THE ROLE OF WOMEN IN ENVIRONMENTAL PROTECTION IN IGABI LOCAL GOVERNMENT, KADUNA STATE, NIGERIA
Hypothesis 2: Ineffective Utilization of Resources has no impact on sub-optimality in production in
2. Literature review
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Assess and Identify Factors Associated With Improved Information Systems For Performance Assessment Production Methodology Push Versus Pull Systems (Case Study
of Electronic Industries of Iran)
Shahram Gilaninia (Corresponding Author)
Assistant professor , Department of management, invited professor in Astara Branch, Islamic Azad University, Astara, Iran
Seyyed Javad Mousavian
Master of Islamic Azad university , Department of management, invited master in Astara Branch, Islamic Azad University, Astara, Iran
Musa Rezvani
Faculty Member ,Department of management, Astara Branch, Islamic Azad University, Astara, Iran
Abstract
This paper first researcher to introduce short of push and pull systems is then paid to the effect of various factors related to information systems are and can evaluate performance of many organizations to help study the research has In the present part of E Company was undertaken by using appropriate statistical methods and indicators that were identified.
Keywords: Performance, DSS, indicators, assessment.push production 1.Introduction
managers contemplating the installation of a pull system cannot measure progress toward meeting pull system goals without knowing the significant metrics of his system. This manager also requires a method for predicting when the system should change from one transition state to the next and a means to control the analysis of these changing metrics. The methods presented fulfill these needs. First, a simulation model of a real electronics assembly system is developed.
The simulation model provides a case study, a mechanism for obtaining data and a vehicle for method validation. Step-wise multiple regression is used to determine which factors and factor interactions significantly influence the simulation model output. The transition functions of pull system implementation are then estimated using dynamic programming. Validation of the transition functions is accomplished by comparing simulated results and results predicted by using the transition functions. A decision support system framework provides guidelines for the development of software employing these methods.
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2.1. Push Systems
A push system supports batch manufacturing operations. In batch manufacturing, thematerial- planning department of an organization typically releases a schedule that isdeveloped by taking into consideration the lead times of various components and subassemblies (Vollmann et al.,1997). These lead times consist mainly ofthe waiting time and the queue time for a particular component as the actual processingtime only constitutes a small part of the total lead time. The main objective of the pushsystem is to effectively utilize the capacity of each work center. This type ofmanufacturing system schedules shop orders by taking into consideration the routing of every component. The criterion used to allocate the schedule is the due date of the finished product. As MRP systems have evolved, it has become possible to track the progress of the components produced throughout the shop floor. In the mid 1970’s, Orlicky introduced the concept of ‘Material Requirements Planning’ and is recognized by many as the father of the modern MRP system (Vollmann et al.,1997). Various studies have been conducted to understand the performance of MRP or push systems. Suri, Krishnamurthy and Vernon (2004) identify the following key issues in modeling a push system:
• Estimating release lead‐times
• Modeling future requirements for different products
• Determining the safety lead‐times and stock
Buzacott and Shanthikumar (1994) conducted research that studied safety stock vs. safety lead- time in a MRP-controlled production system. Their research proposes that safety lead time is preferable than safety stock whenever it is possible to have a good forecast of the future requirements. But in cases where it is only possible to predict the mean demand then either of the two can be used. Even Suri et al (2004) conclude that in case of single product systems where future demand is accurately known it would be more appropriate to implement push strategy with safety lead times.
2.2. Pull Systems
Pull (kanban) strategies have been the subject of numerous studies by researchers (Uzsoy et al., 1990; Berkley, 1992; Liberopoulos &Dallery, 2000 ; the references therein) . A kanban system is a common example of a pull production system that is extensively used in industry. The Toyota Production System has made the word kanban so synonymous with the pull system they practice that pull systems are often referred to as kanban systems (Ohno,1988). In Japanese, kanban means display or a card. A kanban in a pull system is an authorization to produce. It is a signal from a downstream manufacturing process that prompts the upstream process to begin production of a component that has been consumed from inventory. The two things that have become integral to the pull type manufacturing system are:
• kanban cards
• Standardized containers
The containers are used to standardize the production batch sizes, protect the material from damage and also assist in maintaining visual control of the WIP inventory (Hyer&Wemmerlov,2002). Though kanban type pull systems are most commonly used, there exist other pull systems, like POLCA (Paired-cell overlapping loops of cards with authorization), CONWIP (Constant WIP), and DBR (Drum-buffer-rope) (Hyer and Wemmerlov,2002). POLCA
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last of the two cells in a loop. With CONWIP, an authorization to produce the next batch is produced when another batch is removed from inventory, regardless of the product type. DBR is a system in which the master schedule sets the production rate according to the output rate of the bottleneck resource. Researchers have extensively studied pull strategies; Tayur (1993) developed theoretical results to characterize the dynamics of kanban systems and reduce the simulation effort needed to study them. Gstettner and Kuhn (1996) show that the performance of a kanban system is significantly affected by the distribution of kanban cards.
Figure 1. Push versus Pull System
In the Push scheme each time a demand arrives it immediately authorizes the release of a new job. The demand is either immediately satisfied from the stock or it is backloged. The Pull scheme examined is similar to CONWIP (Spearman,1992 ; Spearman et al., 1990 ; Spearman&Zazanis,1992). The main difference is that instead of the requirement that the WIP in the system remain constant we require that the total work-in-process including the Finished Goods Inventory (FGI) remain constant and equal to S. External demands, if not immediately satisfied, are again backloged. In this scheme the arrival of a demand authorizes the release of a new job only when the work in process is less than S or equivalently the finished goods inventory is greater than zero. Proper operation of this scheme, which of course depends crucially upon the right choice for S, results in the same benefits in terms of increased system control as other pull systems (Buzacott&Shanthikumar,1993 ; Hall,1983 ; Karmarkar ,1986 ; Mitra&Mitrani,1990 ; Mitra&Mitrani,1991 ; Spearman et al.,1990 ; Spearman&Zazanis,1992).
2.3.Push/Pull Interface
It is not necessary that a production line be pure push or pure pull. One can design a line so that a segment of the line operates as push and the another segment operates as pull. We present a specific example to illustrate this.
2.4.Electronics assembly case in Iran industry
An actual push design electronics assembly system was modeling using the SIMAN simulation language. The simulation model was a representation of a real electronics assembly
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The unit load of the simulation model was a single circuit board. The production process begins with board washing and serialization. The next process is surface mount technology (SMT) with solder paste screening, parts placement and either phase or infrared (IR) solder reflow. The SMT process is completed with degreaser cleaning. The automatic insertion (A/I) process forms the second part of this electronics assembly system and includes: automatic part insertion, manual part insertion, wave soldering, aqueous cleaning and special parts assembly. Inspection and rework stations are located after: solder screening, SMT parts placement, SMT solder reflow, aqueous cleaning and special assembly. The last step in the simulation model was final inspection. Circuit boards passing final inspection left the system.
In the literature, there has been considerable interest in comparing push and pull strategies, and in studying the driving factors for choosing between push and pull. Table 1 summarizes the literature comparing push and pull in a production system context to capture the following issues: 1) manufacturers’ key performance metrics; 2) key factors that influence the performances; and 3) research methods. While push is still the most widely employed strategy by semiconductor manufacturers today (Fowler et al.,2002), leading manufacturers such as Xilinx (Brown et al.,2000) and solution providers such as Technology have initialized the transition from push to push-pull for the next-generation production of high-margin, high-volume products. The push-pull system that takes the advantage of both push and pull characteristics may outperform pure push and pure pull systems (Spearman& Zazanis,1992). In general, however, no identical conclusions can be drawn from Table 1 for choosing a better strategy since there are numerous factors that influence system performance.
Table 1 .LITERATURE ADDRESSING THE COMPARISON OF PUSH AND PULL STRATEGIES Article Industry Area Method Performance
Metrics
Influential Factors
Conclusion Bonney et al.,(1999) Generic Simulation Fill Rate;
Mean Waiting Time
The flow of control information (order size, batch size, kanban size, etc.)
It may be possible to obtain similar
performance improvement in push systems as in pull systems with particular control
information.
Damodaran and Melouk(2002)
Generic Analytical Model
Throughput;
Average Waiting Time; Machine Utilization
Processing time variation; Demand variation;
Transporters;
Batch size
Research shows that the number of
transporters used and the batch sizes have a
significant effect on the performance
measures of both push and pull systems.
Dengiz and Akbay (2002)
PCB production Simulation Throughput;
Cycle Time
Number of boards in
different operation stages.
Implementation of a pull system increase
productivity by 12%.
Grosfeld-Nir et al.,(2000)
Generic Simulation WIP;
Throughput
Processing time variability
Surprisingly, often push outperforms pull on WIP, while maintaining higher throughput.
Hirakawa (1996) Generic Analytical Model
JIT Delivery;
Inventory
Variance of inventory level and production level
Suggests shorter processing cycle time for
each of the multiple stages to achieve JIT
delivery without having excessive inventory.
Hoshino (1996) Generic Analytical Level of Safety Variance of the Pull performs better in reducing
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Model Stock forecasting error, variance of the demand and the number of periods constrained in lead time
safety stock when the variance of forecast error was large relative to the variance of demand.
Otherwise, push is more effective.
Hurley and Whybark (1999)
Engine assembly in a manufacturing
cell
Simulation Output Rate;
Cycle Time;
Utilization
Average WIP;
Protective capacity;
Variance reduction
Indicate the trade-off between inventory
buffers and capacity buffers and conclude that variance reduction and protective capacity are good alternatives to inventory.
Kelle and Peak (1996) Chemical Simulation Average Annual Cost; Service Level; Fill Rate;
Backorder Rate
Required service level;
Ratio of setup to holding cost; Variance of forecasted demand
Switching from a fixed schedule to an adaptive schedule decreases inventory holding costs and increases customer service while maintaining the same setup costs.
Kher et al.,(2000) Lot splitting in Flow shop
Simulation Lot Traceability;
Material Handling Costs
Number of batches;
Machine utilization;
Setup-to- processing time ratio
Pull lot splitting helps significantly in reducing the number of transfers incurred and in maintaining a greater degree of physical lot integrity.
Kim et al.,(2002) Production with emergency orders
Simulation Service Level;
Operating Costs;
Delivery Time for Late Orders
Proportion of emergency orders
With an option of safety stock, the pull system outperforms the push system in terms of throughput time, delay time of regular and emergency orders, and total cost when demand variation is high.
Krishnamurthy et al.,(2000)
Flexible Manufacturing
Simulation Throughput;
Inventory
Product mix changes, Demand and processing times
The pure push strategy has a higher
throughput for a given level of inventory than the pure pull strategy in some systems.
2.5.Simulation model
Digital simulation was utilized to model the actual production system. Although a brute force method and worker intensive, simulation supplies a feasible mechanism to analyze complex simultaneous activity. The simulation model employed standard probability distributions for input data. A run time period of 40 h, after steady-state, was observed. If the output change was statistically equivalent to zero, steady-state was said to have been reached Input data were verified by comparison to actual system process standards using derived probability distributions and the chi-squared goodness of fit test. Simulation model validation included internal mathematical consistency checks and comparing simulation model output to the physical system metrics.