End Items Research Articles

Design for Material Logistics

A key business strategy for Definity® system private-branch exchanges (PBXs) is offering the customer the ability to customize a system to match his or her needs. This is possible because the Definity PBX architecture allows design of an almost limitless number of customer end item configurations from a limited set of buildable options (e.g., circuit packs). Though this customization is an attractive selling point, it complicates PBX manufacturing. It can also be costly to the manufacturing organization if done without prior installation of capabilities to deal with a high level of product optionality. To do this in AT&T required the support of the company's PBX design community. In particular, the traditional single-design documentation was complex and cumbersome, and had to be broken up into several simpler documents that reflect the producT's buildable options and physical hierarchies (e.g., cabinets, carriers, circuit packs). This paper will review the change and explain its implications and effect on high-option manufacturing.

Single and multistage production lot sizing with work-in-process inventory considerations

This paper modifies the single end item production lot sizing model with the explicit incorporation of the effects of conversion of input to output at finite rates and the resulting work-in-process inventories, for both single stage and multistage production/inventory systems, under deterministic conditions. We show that the single stage model is a special case of the more general multistage model and illustrate our results through simple numerical examples.

Production planning for mixed assembly/arborescent systems

AbstractIn the automotive industry, components are fabricated from raw materials; modules from components; and subassemblies from modules. Final assembly of the end item, the vehicle, then follows. Such a process would be called an “assembly system” if each lower‐level entity (raw material, component. module) were used in only one immediate successor entity (component, module, subassembly).It is common to represent the bill of materials as a directed network. The nodes of this network are entities in the bill of materials; arcs in the network point from lower level entities in the direction of higher level entities and toward the end product. For example, if components 1 and 2 were combined to produce module a, there would be an arc from node 1 to node a. Similarly, there would be an arrow pointing from node 2 to node a.A pure assembly system may thus be described by saying that each node has at most one direct successor, although it may have several immediate predecessors. Similarly, a pure “arborescent network” is the prototype for a distribution system, whereby each node (say a warehouse) may have at most one immediate predecessor (distribution center), but possibly many immediate successors (retailers or smaller warehouses).The focus of this paper is that a complex manufacturing system often has arborescent or non‐assembly portions in its network. In automotive assembly, a given component frequently appears in more than one subassembly in the manufactured product. Certain components are used in each of four wheels; left‐ and right‐side body subassemblies contain common modules. The result is a mixed assembly/arborescent structure. Mixed assembly/arborescent structures also merit consideration as the prototype for combined production/distribution systems.In this article, we consider such mixed structures by building on properties of pure assembly and pure arborescent systems. First, for a wide class of parameters and various pure assembly structures, we study several single‐level lot‐sizing algorithms in conjunction with the cost‐parameter‐revision method of Blackburn and Millen (1982b). Cost‐parameter revision is a way to account for the impact at other stages, of decisions made at a given stage, in a pure assembly system. We also show it is better not to revise these cost parameters for a pure arborescent system.Our approach to a mixed assembly/arborescent system is thus based on identifying its “largest, independent pure assembly sub‐graph.” This is a significant subsystem of the original graph or network, the largest portion that is pure assembly in nature. Modification of the cost parameters there, followed by stage‐by‐stage application throughout the whole network of the best single‐stage lot‐sizing method, gives lower total costs than when no cost revision is applied in the mixed system. Suggestions are then made for further research.

Common component inventory problems with a budget constraint: Heuristics and upper bounds

This paper addresses a so-called assemble-to-order environment. There are a number of end items each of which requires an assembly of several components. Some of the components are unique to specific end items while others are common to two or more final products. Components must, due to the leadtimes, be ordered prior to knowing the end item demand levels (assumed poisson distributed), but final assemblies can be completed after knowing the demands. The objective is to maximize the expected profit contribution subject to a budget constraint on the component inventory investment. The resulting two-stage stochastic programming problems obtained are very difficult, if not impossible, to solve. Heuristics and bounding procedures that give good results have therefore been suggested. The proposed heuristics can easily be extended to problems with continuous demand distributions.

Group technology in the US manufacturing industry: A survey of current practices

This paper reports the findings of a survey of 53 US users of group technology (GT). Respondent installations were medium to large electronics and metalworking manufacturers. They engaged predominantly in fabrication activities, producing a large number of component parts and/or end items. These firms applied GT to design, process planning (including NC programming), sales, purchasing, cost estimation, tooling, scheduling, new equipment sizing, and tool selection. In the majority of cases, firms used classification and coding systems as tools in applying GT. While users identified managerial and technical barriers which must be overcome in successfully applying GT, significant and varied operational and strategic benefits had been achieved. Further, most felt that GT would be an integral and important part of future CAD/CAM activities at their plant. The respondents' experiences confirm that GT's usefulness is quite broad and suggest that failure to understand GT as a general philosophy and, instead, to c...

Optimal and heuristic solutions for a simple common component inventory problem

This paper addresses a so-called assemble-to-order environment. There are a number of end items each of which requires an assembly of several components. Some of the components are unique to specific end items while others are common to two or more final products. Components must be ordered prior to knowing the end item demand levels (assumed normally distributed), but final assemblies can be completed after knowing the demands. The problem addressed is the allocation of a given budget among the components so as to maximize the expected total of end items sold. For a relatively simple commonality structure the optimal allocation is developed. A much simpler heuristic allocation procedure is shown to give excellent performance over a wide range of parameter values.

Aggregation and disaggregation of end items in a class of multistage production systems

Aggregation of end items in a class of multistage production systems within a hierarchical scheduling framework is considered. This class is one of flexible machining and assembly systems where each end item is assembled from a number of components. Each component undergoes a sequence of operations on various machines. The assembly structure of all items is flat. At least for some operations on some machines, set-ups are not negligible.

Optimization methods for large-scale multiechelon stockage problems

I examine the problem of determining inventory stockage levels and locations of different parts in a multiechelon system. This stockage problem is complicated by parts commonality—each part may be used by several different end items. Stockage levels and locations of each part affect the availability of end items that use the part, since an end item will be out of service if it requires a part that is not available. Of course, if the part is available at another nearby location, then the end item will be out of service for a shorter period of time. An essential feature of any model for this problem is constraints on operational availability of the end items. Because these constraints would involve nonconvex functions if the stockage levels were allowed to vary continuously, I formulate a 0–1 linear optimization model of the stockage problem. In this model, each part can be stocked at any of a number of prespecified levels at each echelon. The model is to minimize stockage cost of the selected items subject to the end-item availability constraints and limits on the total weight, volume, and number of different parts stocked at each echelon. Advantages and disadvantages of different Lagrangian relaxations and the simplex method with generalized upper-bounding capability are discussed for solving this stockage model.

PROCEDURES FOR DETERMINING RELATIVE FREQUENCIES OF PRODUCTION/ORDER IN MULTISTAGE ASSEMBLY SYSTEMS*

ABSTRACTWe show that the problems of determination lot sizes in a multistage assembly system for the case of instantaneous production and constant demand for the end item can be reduced to the problems of determining relative frequenceis of production/order for items at each production stage. We further show that such frequencies are independent of the demand levels. Optimal and near‐optimal solution procedures for this reduced problem are provided. The near‐optimal procedure successively treats each stage of production as a final production stage while simulatenously incorporating decisions made at lower stages into decisions made at higher stages. Experimental results show that the near‐optimal procedure results in optimal solutions 75 percent of the time and performs considerably better than representative heuristics available in the literature. Further, its performance is relatively less susceptible to product/structural characteristics of the system.

How to be a Successful Project Manager

The project is a one‐time complex event involving many functional organisation elements that must deliver an end item within very specific cost and schedule constraints. The project manager's responsibilities are to plan, control the organisation of manpower, control the basic technical definition of the project output, lead any people organisations assigned to the project, monitor performance, costs and efficiency, and complete the project on schedule and within costs. To do this, the manager should be committed to the plan, be inquisitive and ask the right questions, not manage by exception, insist that work be done right first time, involve manufacturing, know when to freeze and when to go ahead with the plan, and above all, be able to communicate.

A simulation comparison of group technology with traditional job shop manufacturing

A simulation model of an actual job shop was used to compare group technology with traditional job shop manufacturing. The experiment compared shops which had four different layouts, designed to emphasize different features of traditional job shops and group technology shops, and four distributions of demand for end items. The group technology shops exhibited superior performance in terms of average move time and average set-up time. The traditional job shops had superior performance in queue related variables (average queue length, average waiting time, work-in-process inventory, etc.). This was caused by group technology's dedication of machines. The effects of the queue related variables outweighed the effects of average move time and average set-up time: the average flow time was shorter in the traditional job shop than in the group technology shops.

Component part standardization: An analysis of commonality sources and indices

AbstractThe importance of higher component part standardization has been recognized as an important area of empirical investigation since it has been hypothesized to reduce inventory levels by reducing safety requirements, to reduce planned load through larger lot sizes, and to reduce planning complexity through reducing number of items to be planned. Therefore, component part standardization offers considerable promise for managers wishing to improve their production capabilities. In order to achieve higher standardization, measures indicating the degree of standardization are necessary. The most traditional measure of component part standardization is the degree of commonality index (DCI), which indicates the average number of uses per component parts.Unfortunately, this measure has many theoretical limitations. First, it is a cardinal measure and, therefore, cannot measure the degree of uncommon part numbers that frequently cause production planning problems. Additionally, as a cardinal measure, it cannot be used to compare planning across organizations and is not useful for making summary comparisons of planning complexities across organizations. This study develops a relative index that has boundaries of standardization between 0 and 1 corresponding to the lay language usage‐each item being unique (no standardization) and one item used everywhere (complete standardization).A second major weakness of the DCI is that it does not recognize sources of standardization for decision making. There are at least three principal decisions on which component part standardization indices can provide information: 1) within‐product decisions, 2) between‐product decisions, and 3) make‐buy decisions. The within‐product decision refers to using each component as frequently as possible within each end item. This increased usage means fewer unique items within each end item and is expected to reduce that item's planning complexity. The between‐product index is used to examine the design of new end items. Its purpose is to give indications of additional planning complexity by the increased number of new component parts. Hence, between‐product indices give information for reducing planning complexity with the introduction of new products. This article develops these two types of indices to indicate the relative proliferation of new component parts.The make‐buy decision involves indices that adequately describe manufactured versus made components. For the manufactured components, the level index computes the commonality by each product level of the bill of material. This index gives information for the design of a new, more automated manufacturing system by analyzing each level's standardization for possible inclusion into a more automated system (group technology, cell manufacturing, flexible manufacturing system, or automated factory). For the buy decision, the indices developed here can be utilized for reduction of the number of vendors due to “uncommon” components. Both level and buy indices can be used for analyzing and reducing the planning complexity of the production system.A third fundamental problem with DCI is its lack of realistic dimensions of end‐item volume, quantity per assembly, and cost. The DCI weights each end item precisely equally regardless of its volume. Therefore, an item produced once every two years would be weighted exactly the same as the best selling item. Similarly, the DCI does not consider the quantity per assembly (Q/A) of each component. Consequently, an item that had very small (Q/A) inside of an item has exactly the same weight as a high (Q/A) inside an item. Last, the DCI ignored the price of the component, which further limits its usefulness by causing the DCI not to have cost dimensions. This study developed relative within‐product, between‐product, and total commonality indices that include end‐item volumes, quantities per assembly, and component price. These indices can provide valuable insights for reducing relative planning complexities that improve system performance through analyzing relative costs of component parts usages.

Manufacturing lead time accuracy

AbstractThis article addresses the question of accuracy of planned lead times (PLTs) that are used with a material requirements planning system. Lead time error is defined as the difference between an item's PLT and the actual lead time (flow time) of an order to replenish the item. Three related topics are discussed: the relationship between system performance and average lead time error, the transient effect on work‐in‐process (WIP) inventory of increasing PLTs, and the relative accuracy of three methods of determining PLTs. A distinction is made between available and WIP inventory. The former includes any purchased item, fabricated part, assembly, or finished good that is in storage and available for use or delivery. WIP denotes materials associated with open orders on the shop floor.It was concluded that average lead time error has a considerable affect on system performance. PLTs that are on average too long or too short increase available inventory; and the further the average error is from zero, the more pronounced the increase. Contrary to conventional wisdom, increasing PLTs will increase the service level (decrease backorders), unless PLTs are already severely inflated and MPS uncertainty (forecast error) is small. If PLTs are inflated, decreasing them will decrease the number of setups per unit time in the case of considerable demand uncertainty. Contrary to conventional wisdom, increasing PLTs causes only a transient rise WIP inventory.The fact that the average lead time error has a significant effect on the three areas of system effectiveness mentioned above does not imply that a given order's lead time should be managed in a way that forces its actual lead time to match the PLT. Stated another way, the material planner may use the latest information to manage a given order's lead time; however, if the average discrepancy between the actual and planned lead times is large, system performance can be improved by changing the PLTs to approximate the average flow times.Three methods that have been proposed for determining PLTs are compared. They are historical averages of the actual flow times, calculated lead times based on standard times and historical averages of the queuing time at the appropriate work centers, and the QUOAT lead time proposed by Hoyt. The third was found to perform poorly unless the work content of all operations is identical. With one exception, no differences were found between the first two methods. The simpler historical average method was superior to the calculated lead time in the case where the work content of each operation varies and when considerable demand uncertainty exists.The results are based on simulation experiments employing a generalized MRP/Job‐Shop stochastic simulation model. The program launches orders based on standard MRP logic, reschedules open orders by moving the due date in or out to coincide with revised need dates, moves manufacturing orders through a job shop, schedules the delivery of purchase orders, and updates inventory levels. The product structure tree contained eight distinct items, with four levels and one end item. There is no reason to believe that the conclusions would be any different had a larger system been studied.

Optimal and Heuristic Procedures for Component Lot-Splitting in Multi-Stage Manufacturing Systems

Component lot-splitting considerations, in which the lot-size of a component item may cover only a fraction of its parent item's lot-size, have been ignored in the literature when determining lot-sizes of items in multi-stage manufacturing systems. In this paper, a multi-stage lot-sizing problem is formulated under a specified component lot-splitting policy for the case of noninstantaneous production of items and constant demand for the end item. Optimal and heuristic solution procedures for the formulated problem are provided, including experimental results of comparison between these procedures. It is shown that considerable cost savings can result if the component lot-splitting approach is employed under favorable conditions in multi-stage manufacturing environments. In addition, reduced inventory levels are achieved which translate into lower working capital requirements and a less cluttered shop floor. The heuristic procedure is recommended as an acceptable alternative to the optimal procedure if the number of items in the multi-stage system is large or if inventory carrying and setup/order costs cannot be accurately estimated. Further, component lot-splitting considerations may be ignored if production rates of facilities in the system are in balance. Finally, a methodology for application of the component lot-splitting policy where the end item demand is time-varying is discussed.

Transitory Changes of Primacy and Recency in Successive Single-Trial Free Recall.

Transitory changes of primacy and recency in successive single-trial free recall were investigated in three experiments. A trial-by-trial analysis of the serial position curves indicated a pronounced primacy and a moderate recency on the very first recall trial. The primacy reduced and the recency increased rapidly afterward with a concurrent shift in correlation (from positive to negative) between the input and output order of the items recalled and an increase in output priority of end items. The effects of release of interlist proactive interference (PI) on the primacy and recency were studied in Experiments 2 and 3. Release of interlist PI recovered and maintained the pronounced primacy and the overall recall performance. In addition, the output priority of end items and the correlation between the input and output order of the recalled items did not change significantly when the interlist PI was controlled. Therefore, interlist PI affected the primacy; recall strategy or output priority affected the recency; and the development of recall strategy relied on the presence of interlist PI.

Establishing Consistent and Realistic Reorder Intervals in Production-Distribution Systems

The objective of this paper is to present a model and algorithm that can be used to find consistent and realistic reorder intervals for each item in large-scale production-distribution systems. We assume such systems can be represented by directed acyclic graphs. Demand for each end item is assumed to occur at a constant and continuous rate. Production is instantaneous and no backorders are allowed. Both fixed setup costs and echelon holding costs are charged at each stage. We limit our attention to nested and stationary policies. Furthermore, we restrict the reorder interval for each stage to be a power of 2 times a base planning period. The model that results from these assumptions is an integer nonlinear programming problem. The optimal solution can be found using the proposed algorithm, which is a polynomial time algorithm. A real world example is given to illustrate the procedure.

Cyclic Assembly Schedules

Abstract Cyclic assembly schedules are schedules which follow a repeating production pattern. Each component is produced at times that are integer multiples of the base planning period of the finished product. These schedules have the managerial advantages of ease of control and planning, since future production follows a familiar, repeating pattern. Establishing and maintaining such a schedule requires a system to protect the schedule when bottlenecks occur. This paper discusses some of these issues and describes a simple method for calculating the optimal parameters of a cyclic schedule in a multistage assembly operation when production intervals are power-of-two multiples of a base period specified by management. The cycles are based on an Economic Order Quantity analysis, which is applicable if demand for the end items is fairly steady as in the case of a facility that supplies components to an assembly line.

Safety stocks and component commonality

AbstractIn the implementation of an MRP‐based planning system it is often desirable to hold safety stocks at the component level. This approach is typical of a system that assembles finished products to order but builds components to a forecast. The same approach makes sense when end‐item buffers would be prohibitively expensive to maintain, or when there is significant spares demand for the components.The existing theory for sizing component safety stocks is little more than an application of the principles governing single‐level inventory decisions. The standard deviation of the distribution for demand is the key parameter. A multiple of this standard deviation, called the safety factor, is set by management policy. This safety factor determines the size of the safety stock. In simple cases there is a direct relationship between the safety factor and the level of customer service.Component commonality refers to a situation in which one component is common to more than one end item. The presence of commonality makes it difficult to determine safety stocks accurately. The existing theory, which is reviewed briefly, examines the effects of commonality on the component's safety factor. However, commonality destroys the relationship between safety factor and service level. This article introduces a simple example to illustrate this fact. The example suggests some problems that must be addressed if the theory is to encompass commonality. Among these are the need to develop service level measures for multiple products and to analyze the relationship between safety stock and service level.

PRODUCT STRUCTURE COMPLEXITY AND MULTILEVEL LOT SIZING USING ALTERNATIVE COSTING POLICIES*

ABSTRACTThis research evaluates the effect of product structure complexity on the performance of several lot‐sizing procedures in a multilevel manufacturing environment. The experiment compares two different costing policies, full value added (FVA) and marginal value added (MVA), for calculating inventory holding cost. The major finding of the research is that product structure complexity has very little effect on the performance of various lot‐sizing procedures. A second finding is that when product structures with varying components per parent and stocking points for a particular end item are present, the MVA costing policy emerges as the policy of choice because it favors slightly the Silver‐Meal (SM)/least‐total‐cost (LTC) procedures over the Wagner‐Whitin (WW)/LTC procedures.

Continuous-Review Policies for a Multi-Echelon Inventory Problem with Stochastic Demand

This paper considers a multi-stage, serial inventory system. The demand for the end item is stochastic and stationary. The relevant costs include a fixed ordering cost and an echelon inventory holding cost for each stage, and a backordering cost for the end item. The objective is to find a continuous-review inventory control policy that minimizes the expected average costs. We present and analyze an approximate cost model. Our approximation is analogous to that for the traditional single-item, continuous-review inventory model that assumes a reorder-point, reorder-quantity policy. We obtain policies that are natural extensions to those for the single-item model.