Scheduling of non-repetitive lean manufacturing systems under uncertainty using intelligent agent simulation

World-class manufacturing paradigms emerge from specific types of manufacturing systems with which they remain associated until they are obsolete. Since its introduction the lean paradigm is almost exclusively implemented in repetitive manufacturing systems employing flow-shop layout configurations. Due to its inherent complexity and combinatorial nature, scheduling is one application domain whereby the implementation of manufacturing philosophies and best practices is particularly challenging. The study of the limited reported attempts to extend leanness into the scheduling of non-repetitive manufacturing systems with functional shop-floor configurations confirms that these works have adopted a similar approach which aims to transform the system mainly through reconfiguration in order to increase the degree of manufacturing repetitiveness and thus facilitate the adoption of leanness. This research proposes the use of leading edge intelligent agent simulation to extend the lean principles and techniques to the scheduling of non-repetitive production environments with functional layouts and no prior reconfiguration of any form. The simulated system is a dynamic job-shop with stochastic order arrivals and processing times operating under a variety of dispatching rules. The modelled job-shop is subject to uncertainty expressed in the form of high priority orders unexpectedly arriving at the system, order cancellations and machine breakdowns. The effect of the various forms of the stochastic disruptions considered in this study on system performance prior and post the introduction of leanness is analysed in terms of a number of time, due date and work-in-progress related performance metrics

Optimal Kanban Number: An Integrated Lean and Simulation Modelling Approach

Kanban is credited as a major means to controlling the inventory within a manufacturing system. Determining the optimum number of Kanban is of great interest for manufacturing industries. To fulfill this aim, an integrated modelling approach using discrete-event simulation technique and Kanban Lean tool is developed for a pull system ensuring an optimum Kanban number. This research has developed a base-case simulation model which was statistically validated using ANOVA. Initial Kanban number obtained from the mathematical model of Toyota motor company is used to obtain initial results. A Kanban integrated simulation model is developed that employed the idea of pull system that required the arrival of a customer for a product and Kanban pair to proceed through the production steps. The Kanban-Simulation integrated model is further used to test the effect of different Kanban numbers to obtain the best value of Kanban which is selected as 275. This approach has been applied on a case company involved in the manufacturing of agricultural and construction metal hand tools. The optimum Kanban number is selected by simulating the model about three performance indicators: customer waiting time, weekly throughput, and Work-in-progress. The analysis of the results obtained from the proposed integrated Kanban-simulation model showed a 76.7% reduction in the inventory level. The integrated Kanban-simulation model has also given a minimum customer waiting time of 0.84 Hrs. and a maximum throughput value of 737 Pcs of shovels. The integrated Kanban-simulation model is useful for manufacturing industries working to avoid overproduction waste and greatly reduce inventory costs

Multiproduct supplye chain analysis through by simulation with kanban and EOQ system

This work reviews lean literature on the supply chain focused on the operational approach, from the lean management to the Kanban system. But, the main issue of this work is to analyze the behavior of a lean supply chain using a Kanban system managing the planning in two different ways. The difference between both is related to the production order or sequence to follow: the product with fewer inventories in stock (the most critical to run out) or the one which requires less set-up time to optimize unproductive times. The study the behavior of the supply chain, it would be done through simulation with many different scenarios: 5 different demands, each one with two coefficients of variance, 4 different batch sizes, 4 different compositions of production and process saturation and ensuring different service levels between 92% and 98%. To compare these supply chain models, an approach of the supply chain using the EOQ (Economic Order Quantity) system will be also simulated in the same conditions but with one batch size, the most economic one

Quantitative modelling approaches for lean manufacturing under uncertainty

[EN] Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the opera-tions that do not add value to the product or processes to increase the value of each performedactivity. Here the main motivation is to study how quantitative modelling approaches can supportLM tools even under system and environment uncertainties. The main contributions of the articleare: (i) providing a systematic literature review of 99 works related to the modelling of uncertaintyin LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifyingLM works under uncertainty; and (iv) identify quantitative models and their solution to deal withuncertainty in LM environments by identifying the main variables involved. Hence this article pro-vides a conceptual framework for future LM quantitative modelling under uncertainty as a guide foracademics, researchers and industrial practitioners. The main findings identify that LM under uncer-tainty has been empirically investigated mainly in the US, India and the UK in the automotive andaerospace manufacturing sectors using analytical and simulation models to minimise time and cost.Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce wastein a context of system uncertainty.The research leading to these results received funding fromthe project 'Industrial Production and Logistics Optimizationin Industry 4.0' (i4OPT) (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; and grant PDC2022-133957-I00 funded by MCIN/AEI /10.13039/501100011033 and by European Union Next Generation EU/PRTR.Rojas, T.; Mula, J.; Sanchis, R. (2023). Quantitative modelling approaches for lean manufacturing under uncertainty. International Journal of Production Research. 1-27. https://doi.org/10.1080/00207543.2023.229313812

Fast changeovers using AIV and a tool platform

Fast changeover is changing from one process or machine to another line, process, or machine to produce different products or services. Lean aims at reducing waste, improving productivity, and eliminating non-value-added activities to the customer. Whilst achieving sustained continual improvement in certain activities and process of an organization. This project focuses on implementing the methodologies used in lean to improve a manufacturing process that has a changeover, and integrate an AIV (Automated Intelligent Vehicle) with a tool platform or top module into the same process. After careful literature review and research, I was able to set up a process in the machining laboratory of ‘UiT Universitet i Norge, Narvik,’ that is of value to a customer. I identified the step with the need of a fast tool change and optimized the entire process using a software called Simul8. After running the simulation for a period of two weeks and doing some analysis for the processes, I achieved an optimal solution that ensures that all bottlenecks and buffers were eliminated. I also conducted a smooth flow of the process, improving productivity, efficiency, and reduction in changeover time

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction