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

New species of hybrid pull systems

production models;control systems;simulation

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).

Designing pull production control systems:Customization and robustness

In this dissertation we address the issues of selecting and configuring pull production control systems for single-product flowlines. We start with a review of pull systems in the literature, yielding a new classification. Then we propose a novel selection procedure based on a generic system that we test on a case also studied in the literature. We further study our procedure for a variety of twelve production lines. We find new types of pull systems that perform well. Next, we raise the issue of designing pull systems under uncertainty. We propose a novel procedure to minimize the risk of poor performance. Results show that risk considerations strongly influence the selection of a specific pull system

On the Interface Between Operations and Human Resources Management

Operations management (OM) and human resources management (HRM) have historically been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications and staffing. Human responses to operations management systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields

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

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

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

Designing a robust production system for erratic demand environments.

Production systems must have the right type of material in the right quantities when required for production. They must minimize the work in progress while ensuring no stock-outstock-out occurs. While these twin opposing goals are achievable when demand is stable, they are difficult to realize under an erratic demand pattern. This dissertation aims to develop a production system that can meet erratic demands with minimal costs or errors. After a detailed introduction to the problem considered, we review the relevant literature. We then conduct a numerical analysis of current production systems, identify their deficiencies, and then present our solution to address these deficiencies via the ARK (Automated Replenishment System) technique. This technique is applied to a real-world problem at Methode Engineering ©. We conclude by detailing the scientific benefit of our technique and proposing ideas for future research