Pull Manufacturing System Research Articles

Push and Pull Hybridization Manufacturing Strategy in Medical Diagnostic Equipment Industry

Abstract: The global manufacturing industry employs two main strategies for production: push and pull manufacturing systems. Each strategy has its own set of advantages and disadvantages. However, in this paper, a hybrid push and pull system model is proposed, aiming to capitalize on the strengths of both strategies while mitigating their weaknesses. The proposed model specifically focuses on the medical diagnostic equipment industry, where the annual production volume is relatively low, typically under 200 products. The objective of the proposed model is to improve various aspects of the production system compared to the current approaches used in the industry. These improvements primarily revolve around optimizing production line efficiency, reducing the lead time between customer orders and product delivery, and enhancing overall productivity. By incorporating elements from both push and pull systems, the hybrid model aims to achieve these goals more effectively. The results of the study indicate that the proposed hybrid model outperforms the existing production system in terms of time optimization in the production line, minimizing the lead time between customer orders and product delivery, and increasing the productivity of the production line. These findings suggest that the hybrid model can offer significant advantages and improvements over traditional production approaches in the medical diagnostic equipment industry.

A Multi-Objective Optimization of Secure Pull Manufacturing Systems

This paper aims to optimize the losses of the current manufacturing systems to perform the Just in Time (JIT) system and minimize the time, labor, and material in the manufacturing process. Pull systems, which have a famous name of the Smart Kanban system, are one of the methodologies to implement the JIT system. The setting of manufacturing system frameworks offers intriguing capabilities to improve the performance of pull-controlled creation frameworks. Smart Kanban can be utilized, rather than actual cards, and the data conversion about the manufacturing settings, is accessible through sensors. Since a Just-in-Time system does not include a buffer, any interruption to the system can cause the process to stop quite quickly, so security is necessary for the JIT improvement. However, those systems have recently been unguarded to security threats as they introduce general-purpose technologies. From the viewpoint of the system, a security measure is implemented for pull system methods to check for potential threats and analyze vulnerabilities of the pull system. The Flower Pollination Algorithm (FPA) is used as a novel multiobjective technique to optimize different types of cost and time. The experimental results show that the proposed algorithm has robust security efficiency, as well as reduces cost and time consumption.

Reinforcement learning-based adaptive production control of pull manufacturing systems

ABSTRACTThis research proposes a methodology based on Reinforcement Learning for deriving optimal adaptive control policies for CONWIP-type manufacturing systems. The Reinforcement Learning agent interacts with the production system and learns the best actions for each state. The goal is to minimize the total, average cost that consists of holding and backorder costs. The Reinforcement Learning-based approach is compared to the standard CONWIP, the Dynamic CONWIP and a special case of the Generalized Kanban system in a series of simulation experiments. The proposed approach is found to outperform all alternative control policies in all cases. The Generalized Kanban policy is reported to be a good approximation of the control policies that are derived by the Reinforcement Learning approach in situations where the demand rate is low. A numerical investigation of the total cost function properties for the CONWIP, Dynamic CONWIP and Generalized Kanban is also provided.

An approach of designing CONWIP loop for assembly system in one-of-a-kind production environment

Pursuit of the shortest cycle time does not enhance benefits in one-of-a-kind production (OKP) environment. The quality of services is affected by the delivery on time for OKP enterprise, and a well-designed constant work in process (CONWIP) loop structure can effectively improve the performance of assembly system in OKP. The designed CONWIP loop structures have been tested by simulation under 2 assembly proportion conditions and 14 situations of fluctuation combinations, and analysed relationship between cycle time and punishment of tardiness. Simulation test shows that some of CONWIP policies have stronger robustness, which can perform well in several fluctuation situations. The authors propose an idea of dynamic configuration for CONWIP control. Furthermore, the design procedure of CONWIP can provide a reference and guidance for the application of CONWIP. This article extends the knowledge of OKP shop floor control and promotes implementation of CONWIP in pull manufacturing system.

Dynamic card control strategy in pull manufacturing systems

The dynamic control of cards in pull systems can improve the performance of a manufacturing system in a turbulent demand condition. The traditional fixed card approach can reduce the performance of a system in some cases. This article presents a dynamic card control methodology in order to keep a high level of performance measures. The proposed methodology is based on the observation of customer demand in order to detect the fluctuations and adjust the number of cards. The demand fluctuations is evaluated based on two moving average: the first on a medium horizon and the second on a short horizon. The comparison between the two moving averages is the signal of the controller developed. A simulation environment has been developed to test the proposed methodology in both kanban and Constant Work In Process (CONWIP) systems. The simulations conducted in several scenarios highlight the better performance obtained using the controller. The main performance measures investigated are: the throughput, the queue of orders waiting to be processed, the throughput time of the orders, the number of cards used and the orders delayed. The benefits are more relevant in the case reliability reduction of the manufacturing system.

A Production–Inventory Model for a Push–Pull Manufacturing System with Capacity and Service Level Constraints

We study a hybrid push–pull production system with a two‐stage manufacturing process, which builds and stocks tested components for just‐in‐time configuration of the final product when a specific customer order is received. The first production stage (fabrication) is a push process where parts are replenished, tested, and assembled into components according to product‐level build plans. The component inventory is kept in stock ready for the final assembly of the end products. The second production stage (fulfillment) is a pull‐based assemble‐to‐order process where the final assembly process is initiated when a customer order is received and no finished goods inventory is kept for end products. One important planning issue is to find the right trade‐off between capacity utilization and inventory cost reduction that strives to meet the quarter‐end peak demand. We present a nonlinear optimization model to minimize the total inventory cost subject to the service level constraints and the production capacity constraints. This results in a convex program with linear constraints. An efficient algorithm using decomposition is developed for solving the nonlinear optimization problem. Numerical results are presented to show the performance improvements achieved by the optimized solutions along with managerial insights provided.

Infinite horizon production planning with periodic demand: solvable cases and a general numerical approach

In this paper, we consider a single part-type pull manufacturing system, which controls its production rates in response to periodic demand. When tracking the demand results in a product surplus, an inventory storage cost is incurred. Likewise, if an overall shortage occurs then a backlog cost is paid. In addition, production costs accrue when the system is not idle. Given an infinite planning horizon, the objective is to determine the cyclic production rates in order to minimize the total cost. With the aid of the maximum principle, extremal behavior of the system is studied and the continuous-time production planning problem is reduced to a discrete problem with a limited number of switching points at which time the production rates change. Using this result, an efficient numerical algorithm is proposed, which will yield an approximation to the optimal solution within any desired level of accuracy. In addition, we determine the analytical solution to the problem for three special cases: (i) the system capacity is not limited and the inventory storage cost factor is equal to the backlog cost factor; (ii) the production cost is negligible; and (iii) the surplus and shortages costs are negligible.

A simulation generator for dual-card kanban-controlled flow shops

This paper introduces a simulation generator for dual-card, kanban-controlled flow shops. Modelling kanban-controlled, pull manufacturing systems is a challenging task. The simulation generator eliminates modelling and the coding stages of simulation analysis and generates codes in SIMAN simulation language. An easy to use data-driven environment allows users to develop, edit, save and execute models without requiring any programming skills. The simulation generator is capable of simulating multi-product, multi-stage flow shops with multiple kanban cards (withdrawal and production order kanbans) and variable container sizes.

Optimal control of pull manufacturing systems

We consider the problem of optimal control of pull manufacturing systems. We study a fluid model of a flow shop, with buffer holding costs nondecreasing along the route. The system is subject to a constant exogenous demand, thus incurring additional shortfall/inventory costs. The objective is to determine the optimal control for the production rate at each machine in the system. We exhibit a decomposition of the flow shop into sections of contiguous machines, where, in each section, the head machine is the bottleneck for the downstream system. We exhibit the form of an optimal control and show that it is characterized by a set of times, one for each head machine. Machines which are upstream of a head machine simply adopt a just-in-time production policy. The head machines initially stay idle for a period equal to their deferral time and thereafter produce as fast as possible, until the initial shortfall is eliminated. The optimal values of these deferral times are simply obtained by solving a set of quadratic programming problems. We also exhibit special cases of re-entrant lines, for which the optimal control is similarly computable.

ECONOMIC PRODUCTION QUOTAS FOR PULL MANUFACTURING SYSTEMS

We consider the problem of using “safety capacity” to ensure due date integrity in a pull manufacturing system and quantify the basic tradeoff between lost revenue opportunity and overtime costs. In this context, we address the question of when it is economically attractive to use “under capacity scheduling” and the problem of setting economic production quotas. We develop four models for addressing the quota setting problem. The first three assume that quota shortfalls cannot be carried over to the next regular time production period. Models 1 and 3 assume that these shortages are made up on overtime and incur fixed or fixed plus variable costs. Model 2 does not use a capacity buffer and treats shortages as lost sales. Finally, Model 4 assumes that shortages can be backlogged to the next regular time production period at a cost. For this model, we compute both an optimal quota and an overtime “trigger,” which represents the minimum shortage for which overtime is used. We give computational results that i...

Simulation of an apparel assembly cell with walking workers and decouplers

Abstract In the apparel industry, cells for manufacturing and assembly are called modules. A simulation model has been developed to study the behavior of a unique module that employed five multifunctional, walking workers and 13 sewing stations with great variations in processing times. Cells are the major components in integrated pull manufacturing systems (IPMSs). IPMSs are the designs for the factories with a future.