Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Methodology – A Review of Intelligent Manufacturing Scope, Strategy and Simulation

This paper presents a critical review of some existing modelling, control and optimization techniques for energy saving, carbon emission reduction in manufacturing processes. The study on various production issues reveals different levels of intelligent manufacturing approaches. Then methods and strategies to tackle the sustainability issues in manufacturing are summarized. Modelling tools such as discrete (dynamic) event system (DES/DEDS) and agent-based modelling/simulation (ABS) approaches are reviewed from the production planning and control prospective. These approaches will provide some guidelines for the development of advanced factory modelling, resource flow analysis and assisting the identification of improvement potentials, in order to achieve more sustainable manufacturing

Smart Factories in INdustry 4.0: a review of the concept and of Energy Management approaches in Porduction based on the Internet of Things

The real and the virtual worlds are growing speedily and closely to form the Internet of Things (IoT). In fact, IoT has stimulated the factories and the governments to launch an evolutionary journey toward the fourth industrial revolution called Industry 4.0. Industrial production of the new era will be highly flexible in production volume and customization, extensive integration between customers, companies, and suppliers, and above all sustainable. Reviewing and analyzing the current initiatives and related studies of the smart factories/Industry 4.0, this paper presents a reference architecture for IoT-based smart factories, defines the main characteristics of such factories with a focus on the sustainability perspectives. And then it proposes an approach for energy management in smart factories based on the IoT paradigm: a guideline and expected benefits are discussed and presented

Energy Optimization of a Speed-Scalable and Multi-states Single Machine Scheduling Problem

International audienceThis study deals with the single-machine scheduling problem to minimize the total energy consumption costs. The considered machine has three main states (OFF, ON, Idle), and the transitions between states OFF and ON are also considered (Turn-on and Turn-off). Each of these states as well as the processing jobs consume different amount of energy. Moreover, a speed scalable machine is addressed in this paper. So, when the machine performs a job faster, it consumes more units of energy than with a slower speed. In this study, two new mathematical formulations are proposed to model this problem, and their efficiency are investigated based on several numerical experiments

Preemptive Scheduling of a Single Machine with Finite States to Minimize Energy Costs

International audienceThis paper addresses a single machine scheduling problem in which the system may switch among three different states, namely ON (needed for processing the jobs), OFF or Idle. Each state, as well as switching among the different states, consume energy. The objective is schedule n preemptive jobs to minimize the total energy costs. Time varied electricity price are considered. The complexity of this problem is investigated using a new dynamic programming approach. In this approach, a finite graph is used to model the proposed problem. The dimension (number of vertices and edges) of this graph is dependent on the total processing times and the total number of periods. Then, the optimal solution of the problem is provided by calculating the shortest path between the first node and last node representing respectively the first and the last periods. Based on the Dijkstra's algorithm complexity, we prove that the complexity of this problem, is polynomial of degree

A Simultaneous Lot-Sizing and Scheduling Problem with Energetic Aspects

International audienceIn this paper, the capacitated lot-sizing and scheduling problem with sequence-dependent setups and backlogging with energy consideration is studied. A new mixed-integer programming model is proposed. The planning horizon is split into T periods where each one is characterized by a duration, an allowed maximal power, a price of electricity and power and demands of products. The objective is to find a production scheduling that minimizes the total production cost. As this problem is known to be NP-hard, a Fix-and-Relax heuristic is developed. Computational experiments demonstrate that this method provides good quality results