Simulation for manufacturing system design and operation: Literature review and analysis

The body of research relating to the implementation of enterprise resource planning (ERP) systems in small- and medium-sized enterprises (SMEs) has been increasing rapidly over the last few years. It is important, particularly for SMEs, to recognize the elements for a successful ERP implementation in their environments. This research aims to examine the critical elements that constitute a successful ERP implementation in SMEs. The objective is to identify the constituents within the critical elements. A comprehensive literature review and interviews with eight SMEs in the UK were carried out. The results serve as the basic input into the formation of the critical elements and their constituents. Three main critical elements are formed: critical success factors, critical people and critical uncertainties. Within each critical element, the related constituents are identified. Using the process theory approach, the constituents within each critical element are linked to their specific phase(s) of ERP impleme...

Critical elements for a successful enterprise resource planning implementation in small-and medium-sized enterprises

Supply planning under uncertainties in MRP environments: A state of the art

https://isiarticles.com/bundles/Article/pre/pdf/1131.pdf

Managing uncertainty in ERP-controlled manufacturing environments in SMEs

Purpose – The aim of this paper is to investigate how enterprise resource planning (ERP) systems could create a competitive advantage for small and medium‐sized enterprises (SMEs). The objectives of this study are to examine how responsive and agile the existing ERP systems are to change and uncertainty, and to identify the types of change and uncertainty in SME manufacturing environments.Design/methodology/approach – A mixed methodology is used in this study, which involves literature review, questionnaire survey and follow‐up, in‐depth telephone interviews. An uncertainty diagnosing business model is applied to collect data from SME manufacturers in make‐to‐stock (MTS), make‐to‐order (MTO) and mixed mode (MM) manufacturing environments in a structured manner, and to analyse the effects of the underlying causes of uncertainty on product late delivery in MTS, MTO and MM manufacturing environments in SMEs. Some 108 enterprises responded (86 per cent response rate), of which 64 are SMEs. Analysis of varianc...

Change and uncertainty in SME manufacturing environments using ERP

Most identified research on uncertainty in material requirements planning (M RP)-controlled manufacturing environment, while purporting to represent such environment, does not truly model a multi-level dependent demand system, with multi-product and controlled by planned order release (POR) schedule based on planned lead times. This research has developed an MRP-controlled batch manufacturing simulation model using ARENA simulation software to represent such environment. In this paper, the delivery performance of the environment, measured with parts delivered late (PDL) and finished products delivered late (FPDL), when disturbed by uncertainty is examined. Design of experiments and simulation studies are carried out for these uncertainties: external late supply, internal late supply, planned set-up time exceeded, machine breakdowns, labour unavailability, tooling unavailability, demand batch size enlargement and customer design changes. Analysis of Variance (ANOVA) results identify significant effects from individual uncertainty as well as combination of uncertainties. The knock-on and the compound effects cause more PDL and FPDL. The main finding is that the higher is the level of the significant uncertainties, tile higher is the level of PDL and FPDL. It is suggested that MRP-controlled manufacturing enterprise must diagnose for the significant uncertainties, so that optimum use of buffer or slack can be implemented. Some practical implications of this work and required further work arc also discussed. (C) 2003 Elsevier Science Ltd. All rights reserved.

MRP-controlled manufacturing environment disturbed by uncertainty

This paper presents a new quantitative method and a holistic approach to assess the impact of supply chain uncertainty on customer delivery performance. We adopt system theory, Multi Criterion Decision Making (MCDM) theory – Analytical Hierarchy Process (AHP) and Theory of Constraints (TOC) as the underpinning theoretical frameworks for this new method. Some 72 UK companies have verified this method and we call this – the Supply Chain Uncertainty Impact Assessment (SCUIA) method. Some 66 types of supply chain uncertainty are assessed. This method involves three levels of assessment, namely: (1) relative percentage occurrences of each supply chain uncertainty, (2) relative upstream and downstream impact of each supply chain uncertainty and (3) relative likelihood of each underlying causes of supply chain uncertainty. The findings show that underlying causes of supply chain uncertainty with higher likelihood of occurrences do not necessarily result in greater impact on the downstream delivery performance in the supply chain. It can be concluded that decision makers should not use the likelihood of occurrence or impact criteria in isolation when devising solutions to tackle supply chain uncertainty, but to tackle those underlying causes of supply chain uncertainty that have both greater impact and higher likelihood of occurrence.

Performance prediction using supply chain uncertainty modelling

This paper proposes a knowledge management approach to tackle uncertainty in ERP-controlled manufacturing environment. The development of a knowledge-based ERP planning model and a knowledge-based ERP-controlled manufacturing simulation model within which knowledge of tackling underlying causes of uncertainty can be stored for future reference are discussed. A combination of explicit and tacit knowledge are created and applied to update the planned parameters and to decide for the appropriate buffering or dampening techniques to tackle the underlying causes of uncertainty. Results from interviews and simulation experiments of a real case company showed significant reduction in tardy delivery after the use of the knowledge management approach. Some underlying causes of uncertainty are found to be more likely to result in tardy delivery than the others, and some are found to be more receptive to the effects of the knowledge management approach, but it is shown that interactions are generally difficult to manage due to their unstable effects.

Tackling uncertainty in ERP-controlled manufacturing environment: A knowledge management approach

This research examines how and to what extent uncertainty affects SME manufacturers who plan and schedule their production using MRP, MRPII or ERP systems The results show consistent tardy delivery performance, reinforcing that there is a shortage of guidance and knowledge on how to tackle uncertainty in these SMEs We propose a business model for SME manufacturers to enable diagnosis of causes of uncertainty so that they can be dealt with effectively and efficiently The business model is applied using a questionnaire survey in 51 SMEs in the UK and the empirical results provide the SME manufacturers with a reference on which causes of uncertainty are more likely to result in tardy delivery Using a real case study, simulation modelling and experimental study, the results show that knowing the significant causes of uncertainty does add value to the decision-making of the SME manufacturers in tackling uncertainty

An empirical study of the effects of uncertainty on SME manufacturers

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 the 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 system between parts to be carried out effectively. The part-classification structure was programmed using Visual Basic Application (VBA) and was integrated into the work-to-list generated from a simulated materials requirements planning (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 detailed 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 nor sub-assembly 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 system parameters to study different or more complex cases.

/pdf/parts-verification-for-multi-level-dependent-demand-4kvuh8dyu2.pdf

S.C.L. Koh

Papers

MRP-controlled manufacturing environment disturbed by uncertainty

Performance prediction using supply chain uncertainty modelling

Tackling uncertainty in ERP-controlled manufacturing environment: A knowledge management approach

An empirical study of the effects of uncertainty on SME manufacturers

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