’s vehicle, which can be in different locations during the same day. In the proposed model, the classical distance matrix is replaced by a matrix of probability distributions composed of the distribution of travel times between two points. Thus, the model integrates the fact that the travel time between two points is non-deterministic. This paper explores in detail a medium-sized problem and uses a combination of a Monte-Carlo method and an enhanced greedy randomized adaptive search procedure (GRASP) to find a pseudo-optimum. Moreover, the relevance of the approach is validated through a campaign test. This work intends to build on the current state of the art by proposing a new variant of the VRP and a heuristic method to solve it.
Dans le cours des dernières années, la traçabilité s’est positionnée au cœur de plusieurs enjeux fondamentaux pour les entreprises. Cependant, cette notion est encore aujourd’hui vue comme une ...contrainte, servant uniquement à respecter des impositions légales et à rappeler des produits non-conformes. Dans ce projet, nous nous sommes attachés à élargir la définition de traçabilité aux domaines de la prévision et de la protection, pour qu’elle ne soit plus perçue comme une obligation supplémentaire à assumer, mais comme un véritable argument d’avantage concurrentiel. Ces travaux de recherche sont consacrés à l’exploitation des informations de traçabilité par l’utilisation des techniques d’intelligence artificielle et de recherche opérationnelle, afin de proposer des actions d’amélioration en production et en logistique. Ils ont été menés en collaboration avec la société ADENTS International, experte en traçabilité. Ce projet est composé de deux principaux axes de travail : l’un portant sur le diagnostic de la criticité d’une production, en fonction des informations de traçabilité et l’autre sur les actions à entreprendre par rapport à ce diagnostic. Dans le premier, nous remarquons l’importance de la notion de dispersion de matières premières et des composants, ainsi que celle des écarts en termes de qualité et de sécurité. Dans le second, nous nous intéressons d’avantage à la notion de rappel de produits, visant une gestion de transformations adaptée en aval de la production, afin de minimiser ces rappels. Pour la mise en place de ces deux grandes activités, nous nous sommes engagés à proposer des modèles et des méthodes flexibles et réactives, pouvant s’adapter à la versatilité ontologique des flux d’informations de traçabilité
The recent product traceability requirements demonstrate an industrial need to improve the information management strategies within traceability systems in order to evolve from reactivity to proactivity. The aim of this work is to exploit the recently available real-time access to traceability information. We propose the utilization of artificial intelligence and operational research techniques to analyse the information and therefore suggest improvement actions. This research project is composed of two main activities: first, the diagnosis of the criticality value associated to a production regarding the traceability information and second, the actions to undertake as a result of this diagnosis. One of the issues studied in this thesis is the problem of minimizing the size of products recall. Initially the problem of raw materials dispersion minimization is analysed. Then a result of the dispersion rate along with other production criteria are evaluated in order to determine a risk level criterion in terms of quality and security that we name “production criticality”. This criterion is used subsequently to optimize deliveries dispatch with the purpose of minimizing the number of batch recalls in case of crisis. This is achieved by implementing flexible and reactive tools
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