The interaction between master production scheduling and detailed capacity requirements planning : closing the loop in manufacturing resource planning (MRPII) systems

The widespread implementation of Manufacturing Resource Planning (MRPII) systems in this country and abroad and the reported dissatisfaction with their use formed the initial basis of this piece of research which concentrates on the fundamental theory and design of the Closed Loop MRPII system itself. The dissertation concentrates on two key aspects namely; how Master Production Scheduling is carried out in differing business environments and how well the `closing of the loop' operates by checking the capcity requirements of the different levels of plans within an organisation. The main hypothesis which is tested is that in U.K. manufacturing industry, resource checks are either not being carried out satisfactorily or they are not being fed back to the appropriate plan in a timely fashion. The research methodology employed involved initial detailed investigations into Master Scheduling and capacity planning in eight diverse manufacturing companies. This was followed by a nationwide survey of users in 349 companies, a survey of all the major suppliers of Production Management software in the U.K. and an analysis of the facilities offered by current software packages. The main conclusion which is drawn is that the hypothesis is proved in the majority of companies in that only just over 50% of companies are attempting Resource and Capacity Planning and only 20% are successfully feeding back CRP information to `close the loop'. Various causative factors are put forward and remedies are suggested.

Production scheduling tools' evaluation in a global telecommunications and electronics company

Tutkimuksen päätavoite on arvioida, ovatko neljä ohjelmistovaihtoehtoa riittäviä tuotannon aikataulutuksen työkaluja ja mikä työkaluista sopii toimeksiantajayritykselle. Alatavoitteena on kuvata tuotannon aikataulutuksen nyky- ja tahtotila prosessimallinnuksen avulla, selvittää työkalun käyttäjätarpeet ja määritellä priorisoidut valintakriteerit työkalulle.Tutkimuksen teoriaosuudessa tutkitaan tuotannon aikataulutuksen logiikkaa ja haasteita. Työssä tarkastellaan aikataulutusohjelmiston valintaa rinnakkain prosessinmallinnuksen kanssa. Aikataulutusohjelmistovaihtoehdot ja metodit käyttäjätarpeiden selvittämiseksi käydään läpi. Empiriaosuudessa selvitetään tutkimuksen suhde toimeksiantajayrityksen strategiaan. Käyttäjätarpeet selvitetään haastattelujen avulla jaanalysoidaan QFD matriisin avulla. Toimeksiantajayrityksen tuotannon aikataulutuksen nyky- ja tahtotilaprosessit mallinnetaan, jotta ohjelmistojen sopivuutta, aikataulutusprosessia tukevana työkaluna voidaan arvioida.Tutkimustuloksena ovatpriorisoidut valintakriteerit aikataulutustyökalulle eli käyttäjätarpeista johdetut tärkeimmät toiminnalliset ominaisuudet, järjestelmätoimittaja-arvio sekä suositukset jatkotoimenpiteistä ja lisätutkimuksesta.

Researching Manufacturing Planning and Control system and Master Scheduling in a manufacturing firm.

The objective of this thesis is to research Manufacturing Planning and Control (MPC) system and Master Scheduling (MS) in a manufacturing firm. The study is conducted at Ensto Finland Corporation, which operates on a field of electrical systems and supplies. The paper consists of theoretical and empirical parts. The empirical part is based on weekly operating at Ensto and includes inter-firm material analysis, learning and meetings. Master Scheduling is an important module of an MPC system, since it is beneficial on transforming strategic production plans based on demand forecasting into operational schedules. Furthermore, capacity planning tools can remarkably contribute to production planning: by Rough-Cut Capacity Planning (RCCP) tool, a MS plan can be critically analyzed in terms of available key resources in real manufacturing environment. Currently, there are remarkable inefficiencies when it comes to Ensto’s practices: the system is not able to take into consideration seasonal demand and react on market changes on time; This can cause significant lost sales. However, these inefficiencies could be eliminated through the appropriate utilization of MS and RCCP tools. To utilize MS and RCCP tools in Ensto’s production environment, further testing in real production environment is required. Moreover, data accuracy, appropriate commitment to adapting and learning the new tools, and continuous developing of functions closely related to MS, such as sales forecasting, need to be ensured.

Solving two production scheduling problems with sequence-dependent set-up times

In today’s competitive markets, the importance of goodscheduling strategies in manufacturing companies lead to theneed of developing efficient methods to solve complexscheduling problems.In this paper, we studied two production scheduling problemswith sequence-dependent setups times. The setup times areone of the most common complications in scheduling problems,and are usually associated with cleaning operations andchanging tools and shapes in machines.The first problem considered is a single-machine schedulingwith release dates, sequence-dependent setup times anddelivery times. The performance measure is the maximumlateness.The second problem is a job-shop scheduling problem withsequence-dependent setup times where the objective is tominimize the makespan.We present several priority dispatching rules for bothproblems, followed by a study of their performance. Finally,conclusions and directions of future research are presented.

Solving a concrete sleepers production scheduling by genetic algorithms

PRECON S.A is a manufacturing company dedicated to produce prefabricatedconcrete parts to several industries as rail transportation andagricultural industries.Recently, PRECON signed a contract with RENFE,the Spanish Nnational Rail Transportation Company to manufacturepre-stressed concrete sleepers for siding of the new railways of the highspeed train AVE. The scheduling problem associated with the manufacturingprocess of the sleepers is very complex since it involves severalconstraints and objectives. The constraints are related with productioncapacity, the quantity of available moulds, satisfying demand and otheroperational constraints. The two main objectives are related withmaximizing the usage of the manufacturing resources and minimizing themoulds movements. We developed a deterministic crowding genetic algorithmfor this multiobjective problem. The algorithm has proved to be a powerfuland flexible tool to solve the large-scale instance of this complex realscheduling problem.

Fuzzy Multi-Objective Linear Programming and Simulation Approach to the Development of Valid and Realistic Master Production Schedule

Master production schedule (MPS) plays an important role in an integrated production planning system. It converts the strategic planning defined in a production plan into the tactical operation execution. The MPS is also known as a tool for top management to control over manufacture resources and becomes input of the downstream planning levels such as material requirement planning (MRP) and capacity requirement planning (CRP). Hence, inappropriate decision on the MPS development may lead to infeasible execution, which ultimately causes poor delivery performance. One must ensure that the proposed MPS is valid and realistic for implementation before it is released to real manufacturing system. In practice, where production environment is stochastic in nature, the development of MPS is no longer simple task. The varying processing time, random event such as machine failure is just some of the underlying causes of uncertainty that may be hardly addressed at planning stage so that in the end the valid and realistic MPS is tough to be realized. The MPS creation problem becomes even more sophisticated as decision makers try to consider multi-objectives; minimizing inventory, maximizing customer satisfaction, and maximizing resource utilization. This study attempts to propose a methodology for MPS creation which is able to deal with those obstacles. This approach takes into account uncertainty and makes trade off among conflicting multi-objectives at the same time. It incorporates fuzzy multi-objective linear programming (FMOLP) and discrete event simulation (DES) for MPS development.

Advanced production scheduling for batch plants in process industries

An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.