Maintenance Logistics Management: A Survey Study in the Moroccan Industrial Context

Global logistics system performance could not be achieved without an available intern logistics system for production systems.  Indeed, availability is the fastest path to performance where maintenance logistics occupies the central role.In this paper we define a systemic framework for maintenance logistics to manage all maintenance resources and their interactions.  In order to define main maintenance logistics management problems we conducted a structured survey study in the Moroccan industrial context. The study presents results based on 152 surveys responses from 281 surveys addressed to different industrial production systems. Results analysed on SPSS statistical software revealed an insufficient involvement level for production operator and insufficient organisation level for maintenance logistics environment. Therefore we propose a new sustainable model conception based on empirical conclusions. Keywords— Maintenance logistics system; management model; Operator involvement; maintenance logistics environment; SPSS software; maintenance improvement

Spot Market versus Full Charter Fleet:Decisions Support for Full Truck Load Tenders

This paper presents an approach to help business decision-makers gain market share by providing competitive tender offers for Full-Truck-Load (FTL) services. In particular, we compare operating a fleet of Full-Charter-Trucks (FCT), using spot-market (SM) capacity and a mixture of both options against each other. A Pickup and Delivery Problem is modeled, and solved using an Adaptive Large Neighborhood Search heuristic. Computational results indicate strong service benefits combining FCT and SM usage. Numerical experiments are presented in detail to support the findings. Additionally, a real-life case study originating from DB Schenker is presented.Comment: 22 pages, 4 figures, 9 table

Maturity level of predictive maintenance application in small and medium-sized industries: Case of Morocco

In order to remain competitive in the long term and to push the company's efficiency to its limits, entrepreneurs are more and more open to the idea of integrating into Industry 4.0 aiming mainly at filling the important downtimes and the associated productivity losses by implementing predictive maintenance. This concept, common in developed countries, is much less widespread in Morocco and even less in small and medium-sized Moroccan companies. The objective of this article is to study the maturity level of predictive maintenance in Moroccan small and medium-sized enterprises, through a questionnaire validated by experts and made available to several companies. Valid data from 115 companies throughout the kingdom operating in different sectors were collected and processed by descriptive and factorial analysis under SPSS software. The results obtained show that only 33% of our sample were able to implement predictive maintenance, and that the expected benefits of this approach are the minimization of downtime at 96.5% and the increase in productivity at 94.8%, The main challenges observed are the lack of team motivation and a corporate culture unsuited to digitalization, which represents 42.277% of the total variance, lack of financial resources at 12.916% of the total variance and lack of data protection at 11.644% of the total variance. This analysis indicates that the level of maturity regarding the application of predictive maintenance in Moroccan small and medium-sized companies is low, these rates can be used to improve the root causes

Aircraft Maintenance Routing Problem – A Literature Survey

The airline industry has shown significant growth in the last decade according to some indicators such as annual average growth in global air traffic passenger demand and growth rate in the global air transport fleet. This inevitable progress makes the airline industry challenging and forces airline companies to produce a range of solutions that increase consumer loyalty to the brand. These solutions to reduce the high costs encountered in airline operations, prevent delays in planned departure times, improve service quality, or reduce environmental impacts can be diversified according to the need. Although one can refer to past surveys, it is not sufficient to cover the rich literature of airline scheduling, especially for the last decade. This study aims to fill this gap by reviewing the airline operations related papers published between 2009 and 2019, and focus on the ones especially in the aircraft maintenance routing area which seems a promising branch

Stakeholder Analysis and Readiness to Change on ERP Project Implementation

The successful implementation of Enterprise Resource Planning (ERP) systems in organizations heavily relies on effective change management strategies. The objective of the current study was to investigate the effect of stakeholder analysis and readiness for change on ERP Project Implementation. This study was guided by Stakeholder Theory and Lewin's Three-Step Change Theory. We employed a descriptive research design and conducted data analysis through both descriptive and inferential methods, including correlation and multiple regression. The study encompassed 732 REREC corporate staff in Nairobi, with a sample of 146 individuals selected via simple random sampling. Our findings reveal that change management success factors, specifically stakeholder analysis (r = 0.340; p < 0.01) and readiness for change (r = 0.237; p = 0.015), exhibit a statistically significant cause-and-effect relationship with ERP implementation. Among these relationships, stakeholder analysis holds the highest influence (r = 0.340), followed by readiness for change (r = 0.237). Effective change management strategies significantly effect ERP project success, with stakeholder analysis playing a substantial role and readiness for change being pivotal. Cultivating readiness for change is essential, fostering adaptability and confidence among employees through effective communication and training. Customized change management plans addressing unique stakeholder concerns should be developed and continuously monitored. Keywords: Stakeholder Analysis, Readiness for Change, Critical Success Factors, ERP Implementation, Change Management DOI: 10.7176/EJBM/15-18-08 Publication date: November 30th 202

Solving the bi-objective capacitated p-median problem with multilevel capacities using compromise programming and VNS

A bi-objective optimisation using a compromise programming (CP) approach is proposed for the capacitated p-median problem (CPMP) in the presence of the fixed cost of opening facility and several possible capacities that can be used by potential facilities. As the sum of distances between customers and their facilities and the total fixed cost for opening facilities are important aspects, the model is proposed to deal with those conflicting objectives. We develop a mathematical model using integer linear programming (ILP) to determine the optimal location of open facilities with their optimal capacity. Two approaches are designed to deal with the bi-objective CPMP, namely CP with an exact method and with a variable neighbourhood search (VNS) based matheuristic. New sets of generated instances are used to evaluate the performance of the proposed approaches. The computational experiments show that the proposed approaches produce interesting results

Challenges experienced when outsourcing logistics in South Africa: a case of Lolli Supermarkets.

Masters Degree. University of KwaZulu-Natal, Durban.The effective and efficient operation of distribution centres is a goal for all retailers using the distribution centre strategy. It therefore becomes imperative to strategically position all available resources in order to achieve a smooth operation. This has led to the emergence of outsourced logistics service providers in South Africa to enhance efficiencies while retail organisations focus on the core functions of their business. Lolli Supermarkets makes use of third party logistics (3PL) service providers to achieve delivery efficiencies and cost saving in the distribution centre. The aim of the study is to identify challenges and determine where 3PL service providers are fulfilling their strategic role and where they experience challenges that result in the provision of suboptimal services to distribution centres. This study further attempts to achieve the following: firstly, to identify the challenges Lolli Supermarkets experience with its reliance on 3PLs. Secondly, to determine whether 3PL service providers are fulfilling their strategic role to improve order replenishment. Lastly, to determine whether Lolli Supermarkets are experiencing challenges with 3PL service providers with regards to product availability and as a result provision of suboptimal services by the 3PL service provider. This allows the study to analyse the impact of outsourcing in retail organisations while also evaluating any changes that may be encountered under developing economy conditions. An exploratory design was used to identify the challenges that result in suboptimal services provided by 3PL service providers. Thematic analysis was used to analyse the data collected from sixteen respondents who form part of Lolli Supermarkets management and two respondents who form part of management of the 3PL. The main findings reveal that 3PL service providers commit to creating efficiencies for the distribution centre through adhering to outbound plans, having service levels agreements in place, meeting the set key performance indicators, maintaining information flow to align goals of both parties, and operating in the most flexible manner to achieve efficient product delivery and enhance customer satisfaction. However, there are challenges that arise due to fluctuations in outbound volumes, communication breakdown, unpunctuality and the limited number of trucks available for use

13th International Conference on Modeling, Optimization and Simulation - MOSIM 2020

Comité d’organisation: Université Internationale d’Agadir – Agadir (Maroc) Laboratoire Conception Fabrication Commande – Metz (France)Session RS-1 “Simulation et Optimisation” / “Simulation and Optimization” Session RS-2 “Planification des Besoins Matières Pilotée par la Demande” / ”Demand-Driven Material Requirements Planning” Session RS-3 “Ingénierie de Systèmes Basées sur les Modèles” / “Model-Based System Engineering” Session RS-4 “Recherche Opérationnelle en Gestion de Production” / "Operations Research in Production Management" Session RS-5 "Planification des Matières et des Ressources / Planification de la Production” / “Material and Resource Planning / Production Planning" Session RS-6 “Maintenance Industrielle” / “Industrial Maintenance” Session RS-7 "Etudes de Cas Industriels” / “Industrial Case Studies" Session RS-8 "Données de Masse / Analyse de Données” / “Big Data / Data Analytics" Session RS-9 "Gestion des Systèmes de Transport” / “Transportation System Management" Session RS-10 "Economie Circulaire / Développement Durable" / "Circular Economie / Sustainable Development" Session RS-11 "Conception et Gestion des Chaînes Logistiques” / “Supply Chain Design and Management" Session SP-1 “Intelligence Artificielle & Analyse de Données pour la Production 4.0” / “Artificial Intelligence & Data Analytics in Manufacturing 4.0” Session SP-2 “Gestion des Risques en Logistique” / “Risk Management in Logistics” Session SP-3 “Gestion des Risques et Evaluation de Performance” / “Risk Management and Performance Assessment” Session SP-4 "Indicateurs Clés de Performance 4.0 et Dynamique de Prise de Décision” / ”4.0 Key Performance Indicators and Decision-Making Dynamics" Session SP-5 "Logistique Maritime” / “Marine Logistics" Session SP-6 “Territoire et Logistique : Un Système Complexe” / “Territory and Logistics: A Complex System” Session SP-7 "Nouvelles Avancées et Applications de la Logique Floue en Production Durable et en Logistique” / “Recent Advances and Fuzzy-Logic Applications in Sustainable Manufacturing and Logistics" Session SP-8 “Gestion des Soins de Santé” / ”Health Care Management” Session SP-9 “Ingénierie Organisationnelle et Gestion de la Continuité de Service des Systèmes de Santé dans l’Ere de la Transformation Numérique de la Société” / “Organizational Engineering and Management of Business Continuity of Healthcare Systems in the Era of Numerical Society Transformation” Session SP-10 “Planification et Commande de la Production pour l’Industrie 4.0” / “Production Planning and Control for Industry 4.0” Session SP-11 “Optimisation des Systèmes de Production dans le Contexte 4.0 Utilisant l’Amélioration Continue” / “Production System Optimization in 4.0 Context Using Continuous Improvement” Session SP-12 “Défis pour la Conception des Systèmes de Production Cyber-Physiques” / “Challenges for the Design of Cyber Physical Production Systems” Session SP-13 “Production Avisée et Développement Durable” / “Smart Manufacturing and Sustainable Development” Session SP-14 “L’Humain dans l’Usine du Futur” / “Human in the Factory of the Future” Session SP-15 “Ordonnancement et Prévision de Chaînes Logistiques Résilientes” / “Scheduling and Forecasting for Resilient Supply Chains

An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations

Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions