Multiplant MRP

Many manufacturing firms have multiple manufacturing plants, located in geographically diverse parts of the world. This situation is becoming more common, as firms establish new plants in foreign countries to take advantage of low labor cost. In such cases, it is not unusual for the firm to retain production capability of certain key parts in a backup plant, with the necessary equipment and trained workforce in place. High volume production could be obtained relatively quickly from the backup plant in case of an emergency at the main supplying plant. In such multiplant settings, the transportation costs are significant. Throughout this paper, we use the term “multisourced parts” to describe parts produced in more than one location.Material Requirements Planning (MRP) is the component of a total manufacturing control system that is designed to manage inventory and plan orders for parts and material with dependent demand (demand derived from the demand of other items). Most of the literature on MRP systems discusses MRP methodology in a single-plant environment. Most MRP software systems in use today are single-plant systems.Currently, it is common for firms with multiple plants treated as cost centers to use an independent single-plant MRP system for each and handle the transshipment problems manually. Because of lack of coordination of production schedules between supplying and demanding plants, those firms hold more inventory and experience longer lead times than necessary to compensate for uncertainties in schedules and supply policies.The purpose of this article is to enhance single-plant MRP systems for coping with multiplant situations in which: the plants are regarded as cost centers, there exist multisourced parts, and the transportation costs are significant. The multiplant MRP system should recognize that parts are produced in different plants, make offset calculations for in-transit lead times, and consider transportation costs when establishing production requirements and shipping routes for multisourced parts. The objective is, beginning with the corporate-determined master schedule for finished products, to communicate in one planning cycle time-phased planned order release schedules and shipping/delivery schedules to each manufacturing plant producing components for the finished products.We first present a simplified framework for the multiplant MRP system, where a transportation algorithm is incorporated into the MRP logic. Then we refine this simplified framework to handle more complex aspects of a multiplant network. These complexities include the treatment of requirements that are not shipped on time and the regeneration of new MRP schedules. We also observe that the solution to the transportation problem described above is affected by the lot-sizing rules employed. In addition, we discuss several important issues and decisions that confront a firm when implementing a multiplant MRP system.     

Toward a better understanding of lead times in MRP systems

Lead times in MRP systems represent the planned amount of time allowed for orders to flow through the manufacturing system. Setting lead times is a major issue in the operation of MRP systems. There exists, however, very little documentation on just how lead times should be set.This study examines the effects lead times have on MRP-based manufacturing logistics systems. In particular, it examines the effects that lead times have on backlogs, order tardiness, and finished component inventories.A major finding is that changes to the level of planned lead times have both transient and steady state effects that may not necessarily operate in the same direction. A simple methodology is presented for setting the level of planned lead times when the criterion is finished component inventory.     

Resolving uncertainty in manufacturing systems

This paper examines the effectiveness of three commonly practiced methods used to resolve uncertainty in multi-stage manufacturing systems: safety stock under regenerative material requirements planning (MRP) updates, safety capacity under regenerative MRP updates, and net change MRP updates, i.e., continuous rather than regenerative (periodic) updates. The use of safety stock reflects a decision to permanently store materials and labor capacity in the form of inventory. When unexpected shortages arise between regenerative MRP updates, safety stock may be depleted but it will be replenished in subsequent periods. The second method, safety capacity, overstates the MRP capacity requirements at the individual work centers by a prescribed amount of direct labor. Safety capacity either will be allocated to unanticipated requirements which arise between MRP regenerations or will be spent as idle time. The third method, net change, offers a means of dealing with uncertainty by rescheduling instead of buffering, provided there is sufficient lead time to execute the changes in the material and capacity plans.Much of the inventory management research has addressed the use of safety stock as a buffer against uncertainty for a single product and manufacturing stage. However, there has been no work which evaluates the performance of safety stock relative to other resolution methods such as safety capacity or more frequent planning revisions. In this paper, a simulation model of a multi-stage (fabrication and assembly) process is used to characterize the behavior of the three resolution methods when errors are present in the demand and time standard estimates. Four end products are completed at an assembly center and altogether, the end products require the fabrication of twelve component parts in a job shop which contains eight work centers. In addition to the examination of the three methods under different sources and levels of uncertainty, different levels of bill of material commonality, MRP planned lead times, MRP lot sizes, equipment set-up times and priority dispatching rules are considered in the experimental design.The simulation results indicate that the choice among methods depends upon the source of uncertainty, and costs related to regular time employment, employment changes, equipment set ups and materials investment. For example, the choice between safety stock and safety capacity represents a compromise between materials investment and regular time employment costs. The net change method is not designed to deal effectively with time standard errors, although its use may be preferred over the two buffering alternatives when errors are present in the demand forecasts and when the costs of employment changes and equipment set ups are low. The simulation results also indicate that regardless of the method used, efforts to improve forecasts of demands or processing times may be justified by corresponding improvements in manufacturing performance.     

Overlapping operations in material requirements planning

Material requirements planning (MRP) is a planning and information system that has widespread application in discrete-parts manufacturing. The purpose of this article is to introduce ideas that can improve the flow of material through complex manufacturing systems operating under MRP, and that can increase the applicability of MRP within diverse manufacturing environments.MRP models the flow of material by assuming that items flow from work station to work station in the same batches that are used in production. That is, once work starts on a batch of a certain item at a certain work station, the entire batch will be produced before any part of the batch will be transported to the next work station on its routing plan. Clearly, efficiency can be increased if some parallelism can be introduced. The form of parallelism investigated here is overlapping operations.Overlapping operations occurs when the transportation of partial batches to a downstream work station is allowed while work proceeds to complete the batch at the upstream work station. The potential efficiencies to be gained are the following:

• Reduced work-in-process inventory

• Reduced floor space requirements

• Reduced size of transfer vehicles

Additional costs may accrue through additional cost of transportation of partial batches and through additional costs of control.Some MRP software vendors provide the data processing capability for overlapping operations. However, the user is given little or no guidance on overlapping percentages or amounts. It is our intent to provide a simple, robust technique to MRP users who would like to overlap operations and gain some or all of the above efficiencies.An optimal lot-sizing technique is derived by considering a generic two work station segment of a manufacturing system. Under the assumptions of constant demand and identical production rates, a cost function that considers setup costs, inventory holding costs and transportation costs is derived. This cost function is minimized subject to the constraint that the production batch is an integer multiple of the transfer batch. We solve for the optimal production batch, the optimal transfer batch, and the integer number relating them. Solutions are obtained as closed form, easy to-evaluate formulas.By introducing more parallelism, overlapping operations can reduce lead time. However, this will not happen without modification of MRP logic to accommodate such reduced lead time. We derive a formula that shows how a significant lead time compression can easily be obtained and implemented in MRP.We consider an example to illustrate the application of the technique on typical data from the electronics industry. The outcome showed a cost savings of approximately 22.5% over the standard MRP approach.Overlapping operations allows the applicability of MRP to an increasing number of situations that are not modeled faithfully by conventional MRP logic. Three such situations that occur often are the following:

• Limited size of transfer vehicles dictate that several transfers should be planned.

• Lead time requirements prohibit nonoverlapped operations.

Our analysis suggests how to accommodate these difficult practical situations into MRP.Overlapping operations in material requirements planning provides an enhancement that allows wider applicability, shortened lead times, and lower total costs. It may be applied selectively to any two work stations where it is deemed appropriate. Due to the structure of the cost function, it is possible to make the transfer lot-sizing decisions independent of the production lot-sizing decisions. Therefore, significant improvements can be made through overlapping with minimum disruption to the existing MRP system machinery. It is our conviction that overlapping operations is an important concept that can and will impact MRP. We suggest the approach presented here as a systematic way to implement overlapping.     

The effect of lot-sizing on workload variability

Lot-sizing models which group demand requirements for one or more consecutive time periods into a single production run have received considerable attention in recent years. Material Requirements Planning (MRP) systems must, for instance, make a lot-size decision for each planned order release. Existing decision models attempt to minimize the sum of setup plus inventory holding costs. However, lot-sizing tends to increase the work center load variability, and, consequently, the costs associated with changing production levels from period to period should be incorporated into the economic analysis. This study is concerned, first of all, with analytically describing the relationship between dynamic lot-sizing models and workload variability. Secondly, in order to account for production level change costs we propose a simple modification to existing heuristic models. Lastly, we employ a simulation model to empirically extend these results to a typical MRP multiechelon production environment. An example is included to show clearly that with cost premiums for overtime and severance or guaranteed minimum costs for undertime the traditional lot-sizing techniques significantly underestimate actual costs and can lead to very costly policies.Mean, variance and coefficient of variation of period work time requirements are derived as a function of several algorithm characteristics. Average cycle time (number of periods covered by a single batch) is found to be the most influential factor in determining workload variability. Variance grows approximately in proportion to this cycle time with the proportionality constant being the square of average period workload. Cycle time and demand variability also contribute to workload variability. Results indicate that for a given average cycle time, the EOQ method will minimize workload variability. When N products utilize the same work center, the coefficient of load variation will be reduced by a factor of N^{?$\text{1}\text{2}$} unless requirements are positively correlated. Positive correlation would result when products have similar seasons or parent items. In this case grouping such products cannot help reduce variability.In order to incorporate production level change costs into existing heuristics we may simply introduce a term consisting of a penalty factor times average cycle time. The penalty factor represents the costs of period by period production level changes. Several popular heuristics are extended in this fashion, and it is found that solutions are still readily obtainable, requiring only modifications to setup or holding cost parameters.The effects of level change costs are examined via simulation for a specific yet typical environment. It is found that when setup costs are significant, traditional lot-sizing heuristics can provide cost savings and service level improvements as compared to lot-for-lot production. However, whereas for our model the obtainable profit improvement from lot-sizing was 25% in the case of freely variable capacity, actual improvements were only one half as large when reasonable hiring and firing practices and overtime and undertime costs were considered. Consequently, management needs to consider carefully labor costs and work center product relationships when determining a production scheduling method.     

The interchangeability of safety stocks and safety lead time

Due to the uncertainty in estimating both the demand for end products and the supply of components from lower levels, buffering techniques should be included before the loading of a material requirement planning (MRP) system. Safety stocks and safety lead time are two techniques of providing buffering for loading. There have been many studies made concerning the determination of the amount of safety stocks and safety lead time. Some guidelines for choosing between safety stocks and safety lead time for dealing with uncertainty in both demand and supply also have been established. Although these two different methods have been used successfully, it has not been documented that using these two methods in a given situation will yield essentially the same results; that is, the interchangeability of these two buffering techniques has not been explored quantitatively.Since the net influence of safety stocks and safety lead time and their quantitative interchangeability are of major interest, an analytical model is proposed for this study. The lead-time offset procedure for components loading are represented by a matrix model that is based on a lot-for-lot lot-sizing technique. This lead-time offset matrix model is the product of the precedence matrix and the fixed-duration matrix. The precedence matrix is formed according to the total requirement factor matrix and the duration matrix is formed by each component process time. Thus, the lead-time offset matrix will generate the starting period of each component.When the lead-time offset procedure is modeled, the net influence of buffering quantity can be analyzed. The planned safety stock that is normally used to accommodate unexpected demand, shortage in supply, and defects from the operation at each process can be combined with demand to form the master production schedule. The revised lead time due to the integration of the safety stocks can be calculated through the lead-time offset model. The safety lead time may extend the component process time as well as overall production lead time if the designated safety lead time is longer than the available slack time in a fixed lead-time loading system.When the proposed lead-time offset model is further examined, it is found that planned safety stocks at the higher level can buffer the fluctuations of lower level components quantity as well as the fluctuations of same level components quantity. Safety stocks can also buffer shortages that are caused by the delay of raw material and manufacturing processes. Thus, safety stocks can be used to buffer unexpected delay time up to certain limits. A planned safety lead time at higher level component process can buffer the fluctuations of lower level components process time, as well as the same level component process time. The safety lead time can be used to produce additional products to meet unexpected excessive demand up to certain limits under the following conditions: 1. The excessive demand is known before the actual processing of the components in the lowest level. 2. The raw material at the lowest level is available.Although safety stocks and safety lead time are interchangeable in terms of the ability to buffer variations in quantity, the conditions for safety lead time are seldom met in actual practices. Thus, the slack time in a fixed lead-time loading system cannot be considered as an effective measure to substitute safety stocks. However, all or part of the delay in manufacturing processes or the supply from the lower level components can be buffered by the safety stock and the MPS will still be met. From this study, it is obvious that the slack time can be reduced when safety stocks are planned for an MRP system. The reduction of fixed lead-time duration will be beneficial to the overall planning and scheduling in MRP systems.     

汽车制造行业零部件仓储与配送WPSPI模式研究

汽车制造行业目前普遍采用以主机厂为核心的JIT生产模式,但在JIT模式具体应用的过程中由于种种原因并没有实现在整个供应链内消除库存,大多数情况下只是实现了库存从主机厂向供应链供应商的转移,这种转移无疑增加了供应商的仓储库存成本,鉴于上述情况经过对某大型汽车制造企业上游供应链典型企业的研究,提出了全过程货架托盘一体化物流模式(简称WPSPI,即Whole Process goods Shelf&Pallet Integration模式),该模式旨在减少供应链物流中的包装转换及对仓储设施的购置费用,同时实现整个供应链物流效率的提高和物流成本的降低。     

Scheduling of re-entrant flow shops

We propose and develop a scheduling system for a very special type of flow shop. This flow shop processes a variety of jobs that are identical from a processing point of view. All jobs have the same routing over the facilities of the shop and require the same amount of processing time at each facility. Individual jobs, though, may differ since they may have different tasks performed upon them at a particular facility. Examples of such shops are flexible machining systems and integrated circuit fabrication processes. In a flexible machining system, all jobs may have the same routing over the facilities, but the actual tasks performed may differ; for instance, a drilling operation may vary in the placement or size of the holes. Similarly, for integrated circuit manufacturing, although all jobs may follow the same routing, the jobs will be differentiated at the photolithographic operations. The photolitho-graphic process establishes patterns upon the silicon wafers where the patterns differ according to the mask that is used.The flow shop that we consider has another important feature, namely the job routing is such that a job may return one or more times to any facility. We say that when a job returns to a facility it reenters the flow at that facility, and consequently we call the shop a re-entrant flow shop. In integrated circuit manufacturing, a particular integrated circuit will return several times to the photolithographic process in order to place several layers of patterns on the wafer. Similarly, in a flexible machining system, a job may have to return to a particular station several times for additional metal-cutting operations.These re-entrant flow shops are usually operated and scheduled as general job shops, ignoring the inherent structure of the shop flow. Viewing such shops as job shops means using myopic scheduling rules to sequence jobs at each facility and usually requires large queues of work-in-process inventory in order to maintain high facility utilization, but at the expense of long throughput times.In this paper we develop a cyclic scheduling method that takes advantage of the flow character of the process. The cycle period is the inverse of the desired production rate (jobs per day). The cyclic schedule is predicated upon the requirement that during each cycle the shop should perform all of the tasks required to complete a job, although possibly on different jobs. In other words, during a cycle period we require each facility to do each task assigned to it exactly once. With this requirement, a cyclic schedule is just the sequencing and timing on each facility of all of the tasks that that facility must perform during each cycle period. This cyclic schedule is to be repeated by each facility each cycle period. The determination of the best cyclic schedule is a very difficult combinatorial optimization problem that we cannot solve optimally for actual operations. Rather, we present a computerized heuristic procedure that seems very effective at producing good schedules. We have found that the throughput time of these schedules is much less than that achievable with myopic sequencing rules as used in a job shop. We are attempting to implement the scheduling system at an integrated circuit fabrication facility.     

A cost evaluation method for dynamic line balancing

A procedure is presented for calculating stochastic costs, which include operator (labor) and inventory costs, associated with dynamic line balancing. Dynamic line balancing, unlike the traditional methods of assembly and production line balancing, assigns operators to one or more operations, where each operation has a predetermined processing time and is defined as a group of identical parallel stations. Operator costs and inventory costs are stochastic because they are functions of the assignment process employed in balancing the line, which may vary throughout the balancing period, and the required flow rate. Earlier studies focused on the calculation of the required number of stations and demonstrated why the initial and final inventories at the different operations are balanced.The cost minimization method developed in the article can be used to evaluate and compare the assignment of operators to stations for various assignment heuristics. Operator costs and inventory costs are the components of the cost function. The operator costs are based on the operations to which operators are assigned and are calculated for the entire work week regardless of whether an operator is given only a partial assignment which results in idle time. It is assumed that there is no variation in station speeds, no learning curve effect for operators' performance times, and no limit on the number of operators available for assignment. The costs associated with work-in-process inventories are computed on a “value added” basis. There is no charge for finished goods inventory after the last operation or raw material before the first operation.The conditions which must be examined before using the cost evaluation method are yield, input requirements, operator requirements, scheduling requirements and output requirements. Yield reflects the output of good units at any operation. The input requirement accounts for units discarded or in need of reworking. The operator requirements define the calculation of operator-hours per hour, set the minimum number of operators at an operation, and require that the work is completed. The scheduling requirements ensure that operators are either working or idle at all times, and that no operator is assigned to more than one operation at any time. The calculation of the output reflects the yield, station speed, and work assignments at the last operation on the line.An application of the cost evaluation method is discussed in the final section of the article. Using a simple heuristic to assign operators, the conditions for yield, inputs, operators, scheduling, and output are satisfied. The costs are then calculated for operators and inventories.In conclusion, the cost evaluation method for dynamic balancing enables a manager to compare the costs of assigning operators to work stations. Using this method to calculate the operator and inventory costs, a number of different heuristics for assigning operators in dynamic balancing can be evaluated and compared for various configurations of the production line. The least cost solution procedure then can be applied to a real manufacturing situation with similar characteristics.     

Computer simulation of lot-sizing algorithms in three-stage multi-echelon inventory systems

This paper evaluates conventional lot-sizing rules in a multi-echelon coalescence MRP system. A part explosion diagram of three levels and twenty-one nodes is simulated using FORTRAN IV Level G. Nine separate lot-sizing methods were evaluated in this analysis. These methods included Lot for Lot, Economic Order Quantity, Periodic Order Quantity, Least Total Cost, Least Unit Cost, Part-Period Balancing, the Silver-Meal Algorithm, and the Wagner-Whitin Algorithm. A hybrid rule using both the Economic Order and the Economic Production Quantity rules was also evaluated.The performance of each lot sizing rule was simulated over nine different sets of market requirements patterns over a twelve month period. The types of demand variation included a constant rate, three different patterns of normally distributed demand, a random pattern, and two cyclic patterns. A hybrid pattern was used which equally weighted components of constant, random, normal, and cyclic demand. Finally, the ninth pattern consisted of actual data obtained from a job shop manufacturing facility.Within the part explosion diagram, ratios of setup cost to carrying cost, “goes into” quantities, and lead times were assigned for each node. Assigned values were selected from uniform distributions with prespecified ranges.A computer model was developed to perform the simulation. It consisted of an executive program, a routine for data generation, and separate routines to exercise each of the different lot-sizing rules. The simulations were conducted under three operational rules. The first rule allowed for the establishment of initial inventories just large enough to “cover” those gross requirements that occurred prior to the time the first order arrived. Carrying costs for this stock were included in the computation of total costs per node. The second rule provided for the delay of application of each lot sizing rule. This avoided receiving an order in a period of zero demand. The third rule addressed the computation of costs. The total cost was computed on the basis of average inventory level and the number of required setups.The analysis required the completion of 1701 separate simulation runs (9 rules X 9 demand patterns X 21 nodes). The performance of each rule was evaluated on the basis of total annual inventory cost. The Periodic Order Quantity (POQ) rule performed best in six of the nine demand patterns analyzed. In two of the remaining three cases, it ranked second on the basis of minimizing costs.The Least Unit Cost (LUC), Least Total Cost (LTC), and Pan-Period Balancing (PPB) algorithms demonstrated identical performance in four of the demand patterns analyzed. Generally, they ranked in the upper half of the rules evaluated. The Economic Order Quantity (EOQ) and the Economic Order/Production Quantity hybrid rules performed only moderately well. On the basis of cost, the consistent worst performers were the Wagner-Whitin (WW), Silver-Meal (SM), and Lot-for-Lot (LFL) rules.It was found that gross requirements tend to occur sporadically in different levels of the system. Order policies of parent nodes often cause the policies in higher level nodes to resemble the lot-for-lot order philosophy, regardless of the rule being used. Because of this phenomenon, those rules that generate fewer orders over the planning horizon for parent nodes often tend to perform better on the basis of total inventory cost.     

Inventory accuracy in 60 days!

Despite great advances in manufacturing technology and management science, thousands of organizations still don't have a handle on basic inventory accuracy. Many companies don't even measure it properly, or at all, and lack corrective action programs to improve it. This article offers an approach that has proven successful a number of times, when companies were quite serious about making improvements. Not only can it be implemented, but also it can likely be implemented within 60 days per area, if properly managed. The hardest part is selling people on the need to improve and then keeping them motivated. The net cost of such a program? Probably less than nothing, since the benefits gained usually far exceed the costs. Improved inventory accuracy can aid in enhancing customer service, determining purchasing and manufacturing priorities, reducing operating costs, and increasing the accuracy of financial records. This article also addresses the gap in contemporary literature regarding accuracy program features for repetitive, JIT, cellular, and process- and project-oriented environments.     

What drives financial performance-resource efficiency or resource slack?: Evidence from U.S. Based Manufacturing Firms from 1991 to 2006

Extant research in operations management has revealed divergent insights into the value potential of resource efficiency. While one view relates efficiency with good operations management and asserts that slack resources are a form of waste that should be minimized, the other view suggests that limited resource slack can impose heavy costs on firms by making them brittle. In this research, the authors build on these views to investigate the relationship of inventory, production, and marketing resource efficiency of firms with three metrics of financial performance (i.e., Stock-Returns, Tobin's Q, and Returns-on-Assets). The authors evaluate the theoretical framework using secondary information on all U.S. based publicly-owned manufacturing firms across the 16-year time period of 1991-2006. Analysis utilizing a mixed-model approach reveals that a focus on resource efficiency is positively associated with firm financial performance. However, findings also support the arguments favoring slack, indicating that the financial gains from resource efficiency exhibit diminishing returns.     

Production and supply management strategies in Nordic paper mills

The traditional production management strategy in paper manufacturing is based on a volume-intensive approach. This involves the measurement of overall performance or productivity, while aiming at a high level of capacity utilisation and minimum waste levels. This approach has proved successful in mills producing high volumes with a limited and standardised product range. The situation changes radically when paper and board products are being tailored to customer-specified dimensions and quantities. The volume-based approach is no longer appropriate, and production has to be controlled by an approach that considers inventory performance along the full length of the supply chains. This paper presents five empirical examples to illustrate the use of the two strategies. The detailed analyses of production cycles, the logistical solutions applied and the inventory levels at various stages of the supply chain, show that the Nordic paper industry is slow, with average lead times of 79 days to market. When production cycles are reduced and logistical alternatives are fully exploited, it can be seen that 30% of the inventories can be regarded as slack. The summary of the cases shows that speedier operations easily generate direct cost savings amounting to 2–5% of annual turnover. All these results can be achieved without additional investment; all that is required is a change in production planning principles and logistical control procedures. The paper concludes with a challenge to the Nordic paper industry to be the first in its field to achieve the higher level of productivity that faster operations can generate.     

Load-limited order release in job shop scheduling systems

This report examines the practice of using work load limits to control the release of orders to a job shop. Load limits function in the following general way. Whenever the inventory of work at a work center exceeds some critical value (its “load limit”), further release of orders which are routed to that work center are blocked from entering the shop. After the inventory is “worked off,” release of work to the shop gateways is again permitted. Load-limited order release is intuitively appealing because it appears to be a method for reducing system inventory and flow times. The practice of load limiting order release is becoming popularized by some of the recent production planning software products now on the market. A notable example is OPT. In this report, analytical results for an M/M/1 queueing model, along with existing simulation studies of multi-machine job shops are interpreted to form a theory about the effects of using load limits.The major finding here is the proposition that system flow time, inventory, and order tardiness all deteriorate to the extent that load limits introduce idle time into the schedule. Based on the arguments presented here, a very cautious approach toward the use of input control schemes for anywhere but gateway work centers would be advised. The conclusions drawn here are to a great extent arrived at by interpreting the research results of others, so there is a clear need for further research which tests these assertions in a more direct and controlled way.     

Queue control using a historically developed stochastic model

This paper applies a stochastic model to determine the optimal or ideal average planned queue level. The ideal average planned queue level is defined to be the minimum average queue level necessary to ensure that in the long run the probability of work center idle time over a specified planning horizon is no greater than some value α chosen by management. Also discussed is the usefulness of the ideal planned queue level, not only for controlling work-in-process inventories, but also for obtaining better work center lead time estimates.     

A formal set of algorithms for project scheduling with critical path scheduling/material requirements planning

A hybrid model combining the critical path method (CPM) with material requirements planning (MRP) has been suggested (Aquilano) as a more robust method for scheduling projects and resources. The primary advantage of this technique is that resource acquisition lead times as well as inventory records are integrated into the process of computing the project schedule. This paper presents a set of formal CPM/MRP algorithms that may be used to compute the early and late start schedules as well as the critical sequence. A number of modifications have been incorporated into the CPM/MRP technique to improve the viability of CPM/MRP as a tool for application to actual project scheduling problems. A simple example project is used to demonstrate the CPM/MRP model.The CPM/MRP technique is designed to overcome a basic shortcoming of previously suggested project scheduling methodologies. CPM was initially designed to schedule projects subject to technological constraints only. Later, additional techniques were introduced to consider constraints upon various aspects of resource availability (Davis). None of the suggested techniques attempted to integrate resource acquisition lead time with the generation of requirements for resources. Obviously such a technique would require the integration of inventory records into the scheduling technique.The combination of CPM and MRP provides a possible vehicle for overcoming this drawback in CPM. Both CPM and MRP are linear models that generate schedules based upon precedence relationships. An integrated approach is useful since activities could be scheduled subject to information about the inventory position. An activity may be scheduled as soon as all resources are on hand. It is only delayed by those resources which must be acquired and activities which proceed it in the project network.CPM/MRP also shows promise as an aid to constrained resource scheduling since computations regarding resource availability are an integrated part of the technique. The effect of resource allocation decisions is immediately evident in the MRP-type time phased records.Results of the tests run on short projects of up to 300 activities and resources have shown that the program does work satisfactorily. Execution time for a 300 item network tested was approximately ten seconds on a CYBER 175.     

A heuristic algorithm for capacity sensitive requirements planning

Available lot sizing rules for use in MRP (Material Requirements Planning) systems ignore capacity limitations at various work centers when sizing future orders. Planned order releases are instead determined by the tradeoff only between the item's set up and inventory holding costs. This limitation can cause unanticipated overloads and underloads at the various work centers, along with higher inventories, poorer customer service, and excessive overtime.This research explores one way to make MRP systems more sensitive to capacity limitations at the time of each regeneration run. A relatively simple heuristic algorithm is designed for this purpose. The procedure is applied to those planned order releases that standard MRP logic identifies as mature for release. The lot sizes for a small percentage of these items are increased or decreased so as to have the greatest impact in smoothing capacity requirements at the various work centers in the system. This algorithm for better integrating material requirements plans and capacity requirements plans is tested with a large scale simulator in a variety of manufacturing environments. This simulator has subsequently undergone extensive tests, including its successful validation with actual data at a large plant of major corporations.Simulation results show that the algorithm's modest extension to MRP logic significantly helps overall performance, particularly with customer service. For a wide range of test environments, past due orders were reduced by more than 30% when the algorithm was used. Inventory levels and capacity problems also improved. Not surprisingly, the algorithm helps the most (compared to not using it at all as an MRP enhancement) in environments in which short-term bottlenecks are most severe. Large lot sizes and tight shop capacities are characteristic of these environments. The algorithm works the best when forecast errors are not excessive and the master schedule is not too “nervous.”This proposed procedure is but one step toward making MRP more capacity sensitive. The widely heralded concept of “closed-loop” MRP means that inventory analysts must change or “fix up” parts of the computer generated material requirements plan. What has been missing is a tool for identifying the unrealistic parts of the plan. Our algorithm helps formalize this identification process and singles out a few planned order releases each week. This information comes to the analyst's attention as part of the usual action notices. These pointers to capacity problems go well beyond capacity requirements planning (CRP) and would be impossible without computer assistance.Our study produced two other findings. First, short-term bottlenecks occur even when the master production schedule is leveled. The culprits are the lot sizing choices for items at lower levels in the bills of material. “Rough-cut” capacity planning, such as resource requirements planning, therefore is not a sufficient tool for leveling capacity requirements. It must be supplemented by a way to smooth bottlenecks otherwise caused by shop orders for intermediate items. Second, the disruptive effect of large lot sizes is apparent, both in terms of higher inventories and worse customer service. Large lot sizes not only inflate inventories, but paradoxically hurt customer service because they create more capacity bottlenecks. The only reason why management should prefer large lot sizes is if set-up times are substantial and cannot be efficiently reduced. This finding is very much in step with the current interest in just-in-time (JIT) systems.     

Mission impossible? The boss wants to double our inventory turns.

Despite the prolific implementation of manufacturing systems, JIT principles, Kaizen events, and cycle time reduction programs over the past few years, high inventories still plague many companies. The assumption that implementing these principles and techniques will automatically result in inventory levels that satisfy management frequently proves to be false. Events like mergers, introduction of new competition, and a dropoff in business often trigger edicts to cut inventories. The cost of inventories also extends beyond the traditional accounting measurements to include hidden operating costs that everyone should want to eliminate. This article looks at the reasons for inventories and explores strategies for reducing them.     

A lot sizing and sequencing algorithm for dynamic demands upon a single facility

A heuristic algorithm is developed and applied to determine lot sizes and production sequence on a single facility. The various product demands are treated as deterministic and time varying (dynamic) over a finite planning horizon, such as that generated from a material requirements planning (MRP) system.In contrast to other approaches available, the algorithm considers the sequencing decision in each period by realistically assuming inventory holding cost occurrence in the period of production, and in addition, it is capable of considering set-up times where such set-up times consume available productive capacity. The ability to handle numerous products, and the capability of being able to specify maintenance time and holidays is an integral aspect of the algorithm.The results of an application of the algorithm in a medium size bearing company have shown very significant reduction in the controllable inventory holding cost while eliminating late deliveries. In an effort to cope with potential realistic schedule alterations, different solutions were developed for managerial evaluation providing greater flexibility but at a higher cost.     

The direct digital manufacturing (r)evolution: definition of a research agenda

Direct digital manufacturing, or ‘3D printing’ as it is more commonly known, offers a wealth of opportunities for product and process innovation, and is often touted to ‘revolutionize’ today’s manufacturing operations and its associated supply chains structures. Despite a growing number of successful 3D printing applications, however, evidence of any displacement of traditional manufacturing is limited. In this paper we seek to separate hype surrounding DDM from economic reality in order to ground the future research agenda for the Operations Management field. By opposing direct digital manufacturing with traditional tool-based manufacturing, we show that direct digital manufacturing so far lags behind by several orders of magnitude compared to traditional manufacturing methods. Yet we also find that direct digital manufacturing clearly is on an improvement trajectory that eventually will see it being able to compete with traditional manufacturing on a unit cost basis. As such we conclude that direct digital manufacturing will increasingly challenge operations management researchers to question established practices such as scheduling, batch sizing and inventory management in low-volume, high-variety contexts. Furthermore, an increasing adoption of direct digital manufacturing will drive structural shifts in the supply chain that are not yet well understood. We summarize these challenges by defining the research agenda at factory, supply chain, and operations strategy level.