160 research outputs found

    Reduction of production cycle time by optimising production and non-production components of time in the metalworking industry: a case study

    Get PDF
    The production cycle (PC) time, as a very important economic indicator of freezing current assets, involves the time needed to manufacture a unit or a series of units, from putting them into production until they are put into storage; and yet it is rarely discussed in the literature, even though it should be also analysed and be made as short as possible. The goal of this article is to survey and control the methodology of reducing the PC time of components in the metalworking industry, grouped by factor analysis into the factors of production and non-production components, observed by a modified method of current observations, and viewed as a process whose effectiveness was monitored using control charts. The survey is based on data collected through 1,576 observations in a Serbian company that manufactures electrical and electronic equipment for motor vehicles. The 2012 results, when compared with those of 2011, indicate that the PC time is significantly reduced by 93 minutes, or by 28.53 per cent, and the manufacturing time by 46 minutes, or by 19.17 per cent. The results furnish empirical findings that provide insights into a number of managerial issues concerning investment decisions in product-specific cycle time improvements and reductions, together with process redesigns

    Reduction of production cycle time by optimising production and non-production components of time in the metalworking industry: a case study

    Get PDF
    The production cycle (PC) time, as a very important economic indicator of freezing current assets, involves the time needed to manufacture a unit or a series of units, from putting them into production until they are put into storage; and yet it is rarely discussed in the literature, even though it should be also analysed and be made as short as possible. The goal of this article is to survey and control the methodology of reducing the PC time of components in the metalworking industry, grouped by factor analysis into the factors of production and non-production components, observed by a modified method of current observations, and viewed as a process whose effectiveness was monitored using control charts. The survey is based on data collected through 1,576 observations in a Serbian company that manufactures electrical and electronic equipment for motor vehicles. The 2012 results, when compared with those of 2011, indicate that the PC time is significantly reduced by 93 minutes, or by 28.53 per cent, and the manufacturing time by 46 minutes, or by 19.17 per cent. The results furnish empirical findings that provide insights into a number of managerial issues concerning investment decisions in product-specific cycle time improvements and reductions, together with process redesigns

    The integrated control of production-inventory systems

    Get PDF
    In this thesis, we investigate a multi-product, multi-machine production-inventory (PI) system that is characterized by: ?? relatively high and stable demand; ?? uncertainty in the precise timing of demand; ?? variability in the production process; ?? job shop routings; ?? considerable setup times and costs. This type of PI system can be found in the supply chain of capital goods. Typically, it represents a manufacturer of parts that are assembled in later stages of the supply chain. Our exploratory research aims at identifying promising control approaches for this type of PI systems and the conditions in which they are applicable. The control approaches developed in this thesis are based on an integrated view of the PI system. The objective of the control approaches is to minimize the sum of setup costs, work-in-process holding costs and ¿nal inventory holding costs, while target customer service levels are satis¿ed. The research reveals that the exact analysis and optimization of this type of PI systems is impossible. Therefore, we are restricted to the development of heuristic control approaches. We propose two control strategies that are based on distinct control principles. For each of the control strategies, we develop and test decision-support systems that can be used to determine cost-e¢ cient (but not necessarily optimal) control decisions. Part I of this thesis deals with the ¿rst approach, called Coordinated Batch Control (CBC). This strategy uses a periodic review, order-up-to inventory pol- icy to control the stock points. The replenishment orders generated by this inventory policy are manufactured by the production system. The CBC strat- egy integrates production and inventory control decisions by determining cost- e¢ cient review periods. There is no further integration of control decisions. At the shop ¿oor, a myopic rule is used to sequence the orders, which ensures a certain degree of ¿exibility for responding to unexpected circumstances. We develop three decision-support systems for the CBC approach. The ¿rst decision-support system is based on an approximate analytical model of the PI system. In the approximate analytical model, we apply standard results from inventory theory, queueing theory and renewal theory. The second and third decision-support system use simulation optimization techniques to determine the near-optimal review periods. The three heuristic decision-support systems for CBC are tested in an exten- sive simulation study. The test bed consists of ¿ve basic problem con¿gurations, which de¿ne a routing structure, processing times, etc. We vary four factors over several levels: setup costs, setup times, net utilization and target ¿ll rates. In this way, we obtain 48 instances based on the same basic problem con¿guration, leading to 5 x 48 = 240 problem instances. The simulation study shows that the use of simulation optimization resulted in relatively small improvements over the solution obtained from the approximate analytical model. Since simulation optimization requires large amounts of computation e¤ort, we decide that the use of the decision-support system based on the approximate analytical model is justi¿ed. Part II is concerned with the Cyclical Production Planning (CPP) strategy. This strategy approaches the control of the PI system from a totally di¤erent angle. In this strategy, a detailed production schedule is used to control the production system. The schedule prescribes the sequence in which the orders are produced on the work centers and this schedule is repeated at regular time intervals. The time that elapses between the start of two schedules is called the ¿cycle time¿. The schedule is determined such that the total costs are minimized. The stock points are controlled with periodic review, order-up-to policies. The main advantage of the use of a production schedule is that ¿ow of the orders through the production system is controlled better, which results in more re- liable throughput times. A drawback of this approach is that the production frequencies of the di¤erent products need to be matched in order to make a cyclic production schedule. Hence, there is less ¿exibility in setting the lot sizes, which may result in higher costs. Another drawback of the CPP approach is that production capacity may be wasted by strictly following the prespeci¿ed processing sequences. We propose a decision-support system for the CPP strategy which is based on a deterministic model of the PI system. The decision-support system is used to determine cost-e¢ cient production plans. We present a heuristic method to approximately minimize the total costs of the deterministic model. When the solution of the deterministic model is used in a stochastic environment, the solution may be instable or nearly instable. Therefore, we use a simulation procedure to check whether the proposed solution is stable. If not, slack-time is added to the schedule and deterministic model is solved again. We test the decision-support system for CPP in an extensive simulation study. The test bed is identical to the one used in Part I. We test wether the Summary 273 decision-support system responds soundly to changes in the factors. Further- more, we investigate the estimation quality of the deterministic model that is embedded in the decision-support system. Finally, we test the optimization quality of the decision-support system. Based on the results of these tests, we decide that it is acceptable to use the proposed decision-support system to determine the control variables of the CPP strategy. Part III compares the performance of the CBC and the CPP strategy. Both strategies are compared in a simulation study consisting of the same instances as in Part I and II. We compare the strategies in terms of realized total costs. In about 62% of the instances, the CPP strategy outperforms the CBC strategy. In the remaining 38% of the instances, the CBC strategy realizes lower costs than the CPP strategy. An analysis of variance reveals that the following factors have a signi¿cant impact on the performance di¤erence between CPP and CBC: ?? net utilization; ?? setup costs; ?? interaction between setup costs and net utilization; ?? basic problem con¿guration. Based on our investigations, we can provide an explanation for these obser- vations. The simulation results show that the performance di¤erence is pro- portional to the di¤erence between the average review periods (CBC) and the common cycle length (CPP), denoted as dR. The factors mentioned above have an in¿uence on dR through their impact on capacity utilization. At low lev- els of capacity utilization, we observe that dR is low, which indicates that the CPP and CBC strategy operate with comparable review periods and common cycle lengths. In situations where the CBC strategy operates at higher levels of capacity utilization (because net utilization increases and/or setup costs de- crease), it becomes more di¢ cult for the CPP strategy to ¿nd a feasible cyclical production schedule, mainly because production capacity is wasted by strictly following a prespeci¿ed processing sequence. In these cases, the CPP strategy needs to increase the common cycle length to free up production capacity that is used to compensate for the loss of capacity. This leads to increases in dR and to higher costs. The speci¿c characteristics of a problem instance have a strong in¿uence on the magnitude of this e¤ect. Based on the insights obtained from our research, we formulate some guidelines for the application of CPP and CBC

    Operator Idle Time Rectification as the Solution to Cycle Time Reduction in Oxygen Sensor Production

    Get PDF
    In factory works, there are systems that need to be followed in order to create a well-ordered process such that Just In Time (JIT) production can be reached. The lean manufacturing principle is one of the solutions for sustainable factory system. Within the lean manufacturing system, there are many tools that can be applied for sustainability and improvement. In this paper, Kaizen, the Japanese business philosophy as one of the lean manufacturing principle tools, is applied to give an improvement in reducing production cost. It is done by continuous observation, note, and analysis of time consumption required by the production line. A new Kaizen analysis from a new point of view is applied by comparing the impact of adding operator to the production line to the Over Time (OT) cost and renewing part of the machine-tool mapping for faster Takt Time (TT). Kaizen activity was successfully analyzed. It shows that the production Cycle Time (CT) can be deducted by 0.5 seconds which will reduce the production revenue cost by 10.8%

    Effect of Unequal Lot Sizes, Variable Setup Cost, and Carbon Emission Cost in a Supply Chain Model

    Get PDF
    Due to heavy transportation for single-setup multidelivery (SSMD) policy in supply chain management, this model assumes carbon emission cost to obtain a realistic behavior for world environment. The transportation for buyer and vendor is considered along with setup cost reduction by using an investment function. It is assumed that the shipment lot size of each delivery is unequal and variable. The buyer inspects all received products and returns defective items to vendor for reworking process. Because of this policy, end customers will only obtain nondefective items. The analytical optimization is considered to obtain the optimum solution of the model. The main goal of this paper is to reduce the total cost by considering carbon emission during the transportation. A numerical example, graphical representation, and sensitivity analysis are given to illustrate the model

    Autonomous Finite Capacity Scheduling using Biological Control Principles

    Get PDF
    The vast majority of the research efforts in finite capacity scheduling over the past several years has focused on the generation of precise and almost exact measures for the working schedule presupposing complete information and a deterministic environment. During execution, however, production may be the subject of considerable variability, which may lead to frequent schedule interruptions. Production scheduling mechanisms are developed based on centralised control architecture in which all of the knowledge base and databases are modelled at the same location. This control architecture has difficulty in handling complex manufacturing systems that require knowledge and data at different locations. Adopting biological control principles refers to the process where a schedule is developed prior to the start of the processing after considering all the parameters involved at a resource involved and updated accordingly as the process executes. This research reviews the best practices in gene transcription and translation control methods and adopts these principles in the development of an autonomous finite capacity scheduling control logic aimed at reducing excessive use of manual input in planning tasks. With autonomous decision-making functionality, finite capacity scheduling will as much as practicably possible be able to respond autonomously to schedule disruptions by deployment of proactive scheduling procedures that may be used to revise or re-optimize the schedule when unexpected events occur. The novelty of this work is the ability of production resources to autonomously take decisions and the same way decisions are taken by autonomous entities in the process of gene transcription and translation. The idea has been implemented by the integration of simulation and modelling techniques with Taguchi analysis to investigate the contributions of finite capacity scheduling factors, and determination of the ‘what if’ scenarios encountered due to the existence of variability in production processes. The control logic adopts the induction rules as used in gene expression control mechanisms, studied in biological systems. Scheduling factors are identified to that effect and are investigated to find their effects on selected performance measurements for each resource in used. How they are used to deal with variability in the process is one major objective for this research as it is because of the variability that autonomous decision making becomes of interest. Although different scheduling techniques have been applied and are successful in production planning and control, the results obtained from the inclusion of the autonomous finite capacity scheduling control logic has proved that significant improvement can still be achieved

    Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

    Get PDF
    In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

    The impact of product complexity on ramp-up performance

    Get PDF
    Fast product ramp-ups are crucial in consumer electronics because short product lifecycles prevail and profit margins diminish rapidly over time. Yet many companies fail to meet their volume, cost and quality targets and the ramp-up phase remains largely unexplored in new product and supply chain management research. This study identifies the key product characteristics that affect ramp-up performance using operational data from the cell phone industry. We investigate three research questions: (1) How to measure software and hardware complexity characteristics of consumer electronics products – and specifically cell phones? (2) To what extent drive product complexity characteristics manufacturing performance? and (3), in turn, to what extent drive manufacturing performance and complexity characteristics ramp up performance? The findings contribute to operations management literature in three ways: First, our model reflects the growing importance of software characteristics in driving hardware complexity, an aspect that prior empirical ramp-up studies have not yet addressed. Second, specific hardware and software complexity characteristics (i.e., component count, parts coupling and SW code size) primarily drive the performance of the manufacturing system in terms of final yield and effective capacity. And finally, effective capacity together with the novelty aspects of both software and hardware complexity (i.e., SW novelty and product novelty) are the key determinants of ramp-up performance
    corecore