Simulation study of a kanban-controlled production system

Just-In-Time (JIT) has become one of the most popular production control philosophies in the last three decades. JIT did not find acceptance in the American companies until after the oil crisis. To be more flexible and to adapt quickly to changes in the market, many American companies took to the recourse of JIT and have been extremely successful. JIT systems typically employ kanbans as a means of inventory control. A JIT system operating under kanban control is commonly termed as a pull system due to the way in which succeeding stages trigger production at preceding stages. Owing to this close dependence of stages on a production line, the performance of a kanban controlled JIT system is sensitive to various kinds of stochasticity.;It was the aim of this thesis to characterize such inventory systems under different conditions. In particular, the research focused on kanban controlled feeder lines. Various design and operational parameters like number of kanbans, number of stages, number of product types assembled and processing time variability were studied. Metrics such as time-in-system, throughput, kanban waiting time, utilization, stockout and work-in-process were used to measure the performance of the system. A simulation model was constructed to model the system and to carry out the various experiments conducted as part of this research. It was observed that time in system was significantly affected by de number of kanbans, number of product types and the level of processing time variability at the stages. The analysis of work-in-process indicated that it was affected by the number (of kanbans, number of stages, number of product types and the level of processing time variability at the stages. The factors affecting stockout were the number of kanbans and the number of products. Kanban waiting times were impacted by the number of kanbans, number of stages, number of product types and the level of processing time variability

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

Control of Supply Chain Systems by Kanban Mechanism.

This research studies the control mechanism of a supply chain system to operate it efficiently and economically under the just-in-time (JIT) philosophy. To implement a JIT system, kanbans are employed to link different plants\u27 production processes in a supply pipeline. Supply chain models may be categorized into single-stage, multi-stage, and assembly-line types of production systems. In order to operate efficiently and economically, the number of kanbans, the manufacturing batch size, the number of batches, and the total quantity over one period are determined optimally for these types of supply chains. The kanban operation at each stage is scheduled to minimize the total cost in the synchronized logistics of the supply chain. It is difficult to develop a generalized mathematical model for a supply chain system that incorporates all its salient features. This research employs two basic models to describe the supply chain system: a mathematical programming model to minimize the supply chain inventory system cost and a queuing model to configure the kanban logistic operations in the supply pipeline. A supply chain inventory system is modeled as a mixed-integer nonlinear programming (MINLP) that is difficult to solve optimally for a large instance. A branch-and-bound (B&B) method is devised for all versions of it to solve the MINLP problems. From the solution of MINLP, the number of batches in each stage and the total quantity of products are obtained. Next, the number of kanbans that are needed to deliver the batches between two adjacent stages is determined from the results of the MINLP, and kanban operations are fixed to efficiently schedule the dispatches of work-in-process. The new solutions result in a new line configuration as to the number and size of kanbans that led to simpler dispatch schedules, better material handling, reduction in WIP and delivery time, and enhancement of the overall productivity. These models can help a manager respond quickly to consumers\u27 need, determine the right policies to order the raw material and deliver the finished goods, and manage the operations efficiently both within and between the plants

Evaluation of Pull Production Control Strategies Under Uncertainty: An Integrated Fuzzy Ahp-Topsis Approach

Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty. Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system. Practical implications: To examine the approach a real case from automobile electro-mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand. Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteriaPeer Reviewe

Determining Kanban Size Using Mathematical Programming and Discrete Event Simulation for a Manufacturing System with Large Production Variability

In order to become more competitive and aggressive in the market place it is imperative for manufacturers to reduce cycle time, limit work-in-process, and improve productivity, responsiveness, capacities, and quality. One manner in which supply chains can be improved is via the use of kanbans in a pull production system. Kanbans refer to a card or signal for productions scheduling within just-in-time (JIT) production systems to signal where and what to produce, when to produce it, and how much. A Kanban based JIT production system has been shown to be beneficial to supply chains for they reduce work-in-process, provide real time status of the system, and enhance communication both up and down stream. While many studies exist in regards to determining optimal number of kanbans, types of kanban systems, and other factors related to kanban system performance, no comprehensive model has been developed to determine kanban size in a manufacturing system with variable workforce production rate and variable demand pattern. This study used Stewart-Marchman-Act, a Daytona Beach rehabilitation center for those with mental disabilities or recovering from addiction that has several manufacturing processes, as a test bed sing mathematical programming and discrete event simulation models to determine 2 the Kanban size empirically. Results from the validated simulation model indicated that there would be a significant reduction in cycle time with a kanban system; on average, there would be a decrease in cycle time of nine days (almost two weeks). Results were discussed and limitations of the study were presented in the end

Card-based production control:a review of the control mechanisms underpinning Kanban, ConWIP, POLCA and COBACABANA systems

Since the emergence of Kanban, there has been much research into card-based control systems. This has included attempts to improve Kanban and/or develop alternative systems, particularly ConWIP (i.e. Constant Work-In-Process), POLCA (i.e. Paired-cell Overlapping Loops of Cards with Authorisation) and COBACABANA (i.e. Control of Balance by Card-Based Navigation). Yet, to date, no unifying review of the mechanisms underpinning these systems has been presented. As a consequence, managers are not provided with sufficient support for choosing an appropriate system for their shop; and researchers lack a clear picture of how the mechanisms compare, leading to several misconceptions. This paper reviews the control mechanisms underpinning the Kanban, ConWIP, POLCA and COBACABANA systems. By comparing the ‘control mechanism’ (i.e. the loop structure and card properties) and ‘contextual factors’ (i.e. routing variability, processing time variability, and whether stations are decoupled by inventory or the flow of jobs is controlled), we provide managers with guidance on which system to choose. For research, we show for example that most criticisms put forward against Kanban systems, e.g. to justify the development of ConWIP, POLCA or COBACABANA, only apply to work-in-process Kanban systems and not to production Kanban systems. Future research directions for each control system are outlined

Analysis of Delayed Product Differentiation under a CONWIP Policy

Delayed product differentiation (DPD) increases manufacturers’ competitiveness by enabling faster responses to demand changes and has been shown to require less work in process (WIP) in base-stock systems. We model a system of two products using three CONWIP loops to represent the common processes and the differentiated processes for each product. DPD converts some differentiated processes to common ones. A nonlinear programming (NLP) model can determine kanban counts for each loop to achieve specified throughput bounds. Because these bounds are not tight, a heuristic algorithm starts from the NLP solution and adjusts the kanbans according to simulation. The results indicate that DPD reduces the amount of WIP necessary to achieve a specified throughput

Customized Pull Systems for Single-Product Flow Lines

Traditionally pull production systems are managed through classic control systems such as Kanban, Conwip, or Base stock, but this paper proposes ‘customized’ pull control. Customization means that a given production line is managed through a pull control system that in principle connects each stage of that line with each preceding stage; optimization of the corresponding simulation model, however, shows which of these potential control loops are actually implemented. This novel approach may result in one of the classic systems, but it may also be another type: (1) the total line may be decomposed into several segments, each with its own classic control system (e.g., segment 1 with Kanban, segment 2 with Conwip); (2) the total line or segments may combine different classic systems; (3) the line may be controlled through a new type of system. These different pull systems are found when applying the new approach to a set of twelve production lines. These lines are configured through the application of a statistical (Plackett-Burman) design with ten factors that characterize production lines (such as line length, demand variability, and machine breakdowns).Pull production / inventory;sampling;optimization;evolutionary algorithm

Analysis of delayed product differentiation under pull type policies

Delayed product differentiation (DPD) increases manufacturers\u27 competitiveness in the market by enabling them to more quickly respond to changes in customers\u27 demands. DPD has also been shown to require less Work-in-Process (WIP) than a non-DPD setup in some cases. Previous research was mainly focused on the level of semi-finished and/or finished good inventory under a base-stock policy. The control of WIP inventory was not considered. DPD may also improve response times under pull inventory control schemes, in which the amount of WIP is controlled directly. These systems can be modeled as closed queueing networks in which a fixed number of kanbans circulate as customers among each set of one or more processing stages.;In this study, we first developed models to analyze the performance of simple kanban and CONstant-WIP (CONWIP) controlled systems and set the number of kanbans to achieve a specified performance level. The models help us better understand the behavior of pull systems. The performance evaluation method uses nonlinear programming (NLP) models to bound the throughput for fixed number of kanbans or minimize the number of kanbans necessary to achieve a specified throughput. The model shows how random supplies and demands prevent equilibrium from occurring in a single-stage kanbans system.;We studied a model for a system of two products with unlimited supply and demand using three CONWIP loops to represent the common processes and the differentiated processes for each product. The same system after DPD has more common processes and fewer differentiated processes. The NLP model can determine numbers of kanbans for each loop to achieve specified throughput targets. Because the throughput bounds are not as tight as desired, we developed a heuristic algorithm that starts from the NLP solution and adjusts the kanbans using simulation to evaluate the performance. A comparison of the result of the heuristic algorithm for the systems with and without DPD indicates that DPD reduces the amount of WIP necessary to achieve a specified throughput. Furthermore, we show how models of systems with similar structure can be generalized