M.Comm. / Operational management is to plan and control the transformation process that converts the requirements of customers into a product or a service. The non programmed refurbishing of railway wagons at Transwerk (Germiston) is a very difficult task for the operational manager. The number of wagons and what to repair on each wagon, only become known when the wagon arrives at Transwerk. The planning and scheduling of resources like labour, material, facilities, equipment and cash flow are therefor not so easy. To overcome the difficulties of the planning and scheduling of resources, and to refurbish the wagons according to the customer's requirements and satisfaction, it is necessary to have an operational management model or system. This system comprises of functions like aggregate planning, production scheduling, planning for fixed and variable capacities, inventory planning, design of the operational process itself as well as the control of the whole system. All of this must be set in a framework of the company's overall strategy as well as the financial and operational strategies. The first part of this study deals with the heories of operational management. It is important that the operational manager is familiar with the types of decisions to be made and what concepts are available to optimise his decisions. He also needs to know which product positioning strategy and which process positioning strategy to take. In the case of Transwerk a make-to-order product positioning strategy will be used to refurbish the wagons, and a make-to-stock strategy to manufacture the components that are needed to repair the wagons. A job-shop process positioning strategy will be used, because no fixed process flow can be followed to repair the different wagons. In the second part of this study an empirical research was done. The frequency and number of the different types of wagons that were sent to Transwerk, as well as the occurrence of the repair types on the wagons, were researched. Four years (1992 to 1995 book year) of information were used. The number of wagons and when the wagons might come in for repairs in 1996, as well as what types of repairs to do, are forecasted by using quantitative techniques as time series decomposition and weighted moving averages. The forecast or aggregate plan is the starting point of any operational model. Finally the operational model is designed. The model starts with the business- and operational strategies followed by the production plan. The production plan, derived from the forecast, shows the wagons that are expected to be built for the next five years. Transwerk should check the major available resources to assure that the production can be met. After the production plan is authorised by the executive manager the operational manager can do the master schedule. This schedule shows by model level which wagon types are expected to be repaired per day per month. This information also comes from the forecast that was done. Out of the master schedule, the material- and capacity plans can be drawn up. Production can start once the operational manager finds the planning to be correct. It is important that every step in the model is controlled and that the plans are changed if necessary. A well defined operational management model will help Transwerk on the way to be a world class company.

Ce travail de thèse s’inscrit dans le projet logistique de l’Assistance Publique Hôpitaux de Marseille. En effet l’AP-HM a ouvert une plateforme logistique en avril 2013 afin de centraliser les activités de production de repas, de stérilisation, de stockage de produits hôteliers et de blanchiment de linge. Ces produits finis sont ensuite transportés dans des contenants, grâce à une équipe de transport, vers les quatre centres hospitaliers de Marseille. Le but de cette étude de recherche est de proposer une méthode et un outil permettant d’aider l’équipe de régulation des transports pour la gestion des ressources de transport. Cette étude prend en compte la variabilité des temps de trans- port ainsi que les aléas pouvant intervenir dans le cycle de vie d’une tournée de transport.Pour cela nous commençons par réaliser un modèle de connaissance du système logistique existant grâce à la méthodologie ASCI. Nous proposons ensuite une méthode et un outil permettant la génération de planning de tournées quotidien. Cette méthode est une solution ad-hoc qui intègre la résolution d’un problème de charge- ment, la planification de véhicules et d’équipages, ainsi qu’une représentation et une mo- délisation statistique de la variabilité des temps de transports en milieu urbain. En effet, le taux de congestion quotidien peut faire varier un temps de transport du simple au double. Enfin pour la gestion des aléas, nous proposons une méthode de réparation de planning que nous modélisons grâce aux systèmes multi agents. Ce dernier point de ce travail de thèse permet, en fonction des scénarios de défaillance, de proposer la solution la plus adaptée aux équipes de transport. / This thesis work is part of the logistics project of the Assistance Publique-Hôpitaux de Marseille. Indeed the AP-HM opened a logistics platform in April 2013 in order to central- ize production activities of meals, sterilization, storage product and bleaching of linen. These products are then transported in containers, thanks to a team of transport, to the four hospitals in Marseille. After consumption of the products, by healthcare units, used containers must be re- ported to the logistics platform that they are disinfected and reinstated in the production loop. The purpose of this research study is to propose a method and a tool to help the team of regulation of transport for the management of transport resources. This study takes into account the variability of the transport time and the hazards that could inter- vene in the life cycle of a tour of transport.For this we make a knowledge model of logistics system using the ASCI methodology. This model of knowledge is then validated with a simulation model. We offer then a method and a tool allowing the generation of daily tour schedule. This method is an ad - hoc solu- tion that integrates solving a problem loading, planning for vehicle and crew, as well as representation and statistical modelling of variability in the time of transport in urban areas. Indeed, the daily congestion rate can vary a transport time of one to two. Finally, for the management of the ups and downs, we propose a method of repair of planning that we model with multi agent systems. This last point of this thesis according to failure sce- narios, makes it possible to propose the best solution to the transport staff.

This treatise investigates the underlying factors that are hindering the improvement of Overall Equipment Effectiveness at the Ford Struandale Engine Plant. In January of 2008 the Ford Motor Company announced plans to invest more than R1.5 billion to expand operations for the production of Ford's next-generation compact pickup truck and the PUMA diesel engine. Ford will use the investment to expand operations both in Silverton for the production of 75 000 units of a new bakkie and in Port Elizabeth for 220 000 units of its new-generation PUMA diesel engines (http://www.autoblog.com /2008/01/31/ford-to-invest-209m-in-south-africa-for-new-ranger-pickup/). Only five Ford plants globally will be producing the PUMA engine (Turkey, UK, Thailand, Argentina and South Africa – Ford Struandale Engine Plant). The Ford Struandale Engine Plant will be the only plant which will have the I4 assembly, I5 assembly and 3C (Crank, Cylinder Block, and Cylinder Head) machining and the expectations of operating in a lean environment is high. The management team at the Ford Struandale Engine Plant needs to understand what the underlying factors that are hindering the improvement of Overall Equipment Effectiveness of the plant or, in other words, they need to be informed of the total benefits of TPM. A literature review was conducted to determine what the theory reveals about Overall Equipment Effectiveness, the three factors of OEE (Availability, Performance Efficiency, and Quality), the influence of Six Big Losses on each of the factors and the role of Total Productive Maintenance in improving OEE by eliminating these Six Big Losses. A Ford literature study was conducted to reveal the current literature being applied at Ford. This was then followed by an empirical survey conducted within the Ford Struandale Engine Plant. In addition, a task team formed to analyse the current maintenance operating strategy. Finally, the findings from discussions with the task team, the empirical survey, Ford Struandale Engine Plant literature survey and a general literature survey were amalgamated to draw conclusions relating to the Ford Struandale Engine Plant. These conclusions indicate what the underlying factors are that are hindering the improvement of Overall Equipment Effectiveness of the Ford Struandale Engine Plant facilities and equipment. Then recommendations are made as to how the Ford Struandale Engine Plant can improve the Overall Equipment Effectiveness of its facilities and equipment.