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Push and Pull Production Systems
Abstract
The terms push and pull are used to describe alternative systems for executing the production process in manufacturing organizations. A “push” system is based on forecasted demand that is completed and sent to the next work station or in the case of the final work station is pushed to finished goods inventory. On the other hand, in a “pull” system, the movement of work is based on the requirements of the following work station. Each succeeding workstation pulls (demands) output from the previous workstation as needed. The next work station determines when and how much output is requested. The output from the final workstation is pulled by customer demand or the master production schedule.
The choice between the push and pull systems is based on firm's production–inventory relationship. Firms that have highly repetitive production processes and well‐defined work flows of standardized items require the pull system based on the need for tighter control of inventory and output at the work stations. On the other hand, production processes with long lead times, accurate demand forecasts, a large number of products produced on common production processes, and a diverse customer base tend to use the push systems.
... T HE topic of this paper is the mining of data collected through RFID from schedule-based systems. A schedulebased system is a system that operates on (or contains within) a schedule of events and breaks at particular time intervals [1], [2]. Figure 1 illustrates a schedule-based system, which is characterized by a set of entities (or resources) I entering and exiting a particular set of locations that have events J 1 taking place in them according to a schedule. ...
A schedule-based system is a system that operates on or contains within a schedule of events and breaks at particular time intervals. Entities within the system show presence or absence in these events by entering or exiting the locations of the events. Given radio frequency identification (RFID) data from a schedule-based system, what can we learn about the system (the events and entities) through data mining? Which data mining methods can be applied so that one can obtain rich actionable insights regarding the system and the domain? The research goal of this paper is to answer these posed research questions, through the development of a framework that systematically produces actionable insights for a given schedule-based system. We show that through integrating appropriate data mining methodologies as a unified framework, one can obtain many insights from even a very simple RFID dataset, which contains only very few fields. The developed framework is general, and is applicable to any schedule-based system, as long as it operates under certain basic assumptions. The types of insights are also general, and are formulated in this paper in the most abstract way. The applicability of the developed framework is illustrated through a case study, where real world data from a schedule-based system is analyzed using the introduced framework. Insights obtained include the profiling of entities and events, the interactions between entity and events, and the relations between events.
Purpose :
The purpose of this paper is to develop and validate a scale measurement of supply chain operations reference (SCOR)-related performance indicators and proposed constructs, SCOR-related performance indicators as practices within the Indian manufacturing sector.
Design/methodology/approach :
A literature-based model on SCOR processes with five constructs and respective performance indicators was empirically validated by using a structured questionnaire. A total of 155 respondents among Indian manufacturing sector participated in this research, and the returned questionnaires were analyzed by using structural equation modeling.
Findings :
The study established a relationship among the SCOR-related performance indicators and overall supply chain performance indicators (OSCPI). The moderation effect of demographic characteristics, namely, employee size, company age and type of company showed significant differences between SCOR-related performance indicators and overall supply chain indicators.
Research limitations/implications :
The scope of the study is limited to specific Indian manufacturing firms. The survey could not represent whole population of manufacturing sector.
Practical implications :
The findings assist managers/supply chain practitioners in improving the performance measures identified using the standard framework, i.e., SCOR processes, overall supply chain performance measures as standard practices for Indian manufacturing sector for a profitable and sustainable business growth in global environment.
Originality/value :
This research holds a value for suggested practices under SCOR processes and the proposed model for OSCPI, a path finder/performance measurement tool for supply chain professionals in the Indian context.
Shop-floor control (SFC) is an important element in managing manufacturing system operations in order to faithfully execute production plans. In this article, the functions and challenges encountered in typical SFC systems are presented along with their evolution from classical centralized approaches to more distributed approaches. An example of modern SFC is used to illustrate the manner in which such systems increasingly encompass manufacturing execution system and supervisory control and data acquisition levels of enterprise architectures. Some recent developments in feedback control approaches for SFC are also presented along with architectural, functional, technological, and human-centric considerations.
A hybrid process structure, a continuous flow followed by a batch process, is used in the primary aluminum production. The continuous flow provides a steady stream of Work-In-Process (WIP) to the process; but the WIP is usually delayed before it can move to the batch process. This leads to increased flow times, increased inventories, and inefficient use of resources. In such hybrid structures, process synchronization can be achieved by carefully scheduling the production and allocating the resources. We study the performances of two alternative production-scheduling policies and two alternative resource allocation mechanisms in the primary aluminum production process, which is a hybrid process. First, we build a simulation model that can represent a nearly realistic primary aluminum production setting under various design parameters. Second, we show that the production scheduling policy led by the latter stage of the production, significantly improves the process performance compared to a decentralized policy, in which each stage makes its own schedule. Third, we show that the amount of resources at the buffer zone in between the two stages of the process is very critical for the process performance; and we quantify its benefits through numerical work. Finally, we suggest that dedicating common resources, such as trucks, to individual stages of the process slightly improves the process performance as opposed to pooling those resources.
The terms “push” and “pull” have been used to describe a wide variety of manufacturing and distribution environments. To some, the distinction refers to a specific attribute which can be identified by observing the mechanisms for controlling material flow on the factory floor. To others push and pull can be defined in terms of a specific policy for the management of inventories and production schedules. Finally there are skeptics who maintain that the push/pull dichotomy is a fiction created by academics or unscrupulous consultants to promote their latest theories or systems.
In this paper, we review the various definitions of push and pull in operations literature. It is clear from this review that the attributes which distinguish pull systems from push systems are not well understood.
The purpose of this paper is twofold. First, we clarify the meaning of push and pull in manufacturing and distribution systems. This clarification gives rise to a framework for push and pull that can serve as a classification scheme for material control systems. While we do not claim that our framework sufficiently classifies all material control systems, we demonstrate its application to a number of common systems. Thus, this framework can be used to analyze and compare management procedures for different classes of material control policies (such as MRP, kanban, base stock). This analysis is particularly useful to designers of material control systems, as well as those doing empirical research, who need to consider in depth various aspects of material control. Second, we intend to stimulate research efforts by the use of our framework. We demonstrate one such use of the framework by introducing a model of a particular integrated production‐distribution system. Our model represents a simple, three‐location, production‐distribution system with multiple products. We develop a system that is predominantly pull, and several systems with varying degrees of push. In the particular example we discuss, our pull system provides lower costs and higher service levels than the push systems.
In developing our framework, we analyze material flow decisions in terms of four related decisions that can be used to distinguish push systems from pull systems: 1) batch size, 2) timing of a production or shipment request, 3) setting dispatch or allocation rules, and 4) the presence of interference mechanisms for expediting or handling of emergency orders. The first two decisions are primarily concerned with individual products or parts. The second two decisions introduce the added complexity of multiple products, locations and customers. For each decision, we examine the authority for the decision and the information content used in making the decision.
We shall see that it is not possible or useful to label a manufacturing system as being entirely push or entirely pull. We argue that push and pull are characteristics of the underlying decision‐making process; and hence manufacturing control systems, which embody a collection of decisions, will contain elements of push or pull to varying degrees. Nevertheless, certain systems, containing such diverse elements often give the impression that they are predominantly one or the other. Our extended definitions provide a more complete picture that can be useful to researchers engaged in empirical work, as well as to managers concerned with system design.
The paper is organized as follows: following an introduction and a review of various definitions of push and pull, we describe integrated production‐distribution systems. In the next section we present our framework for defining push and pull systems. Throughout this section we refer to commonly used planning and control systems, such as MRP, kanban, DRP, fair shares, and OPT. We then introduce a model that can be used to compare different material control systems. Finally, we present a summary and conclusions.
Approaches to multistage production scheduling can be conveniently classified into push type (i.e., Materials Requirements Planning (MRP) systems) or pull type (i.e., kanban systems). Each is generally thought to have both advantages and disadvantages. In this paper, a hybrid push/pull strategy is developed with the aim of gaining the advantages of both approaches. Material flow between work centers is regulated using the standard single card kanban/pull arrangement. Superimposed on this is the MRP-type information flow which feeds forward demand information directly to one or more (but not necessarily all) work centers.A general N-stage hybrid push/pull model is developed. The use of the approach is illustrated using 3-stage and 4-stage serial flowlines. The results indicate that the push/pull approach has lower inventory levels and a better response to demand changes than the pure pull system. The hybrid approach seems to combine many of die advantages of MRP approaches while retaining much of the simplicity of kanban/pull systems.
Concerns about American manufacturing competitiveness compel new interest in alternative production control strategies. In this paper, we examine the behavior of push and pull production systems in an attempt to explain the apparent superior performance of pull systems. We consider three conjectures: that pull systems have less congestion; that pull systems are inherently easier to control; and that the benefits of a pull environment owe more to the fact that WIP is bounded than to the practice of "pulling" everywhere. We examine these conjectures for analytically tractable models. In doing so, we not only find supporting evidence for our surmises but also identify a control strategy that has push and pull characteristics and appears to outperform both pure push and pure pull systems. This hybrid system also appears to be more general in its applicability than traditional pull systems such as Kanban.
A unitary scheme which classifies certain subsystems, within production management, according to push and pull logics is proposed. The three subsystems described are: inventory management, manufacturing priority assignment and material picking and moving, and production planning. The classification proposed is a starting point for establishing application requirements. The characteristics of the production context, not the inherent logic, determine the choice of the most feasible techniques. Techniques with different logics can hence coexist in the same production system.
Common sense manufacturing (CSM) builds on the benefits of both JIT and MRP systems. The CSM system implemented at Lucent Technologies resulted in significant improvements: consistent on-time delivery; increased throughput; and improved costs of manufacture, without adding people or capital equipment.
MRP and Kanban are compared to show that these two systems, which have been developed within different cultures, are similar in orientation and in objectives. Both systems combine material control and priority control and depend for success upon good managerial planning, information flow, and common goals.
The strategies developed in part I of this paper are extended to a more general case in part II. Based on the optimal policy structure of a Markov Decision Process model and by control and information structure analysis, we propose that the ‘best’ strategy for a general series/parallel multistage (assembly) production/inventory system is to use a push (MRP) strategy at all initial stages of the system and a pull (JIT) strategy at all the other downstream stages. As viewed from the information structure, this recommended strategy is a group of decentralized controllers with a centralized coordinator.
A multistage production/inventory system is modelled. The system structure, which has the form of an assembly network, is abstracted from the production process of a typical integrated iron and steel works. The system is modelled as a Markov Decision Process (MDP). Combinations of Just-In-Time (pull) and MRP (push) policies are used as alternatives in the MDP. Optimal hybrid strategies are developed. In part II, we extend our observations to a more general case.
This paper presents a control methodology for flow shops that is decentralized and adaptive in nature, and has low data handling and computational requirements. The methodology is based on stochastic automata methods for modelling learning behaviour. It is proposed that such a methodology can be used with Kanban type control technique to make flow shop systems more flexible and adaptive in nature, Relationship of the control model to computational models such as neural computing is discussed.
Recently, it has been recognized that production control systems
for multi-stage manufacturing processes can be classified into push-type
and pull-type systems. The push-type systems are commonly defined as
those types of materials requirements planning system which utilize the
forecast of demands. The pull-type systems, on the other hand, are those
where order quantities are determined on the basis of real demand.
Describes a hybrid push/pull production control system, operated
periodically, which combines the benefits of both systems. Discusses
theoretical arguments in support of this system and numerical studies
are shown to give insight into the system's performance. Hybrid
push/pull-type systems can attain a higher degree of effectiveness if
they are appropriately operated.
This paper deals with MRP treatments of the coexistence of MRP and kanban systems. In practical applications, MRP and kanban can both exist in a factory. When a production system contains parts managed by an MRP system and parts managed by a kanban system, MRP needs to have a special way of dealing with those parts managed by the kanban system in order to provide accurate gross demand data for the MRP system.
A modified MRP for treating parts ordered through kanbans with the coexistence of MRP and kanbans is proposed. Along with two other existing methods (one by Karmarkar and another by a major tractor company), three methods are compared in a GPSS simulation study.
Empirical results regarding inventory and shortages from the simulation study showed that both the proposed modified MRP and Karmarkar's methods of dealing with parts ordered by kanbans performed quite well.
This research study suggests two effective lot‐sizing rules to be used in MRP part explosion for kanban‐controlled stages of a production system in which some stages are controlled by kanbans and other stages by MRP releases. The simulation study established empirical evidence for the effectiveness of these two lot sizing rules.
Material Requirements Planning (MRP) and Just-in-Time (JIT) system are directed toward planning and controlling the important characteristics of material flow: how much of what materials flow and when. Since the material flow is at the heart of the manufacturing firm, MRP and JHT are the powerful management tools that could determine the success or failure of an entire manufacturing system. One of the strongest debates in manufacturing has been centered on the performance comparison and compatibility of JIT production system to the existing MRP. The primary intent of this research is to provide an overview of the manufacturing planning and control environment associated with MRP and JIT. Classifying the existing MRP/JIT comparison and integration literature, two different perspectives on MRP/JIT are discussed, and future research area is proposed based on the taxonomy.
The recent interest in Japanese manufacturing techniques includes the acknowledgement of the demand ‘pull’ strategy as an alternative to more traditional production ‘push’ practices. In many cases, the pull approach includes a simple shop floor information system such as the Kanban system. The idea of simplicity has been emphasized in many manufacturing firms and is then often linked to a pull system. However, a pull strategy is not necessarily applicable to all manufacturing environments. This paper discusses push and pull systems and how these approaches can be combined in an integrated push-pull manufacturing strategy. A case study in a semi-repetitive, make-to-order environment illustrates some potential benefits from such an integrated approach. The major issue is the linking of the manufacturing strategy to the business strategy by changing the manufacturing planning and control focus. This has resulted in improved competitiveness primarily in terms of delivery dependability and production flexibility.
The generic structure of production control systems for multistage manufacturing processes is modelled and a hybrid push/pull production ordering rule operated periodically is introduced at each of the stages. This combines the benefits both of push- and pull-type control concepts. The transmission of demand information between the stages plays an important role in a multistage production control system. Using the structure of multistage processes, it has been clear that the proposed multistage production control system has a nested structure of a single-stage production control system. Assuming a specific demand and forecasting model, the variances of inventory level and production level at each of the multiple stages are investigated to provide insights into achieving “just-in-time” deliveries in multistage manufacturing environments.
Getting control of just‐in‐time
- Karmarker U
Optimal hybrid push/pull control strategies for a parallel multistage system: part 1
- Hodgson TJ