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

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

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

Modeling and Analysis of Manufacturing Systems with Multiple-Loop Structures

Kanban and Constant Work-In-Process (CONWIP) control methods are designed to impose tight controls over inventory, while providing a satisfactory production rate. This paper generalizes systems with kanban or CONWIP control as assembly/disassembly networks with multiple-loop structures. We present a stochastic mathematical model which integrates the information control flows into material flows. Graph theory is used to analyze the multiple-loop structures. An efficient analytical algorithm is developed for evaluating the expected production rate and inventory levels. The performance of the algorithm is reported in terms of accuracy, reliability and speed.Singapore-MIT Alliance (SMA

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