Manufacturing System Lean Improvement Design Using Discrete Event Simulation

Lean manufacturing (LM) has been used widely in the past for the continuous improvement of existing production systems. A Lean Assessment Tool (LAT) is used for assessing the overall performance of lean practices within a system, while a Discrete Event Simulation (DES) can be used for the optimization of such systems operations. Lean improvements are typically suggested after a LAT has been deployed, but validation of such improvements is rarely carried out. In the present article a methodology is presented that uses DES to model lean practices within a manufacturing system. Lean improvement scenarios are then be simulated and investigated prior to implementation, thereby enabling a systematic design of lean improvements

Variation Modeling of Lean Manufacturing Performance Using Fuzzy Logic Based Quantitative Lean Index

The lean index is the sum of weighted scores of performance variables that describe the lean manufacturing characteristics of a system. Various quantitative lean index models have been advanced for assessing lean manufacturing performance. These models are represented by deterministic variables and do not consider variation in manufacturing systems. In this article variation is modeled in a quantitative fuzzy logic based lean index and compared with traditional deterministic modeling. By simulating the lean index model for a manufacturing case it is found that the latter tend to under or overestimate performance and the former provides a more robust lean assessment

Improving the efficacy of the lean index through the quantification of qualitative lean metrics

Multiple lean metrics representing performance for various aspects of lean can be consolidated into one holistic measure for lean, called the lean index, of which there are two types. In this article it was established that the qualitative based lean index are subjective while the quantitative types lack scope. Subsequently, an appraisal is done on techniques for quantifying qualitative lean metrics so that the lean index is a hybrid of both, increasing the confidence in the information derived using the lean index. This ensures every detail of lean within a system is quantified, allowing daily tracking of lean. The techniques are demonstrated in a print packaging manufacturing case

A lean assessment tool based on systems dynamics

Lean manufacturing is synonymous with a set of practices used in the identification and elimination of waste related with the manufacturing system, and focusing on what creates value for the customer. Lean assessment tools enable an overall audit of the performance of lean practices, and so are able to identify lean improvements. The interactions between lean practices and their improvements are often latent and need to be investigated: a systems approach can be used to disclose these hidden interactions. In this article, system dynamics is used as a lean assessment tool to assess and improve lean performance for a print packaging manufacturing system

The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty

Schedule performance measurement based on statistical process control charts

In a job-shop manufacturing environment, achieving a schedule that is on target is difficult due to the dynamism of factors affecting the system, and this makes schedule performance measurement systems hard to design and implement. In the present paper, Statistical Process Control charts are directly applied to a scheduling process for the purpose of objectively measuring schedule performance. SPC charts provide an objective and timely approach to designing, implementing and monitoring schedule performance. However, the use of Statistical Process Control charts requires an appreciation of the conditions for applying raw data to SPC charts. In the present paper, the Shewart’s Individuals control chart are applied to monitor the deviations of actual process times from the scheduled process times for each job on a process machine. The Individuals control charts are highly sensitive to non-normal data, which increases the rate of false alarms, but this can be avoided using data transformation operations such as the Box-Cox transformation. Statistical Process Control charts have not been used to measure schedule performance in a job shop setting, so this paper uniquely contributes to research in this area. In addition, using our proposed methodology enables a scheduler to monitor how an optimal schedule has performed on the shop floor, study the variations between planned and actual outcomes, seek ways of eliminating these variations and check if process improvements have been effective

The implementation of 5S lean tool using system dynamics approach

The 5S (sort, set, shine, standardize and sustain) lean tool has been known to improve system performance. In the current study, the short run dynamic implications of the sorting aspect of 5S is investigated using system dynamics. A system dynamics model is developed for a manufacturing case study and simulated to establish the effect of sorting activity on manufacturing throughput. The purpose was to assess, in advance, the system performance outcomes when 5S practices are improved. The simulation results were the stimulus for real life improvements in the system because the simulation results were able to mimic the real-life outcomes. While the simulation results encourage further improvements to be implemented, the model developed in the current paper is replicable in other instances as the variables used in the model are generic and common to most types of manufacturing systems, particularly those new to lean practices. The dynamic analyses of 5S lean practices is not common. The study also reveals some interesting relationships between 5S and other lean practices and between 5S and system performance

A framework for designing data pipelines for manufacturing systems

Data pipelines describe the path through which big data is transmitted, stored, processed and analyzed. Designing an appropriate data pipeline for a specific data driven manufacturing project can be challenging, whereas there is a paucity of frameworks to guide one in the design. In this research we develop a framework for designing data pipelines for manufacturing systems. The framework consists of a template for selecting key layers and components that make up big data pipelines in manufacturing systems. A use case is presented to provide an illustrative guideline for its application. Benefits of the framework and future directions are discusse

Analysis of lean manufacturing strategy using system dynamics modelling of a business model

A system dynamics-based methodology is described for analysing the impact of lean manufacturing strategies on a company's business performance, using Business Model Canvas perspective. A case study approach is used to describe the methodology which consists of conceptualizing a system dynamics model on the basis of Business Model Canvas. The base system dynamics model is elaborated to include variables and concepts that consider the effects of lean manufacturing metrics on business performance. In the modelling experimentation, the lean manufacturing metrics are made to take on likely values one would expect if certain lean practices are initiated or improved. The experimental results provide one with the likely impact on business performance, if one were to improve lean manufacturing practices. The simulation results for the case study show that lean improvements, on the short-run, have a significant impact on business performance, but on the long-run, the impact is only marginal. The described methodology provides one with a structured format for investigating the impact of lean practices on business performance. Although the developed system dynamics model was built with generality in mind, it remains to be reproduced in other settings to test its replicability. The methodology enables an organization target which lean improvements to initiate based on their strategic impact on the business. Limited studies exist where system dynamics and business models are combined to test the strategic impact of lean manufacturing

Machine learning algorithms comparison for manufacturing applications

With the vast amount of data available, and its increasing complexity in manufacturing processes, traditional statistical approaches have started to fall short. This is where machine learning plays a key role, addressing the challenges by bringing the ability to analyse large and complex datasets from multiple sources, finding non-linear and intricate patterns on data, relationships between several factors and their influence on the manufacturing process outputs. This paper demonstrates the advantages and applications of using supervised machine learning techniques in the manufacturing industry. It focuses on binary classification and compares the performance of three different machine learning algorithms: logistic regression, support vector machine, and neural networks. A case study has been conducted on a manufacturing company, using the techniques and algorithms mentioned. The case study focuses on analysing the relationship between different manufacturing process variables and their impact on one key output variable of a product, which in this case is the result of a quality test that measures product performance. The modelling problem has been oriented towards a Boolean goal to predict whether the parts will pass this test