The use of information systems for logistics and supply chain management in South East Europe: Current status and future direction

This research aims to investigate the current status and future direction of the use of information systems for logistics and supply chain management (LSCM) in South East Europe. The objectives are threefold: (1) to identify major challenges and developments on the use of information systems for LSCM by enterprises, (2) to examine the actual level of satisfaction of current policy on LSCM, and (3) to reveal the actual need of enterprises in South East Europe on effective use of information systems for LSCM. Mixed methodology of literature review and questionnaire survey is adopted in this research. Data collected from 79 enterprises are analysed using descriptive analysis in SPSS. The findings suggest that enterprises in Albania, Bulgaria, Greece, Former Yugoslav Republic of Macedonia (FYROM), Romania, and Serbia and Montenegro, face similar challenges but all are in different stages of developments of LSCM. Their use of information systems explains their heavy focus on supply chain partnership and weakness in demand chain partnership. Major findings suggest that companies and governments alike in that region do not seem to be ready for playing a significant and demanding role in global supply chains. Current deficiencies, including limited abilities in building valuable forward relations, weak strategic planning and organisation, and infrastructural problems, are major obstacles for fast development in LSCM. At the same time though, traces of changing mentalities do exist, setting the ground for improved performance and ultimately for a better position in global business

Development of a business model for diagnosing uncertainty in MRP environments

Over the last thirty years, Materials Requirements Planning (MRP) based systems have become commonplace within batch manufacturing environments, but are still widely held to be under performing. This research hypothesises that there may be inherent problems associated with the application due to uncertainties that exist within dynamic operating environments. Research has highlighted both the absence of any business model that uses a structured and systematic approach to deal with uncertainty holistically and the lack of any widely used, consistent performance measures to allow comparison of research results. The industrial need for such a holistic approach became apparent from survey work, which showed MRP under-performed in the presence of uncertainty even when numerous Buffering and Dampening (BAD) approaches were applied. A business model of uncertainty that structures the causes and effects of uncertainty as a hierarchy of four levels has been proposed, to be verified and validated through industrial survey and simulation respectively. The relationship between causes and effects in the business model has been verified from survey results using Analysis of Variance (ANOVA), which identified twenty-three significant uncertainties within Mixed-Mode (MM) operating environments. Using a multi-product, multi-level dependent demand MRP simulation model within an MM operating environment driven by planned order release, an experimental programme has been carried out that showed finished products delivered late to be insensitive as a performance measure. Parts Delivered Late (PDL) was found to be more sensitive and has been adopted as the preferred measure. ANOVA on the simulation results validated the cause-and-effect relationships, showing that the higher the level of uncertainty, the worse was delivery performance. Individual uncertainties produced effects that were not discretely recognised in the literature. `Knock-on' effects are created by uncertainties delaying the issue of batches and affected particular Bill of Materials chains. `Compound' effects are caused by uncertainties affecting resource availability and also induced consequent knock-on effects. Simulation results also showed that late deliveries from suppliers, machine breakdowns, unexpected or urgent changes to schedules affecting machines and customer design changes are the most significant uncertainties within the parameter levels modelled. Several significant two-way and three-way interactions were found. The business model of uncertainty represents a practical and pragmatic attempt to act as a diagnostic tool to identify significant underlying causes affecting PDL for MM companies using MR1, enabling more effective application of suitable BAD approaches. Using the business model to drive a continuous improvement programme that monitored both levels of uncertainty and PDL would allow internal and external benchmarking for the efficacy of BAD approaches and for the reduction of uncertainties

Parts verification for multi-level-dependent demand manufacturing systems: a recognition and classification structure

This research has developed and implemented a part recognition and classification structure to execute parts verification in a multi-level dependent demand manufacturing system. The part recognition algorithm enables the parent and child relationship between parts to be recognised in a finite-capacitated manufacturing system. This algorithm was developed using SIMAN simulation language and implemented in a multi-level dependent demand manufacturing simulation model. The part classification structure enables the modelling of a multi-level dependent demand manufacturing between parts to be carried out effectively. The part classification structure was programmed using Visual Basic Application (VBA) and was integrated to the work-to-list generated from a simulated MRP model. This part classification structure was then implemented in the multi-level dependent demand manufacturing simulation model. Two stages of implementation, namely parameterisation and execution, of the part recognition and classification structure were carried out. A real case study was used and five detail steps of execution were processed. Simulation experiments and MRP were run to verify and validate the part recognition and classification structure. The results led to the conclusion that implementation of the recognition and classification structure has effectively verified the correct parts and sub-assemblies used for the correct product and order. No parts and sub-assemblies shortages were found, and the quantity required was produced. The scheduled release for some orders was delayed due to overload of the required resources. When the loading is normal, all scheduled release timing is adhered to. The recognition and classification structure has a robust design; hence it can be easily adapted to new systems parameter to study a different or more complex case

Comparing dynamic risk-based scheduling methods with MRP via simulation.

Material Requirements Planning (MRP) is one of the earliest production scheduling approaches that utilizes computers. MRP is still regarded as one of the most widely used systems for production scheduling. Even though MRP has made contributions, there are some fundamental problems (i.e. the assumption of infinite capacity and fixed lead times) which make the MRP system vulnerable to effects of uncertainty. To overcome this fundamental flaw, there was a trend towards the development of detailed finite-capacity scheduling systems (i.e. MRP II, ERP, and APO). All these MRP-based systems still ignore variability and randomness and are inherently push systems. Instead of creating a detailed schedule based on forecast, Factory Physics Inc. developed Dynamic Risk-Based Scheduling (DRS), which creates a set of policy parameters (e.g. WIP level, lot sizes, reorder point, and reorder quantity) that work for a range of situations to calculate the production schedule. This thesis compares the key performance measures of DRS and MRP-based scheduling systems. We begin with a single-machine problem and develop simulation models for varying levels of uncertainty in forecast demand (i.e. base demand scenario, under-estimated scenario and over-estimated scenario) and two levels of variability in the system (i.e. moderate variability and no variability). Then the experiment is extended to multiple-machine problems. We also introduce more constraints into the DRS and MRP models to improve their performance. We also test the performance of MRP models for different planning horizons. We find that the DRS strategy is more robust to forecast error than MRP-based strategies. DRS also usually obtains better performance than MRP-based models in terms of higher fill rate and lower inventory

formulas of revised mrp

Information sharing among supply chain echelons is now an acquired result. As a consequence, most of the traditional techniques and procedures in production management must be revised and updated, exploiting the opportunities provided by new technologies. This paper presents an improved version of Material Requirement Planning procedure, which assumes information sharing capabilities and permits the creation of new business opportunities. In Orlickyʹs MRP, orders are computed considering the parent items gross requirements. On the contrary, here the order release procedure related to a certain item is computed both by exploiting all the information sharing advantages and by introducing a drastic innovation to the main process functioning. As a result, the proposed algorithm copes better with demand uncertainty, lowers the system nervousness and also removes the need for continuous forecast adjustments, thereby improving the ease in managing the material flow, allowing the development of new forms of collaboration among different supply chain partners and the creation of new business networks. The algorithm is presented in formulas to describe in detail each procedure step and calculations

Advanced resource planning as decision support module to ERP.

In this paper, we show that the planning and decision-support capabilities of the MPC (Manufacturing Planning and Control) system, which forms the core of any ERP (Enterprise Resource Planning) package, may be substantively enhanced by including a Decision Support Module (DSM) as an add-on at the midterm planning level. This DSM, called Advanced Resource Planning (ARP), serves as parameter setting process as well as tool for improving the structure of the ERP system itself. The ultimate goal of the DSM is to yield realistic information both for scheduling, sales and marketing, strategic and operational decision making and suppliers and customers.

Exploring applicability of the workload control concept

To be successful in companies, a production planning and control (PPC) concept should fit to the production environment. Essential elements of the concept should correspond with the characteristics of the production system. For classical concepts such as MRP these elements have become common sense. For example BOMexplosion and constant lead times make MRP known to perform best in environments with high material and low capacity complexity. For many other concepts the situation is less clear. In this paper the Workload Control (WLC) concept is considered for which the requirements for a successful application have never been investigated. A framework is proposed to explore the applicability of WLC in small- to medium-sized make-to-order (MTO) companies. It supports an initial consideration of WLC in the first phase of a PPC selection and implementation process. As a first step in developing the framework the inherent characteristics of the WLC concept and the relevant MTO production characteristics are identified. Confronting the indicators of the company characteristics with the WLC elements results in bestfit indications for the WLC concept. Contrarily to other PPC evaluation schemes the framework considers variability indicators besides averages. Use of this framework for a medium sized MTO company demonstrates its suitability in getting a systematic and quick impression of the applicability of WLC. Essential elements are treated and assessed.

Optimal Cyclic Control of a Buffer Between Two Consecutive Non-Synchronized Manufacturing Processes

This thesis presents methods for efficiently controlling a buffer that is located between two non-synchronized manufacturing processes. Several machines with different cycle times and/or batch sizes perform each manufacturing process. The overall operation cycles every T time units. The first objective of the problem is to minimize the average buffer inventory level during one cycle. The second objective is to minimize the maximum inventory level observed at any point during the cycle. This new optimization problem has not been previously considered in the literature. An integer program is developed to model this problem. In addition, two heuristic methods—a simulated annealing algorithm and random algorithm—are devised for addressing this problem. Extensive experiments are conducted to compare the performance of four methods for attacking this problem: pure integer programming using the solver CPLEX; integer programming where CPLEX is initialized with a feasible solution; simulated annealing; and a random algorithm. The advantages and disadvantages of each method are discussed