USING TRANSFER BATCHES IN FLOW LINES WITH OPERATOR-BASED MATERIAL HANDLING

In this paper we extend a model proposed by Karmarkar for studying the relationship between batch sizes and production lead times. In our extended model we differentiate between production and transfer batches and examine the effect of having smaller transfer batches on lead times. The model is used to obtain optimal transfer batch sizes and determine their relationship to the size of production batches. Further extensions are made to include multiple machines and material-handling considerations and to evaluate their effect on batching decisions. Conditions under which either type of batching can be useful are identified and the corresponding optimal batch sizes are described.

This paper presents a framework of intelligent manufacturing scheduling and control with specific applications to operations of rail-guided vehicle systems (RGVS). A RGVS control architecture is discussed with a focus on a simulated experiment in operations of the load/unload area of a real industrial flexible manufacturing system (FMS). In the operation stage of a material handling system (MHS), all shop floor data are subject to change as time goes. These data can be collected using a data acquisition device and stored in a dynamic database. The RGVS simulator used in this experimental study is designed to incorporate some possible situations representing existing material handling scenarios in order to evaluate alternative control policies. At the development stage of the controller, all possible combinations of most commonly encountered scenarios such as RGV failures, production schedule changes, machine breakdowns, and rush orders are to be simulated and corresponding results collected. The data are then structured into training data pairs to properly train an artificial neural network. The neural network, trained by using input/output data sets obtained from a number of simulation runs, will then provide control strategy recommendations. At the application stage, whenever an abnormal scenario occurs, a pre-processor will be activated to pre-screen and prepare an input vector for the trained neural network. If such an abnormal scenario falls outside the existing domain of data sets employed to train the neural network, as judged by the MHS supervisory controller, an off-line training module will be activated to eventually update the neural network. The recommended control strategies will be transmitted to the MHS control for real-time execution. If there is no further abnormal event detected, the dynamic data base (DDB) module simply continues to monitor the MHS activities. The proposed MHS control system combines the features of example based neural network technology and simulation modeling for true intelligent, on-line, pseudo real-time control. Not only will the system assure that feasible material handling control actions be taken, but also it will implement better control decisions through continuous learning from experiences captured as the operation time of the MHS accumulates.

During the process of distribution of high-value and time-varying products (HVTVP) by e-commerce retailers, there exists a mismatch between product delivery lead time and customer required time. Within this context, the composition and reduction mode of delivery lead time were studied, and two types of multi-stage lead time reduction models were established. The first type is based on a controllable lead time reduction model and primarily considering the interaction between the different components of lead time reduction, a multi-stage lead time reduction model was bulit with the goal of minimizing total cost. The second type of model considers the impact of product life cycle on cost and lead time reduction. Based on a comparison of the reduction of lead time in different stages and the quantity of products handled, a multi-stage lead time reduction model is created with the goal of maximizing profit. The application of these models demonstrates that the first type can be used to select components of lead time reduction, and the second type can be used to select components of lead time reduction. Finally, the numerical analysis shows the practicality and consistency of the models.

Mass management and production of customized products requires material handling systems (MHS) which are flexible and responsive enough to accommodate dynamic and real-time changes in material handling tasks. Towards this goal, we develop a novel control framework to improve the flexibility and responsiveness of material handling systems. Flexibility is achieved by using multi-commodity flow network optimization to find the most optimized job sequence in terms of minimum transfer steps. Responsiveness is achieved by the use of a matrix-based discrete event (DE) supervisory controller to dispatch equipment control commands in real-time based on real-time sensor information, according to the optimized sequence. By modeling the MHS network as multi-commodity flow network to define job routes, and using the matrix-based DE controller to implement the job routes in real-time, the users achieve a seamlessly integrated solution to control the execution of transfer jobs that covers the supervisory planning stage through the real-time actual dispatching decisions. The proposed control framework is evaluated on an industrial case study of airfreight terminal material handling and simulation results show its effectiveness.

ABSTRACTThe pressure to reduce inventory investments in supply chains has increased as competition expands and product variety grows. Managers are looking for areas they can improve to reduce inventories without hurting the level of service provided. Two areas that managers focus on are the reduction of the replenishment lead time from suppliers and the variability of this lead time. The normal approximation of lead time demand distribution indicates that both actions reduce inventories for cycle service levels above 50%. The normal approximation also indicates that reducing lead time variability tends to have a greater impact than reducing lead times, especially when lead time variability is large. We build on the work of Eppen and Martin (1988) to show that the conclusions from the normal approximation are flawed, especially in the range of service levels where most companies operate. We show the existence of a service‐level threshold greater than 50% below which reorder points increase with a decrease in lead time variability. Thus, for a firm operating just below this threshold, reducing lead times decreases reorder points, whereas reducing lead time variability increases reorder points. For firms operating at these service levels, decreasing lead time is the right lever if they want to cut inventories, not reducing lead time variability.

In apparel supply chains, manufacturers usually request a short lead time for fabric supplies. However, a short supply lead time would create environmental problems such as insufficient time for proper control of chemicals and material processing operations, and lead to a lower production yield of good quality supplies. Motivated by this observed industrial practice in fabric sourcing and apparel production, we build a stylized analytical model to investigate how lead time reduction in fabric sourcing affects performances of the fabric supplier and apparel manufacturer as well as the environment. To be specific, we first derive the optimal ordering quantity for the apparel manufacturer and find that it is a production yield scaled newsvendor fractile quantity. We then explore the expected values of lead time reduction, and derive the respective analytical conditions for the apparel manufacturer, fabric supplier and whole supply chain to be benefited by lead time reduction. From the conditions, we reveal that the prior demand mean (which also implies the relative prior demand uncertainty) plays a critical role in determining whether lead time reduction is beneficial. We illustrate how a win–win situation in the supply chain can be achieved by a properly designed deposit payment scheme. For the environment, we show that when the fabric supplier’s profit is improved under lead time reduction, the environment must be hurt. We further investigate how an environment tax can be imposed on the fabric supplier so as to entice it to invest in green technologies.

Reduction in lead time is very essential in every industry to increase the productivity, satisfy the volatile demands of a customer and sustain the market reputation among the global industries. The competition among the global industries has forced almost all industries to adopt the lean manufacturing system over the traditional manufacturing system. The present study focuses on the identification of barriers which resist the reduction of lead time in electronics industries in Himachal Pradesh. Nine critical barriers were found on the basis of data collected from the industry experts through the questionnaire survey and consultation from the different regions of Himachal Pradesh. Reduction in lead time is very important for any industry. There are always some barriers which resist the lead time reduction. Industries should know which barrier is most influencing barrier, selection of most influencing barrier can help the industries to overcome from barrier. This study investigates and ranks the barriers in lead time reduction based on the judgement of industry experts from electronics industry. Analytical hierarchy process (AHP) is used to prioritise the barriers.

Material handling is an important aspect in the industry. Material handling supports all the supply for production which is why it plays a crucial role in the production line. Manual material handling uses workers to keep the supply of parts from storage to the assembly line. This is based on a Japanese automotive manufacturer company material handling problem. The material handling system is complex and not efficient. There are too many unnecessary movements for instance it took too many steps to supply the parts. This movement is seen as not efficient and categorized as waste in Lean Manufacturing. The data is gathered by observation the whole process in the injection section. DMAIC method is used as the main method to analyze the problem and to develop the solution. The Six Phases of The Generic Product Development Process is used to further develop the design of the supporting tool. The solution will reduce waste of movement in material handling and thus improve the productivity in the section by 19.6%.

As semiconductor device ground rules become more stringent, lithographers are driven to reduce the magnitude of system error budget components to near atomic scale levels. Near term requirements (1995) for semiconductor photomask image placement have already been defined at 25 nm and are presumed to become tighter. Error contributions from both the stage metrology platform and material handling subsystems must be improved to 10 nm, thus allowing the balance of the error budget to be apportioned to the electron-optics and the electronic subsystems. As a result, a new, automated state-of-the-art Material Handling System (MHS) was designed for the Lepton EBES4 E-Beam Reticle Generator. This MHS is capable of handling multiple products such as reticles up to 9 in. (229 mm), x-ray masks, and wafers. Accuracy, required to support future E-beam pattern-writing applications for the production of 256 Mb and 1 Gb DRAM substrates, was the principle objective in its design. Therefore key attributes of the MHS are: (1) Precise substrate positioning with a resolution of (lambda) /120 achieved by means of two high-stability dual linear/angular interferometers combined in a single, compact optical assembly. (2) Stringent thermal management within the vacuum environment via a unique low-pressure (50 torr) gas conduction isotherm mode of action, which represents a new approach for rapid substrate temperature equilibrium to less than +/- 0.02 degree(s)C control. This paper focuses on how the MHS design addresses the minimization of mechanical error contributions to lithographic accuracy. Tests results of numerous critical parameters are presented (aligner/robot-corrected substrate positioning accuracy, temperature equilibration and stability, stage smoothness of motion, and electromagnetic interference). In addition, 3D finite element models are included to demonstrate induced static and random distortions, and dynamic modes of the metrology platform.

Lead time reduction and process enhancement for a low volume product

Enterprises within a supply chain can cut their expected inventory costs, raise service level, reduce losses caused by shortages, gain quick-response capability, and improve competitiveness by lead time reduction. However, the extra crashing cost spent within a supply chain partnership will inevitably increase with the shortening of the lead time. On the other hand, the longer the lead time, the larger the amount of safety stock that must be held. The problem of determining a suitable policy of inventory and purchasing management is investigated in this study, with the objective of minimizing total expected inventory costs with multiple partners. This paper presents a single-vendor multiple-buyer integrated inventory model with controllable lead time in which the distribution of lead time demand is normal.

In this study, we investigate a continuous review inventory model to reduce lead time, yield variability and setup cost simultaneously through capital investments. We assume that the backorder rate is depending on the length of the lead time through the amount of shortages. We also assume that lead time demand's distribution is not known but its first and second moments are known. We apply minimax distribution free procedure to minimise the expected total annual cost. By using logarithmic investment function we describe the relationship between the reduction in lead time, yield variability and setup cost with capital investment. This function was used in many existing models. Our main aim is to determine the optimal capital investment and ordering policies that minimises the expected total annual cost. To find out the optimal solution, an algorithm is given. With the help of this algorithm, optimal capital investment and ordering policies are wrought out. Numerical examples are given to elucidate the model. Our proposed model greatly differs from the model existing in the literature (the model by Lin and Hou (2005)) viz: (1) In the above model, yield variability and setup cost were reduced through capital investment. In our model we reduce yield variability setup cost and also the lead time, which plays a vital role in any business. By reducing lead time we can improve the service level to the customer so as to increase the competitive edge in business. (2) In the model (the model by Lin and Hou (2005)), it was assumed that lead time demand follows normal distribution. But in our model we take the distribution of lead time demand as distribution free. That is, it can follow any distribution which is more general. (3) In the above model (the model by Lin and Hou (2005)), shortages are completely backlogged. But we consider partial backlogging and take the backlogging rate as 0 ≤ B ≤ 1. If we set backlogging rate B = 1 we get the above model. That is, the above model is particular case of our model. (4) We also assume that the backorder rate depends on the length of the lead time through the amount of shortages. If the lead time is longer then shortage accumulation is higher. The patience of customers will result in failure in business since some customers may turn to some other supplier. Hence, the backorder rate will be reduced. This assumption is very realistic.

Inventory management and control is concerned with the acquisition and storage of materials required for supporting various business operations. Lead time reduction is another important production activity in an integrated inventory control. Lead time plays a vital role and has been a topic of interest for many authors in inventory organization. Lead time is a necessary aspect in any supply chain management and inventory management system. The time gap between placing of an order and its actual arrival in the inventory is known as lead time. In most of the literature dealing with inventory problems, either in deterministic or probabilistic model, lead time is viewed as a prescribed constant or a stochastic variable, which therefore, is not subject to control. But, in numerous sensible circumstances, lead time can he reduced by an additional crashing cost; in other words, it is controllable, so in this article, we have considered the lead time crashing cost is an exponential function of lead time. A solution procedure is developed to find the optimal solution. In this article, both lead time and the order quantity are considered as the decision variables. The behaviour of the model is presented graphically. The result is illustrated with the help of a numerical example. Finally, Graphical representation is presented to illustrate the model. The solution procedure with the help of the software Matlab 2008 is furnished to determine the optimal solution.

The purpose of this paper is to assist designers and logistics/manufacturing managers in designing efficient material handling (MH) systems from compatible commercial MH models that meet user requirements and have desired specifications using data envelopment analysis (DEA). The latter approach helps consider both technical and economic aspects of MH and ranks candidate commercial models and systems. The results show that attractive systems and their commercial models to be acquired should have low cost, require small space, and have high capacity. The application presented herein enhances formation of supply chains, where a MH system is a key component, and relieves users of the need to assume and estimate data relating to operations in a facility as it uses only data that are available during the design of a new system, and eliminates through modelling the need to quantify qualitative factors.

Involvement of controllable lead time and variable demand for a smart manufacturing system under a supply chain management