A generalized lot sizing model for the process cycling problem

Most scheduling/lot sizing models for the single-machine problem assume that aggregate demand equals aggregate production; and that backorders are to be avoided. Where working inventories are low, the scheduler may wish to avoid short production runs and willingly incur some backorder penalties so as to increase the length of production runs and reduce setup costs per unit of time. The model proposed here identifies optimal lot sizes with respect to the backorder/setup cost relationships. Use of the model will result in an optimally balanced inventory even when aggregate inventory levels are changing.     

On the interpretation of cointegration in the linear–quadratic inventory model

The traditional formulation of the linear–quadratic inventory model with unit roots predicts cointegration between inventories and sales. That formulation implies that marginal production costs and the marginal benefits of inventories are both tending to ∞, and the cointegrating coefficient reflects the optimal trade-off between these competing factors. This paper suggests a reformulation of the problem in which marginal production costs and marginal inventory benefits are both stationary and in which the cointegrating coefficient is the same as the value that characterizes the target inventory level in the cost function.     

A cost-effective approach to stochastic line balancing with off-line repairs

The layout of production facilities is an important determinant of the productivity potential of a manufacturing enterprise. It is particularly important in the design of assembly lines where the objective is to assign tasks to work stations in such a way as to minimize total variable production costs.Early approaches to the line balancing problem assumed known constant task times and sought a line layout which would produce the desired output with the fewest number of work stations, which is equivalent to minimizing idle time. Studies have shown that task times are random variables, therefore the cost of task incompletion must be considered a part of total production cost. Incompletion cost will be the cost of repairing or completing tasks which cannot be completed within the cycle time after the item has reached the end of the assembly line.This paper describes a methodology for designing approximately minimum cost paced assembly lines under conditions of random task times and off-line repair of incompleted tasks. Task times are assumed to be normally distributed random variables with known means and variances. The methodology consists of heuristically identifying a large number of feasible balances for each of which total costs are computed. The line design with the lowest total is retained as the “best.”In order to evaluate candidate line layouts, a total cost model is developed. Total cost is the sum of normal operating cost—which is simply a function of the number of work stations—and the cost of repairing products containing incompleted tasks. Because this latter cost is a random variable for a given balance, the expected value is used to evaluate a candidate layout. The cost associated with one or more workers exceeding the cycle time is the product of the probability of this happening and the expected cost of off-line repair.The heuristic method for generating feasible balances builds work stations from continually updated lists of precedence satisfying tasks. Qualifying tasks are added to the station as long as the probability of the station exceeding the cycle time remains below a pre-specified threshold. The methodology requires systematically varying this threshold to permit a lowest total cost solution to emerge. The process of generating a large number of balances for a particular threshold is efficient. Evaluating the total costs of the resulting balances takes the majority of the computational time.An experiment was conducted in order to compare the above cost-effective methodology with a purely deterministic approach and a commonly used industrial approximation method for dealing with task time variability. The experiment applied the three methods to four problems from the literature under a variety of repair cost and time variance conditions. In 21 of the 24 cases studied, the stochastic method produced a lower cost balance than the two alternatives. In the remaining 3 cases, the deterministic method also found the lowest cost balance. The stochastic method saved an average of 22.5 percent in total operating cost over the deterministic method and 8.4 percent over the industrial method.The experiment clearly showed the need to explicitly consider task time variability in arriving at a line balance. The stochastic approach of this paper offers large potential savings with no risk of obtaining a less desirable balance and so should be considered for implementation whenever there is a variation in task times. Even for large-scale problems, the computational cost is infinitesimal in the context of assembly line balancing, where very small improvements in productivity can mean substantial increments to profitability.     

Maintenance management: Keeping up with production's changing trends and technologies

Current trends in manufacturing have led to reduced levels of work-in-process inventory and increased levels of automation and machine complexity. These changes have two important implications for maintenance management. First, machine breakdowns not only cause a loss of output from the down machine, but also result in lost production on downstream machines. This occurs because of the elimination of buffer stock between workstations. Second, the gap between the technological expertise of the operators and the technological sophistication of their machines has widened. This gap decreases the ability of operators to make adjustments and minor repairs to their own machines.These two factors cause an increase in maintenance costs associated with machine downtime and maintenance labor. Historically, the proportion of preventive and corrective maintenance has attempted to balance these costs. As buffer stock is reduced and more machines are coupled, the costs of downtime will increase, increasing the need for preventive maintenance and decreasing its cost compared to the cost of lost production.Increases in the use of computer-based planning systems have resulted in the development of computerized preventive maintenance systems that have proven to be successful in planning for maintenance labor and materials. Integrating planning methods with corrective maintenance tasks will help reduce breakdowns, make more efficient use of maintenance craft labor, and provide information for inventory control of maintenance, repair, and operating (MRO) materials.This article examines methods of reducing machine downtime costs, maintenance labor costs, and MRO inventory costs in today's changing production environment. Techniques for reducing these costs are explored in the areas of maintenance planning, maintenance methods improvement, and MRO inventory control.     

Inventories and Endogenous Stackelberg Leadership in Two‐Period Cournot Oligopoly

Two‐period Cournot competition between n identical firms producing at constant marginal cost and able to store before selling has pure strategy Nash‐perfect equilibria, in which some firms store to exert endogenously a leadership over rivals. The number of firms storing balances market share gains, obtained by accumulating early the output, with losses in margin resulting from increased sales and higher operation costs. This number and the industry inventories are nonmonotonic in n. Concentration (HHI) and aggregate sales increase due to the strategic use of inventories.     

The effect of transaction costs, payment terms and power on the level of raw materials inventories

This paper proposes and tests an explanation for the level of raw materials inventories based on transaction cost economics theory and the role of power in a supply chain. According to this explanation, raw materials inventories are larger the higher a company's transaction costs and the lower its storage-related production and management costs. Factors that affect these costs are the company's vulnerability to opportunism, whether the input becomes more or less costly to store and manage as it moves through the supply chain, payment terms and the company's power in relation to its supplier. This explanation for the level of raw materials inventories was tested on a large sample of customer industries matched to their main supplier industries. Consistent with this theory, the empirical results show that companies hold larger raw materials inventories the more money their suppliers spend on research and development and the less important the customers are to their suppliers. These results are important because they indicate companies must consider a wider range of factors than previously thought necessary when establishing inventory policy.     

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.     

Random shocks in experimental spot and forward auction markets

Traders face random demand and supply schedules in two experimental auction environments. One is the standard double auction and the other requires sellers to produce and hold inventory before trading. Inventories cannot be held between trading cycles. The endogenous and random cost of inventory disciplines sellers to restrict sales and keep prices relatively high. Anxious buyers may bid up price because total sales cannot exceed inventories. Shocks to the system do not change this behavior except when seller production costs are random. In this treatment, prices converge to the predicted competitive equilibrium. The inventory requirement in all cases increases the earnings of sellers relative to buyers.     

基于3PL_Hub的电信运营商库存管理技术研究

随着电信运营商之间的竞争逐渐加剧,为取得竞争优势,电信运营商也已开始与供应商、物流企业合作,采用3PL_Hub模式进行库存管理,降低供应链成本。但因行业不同,电信运营商基于3PL_Hub的库存管理运作模式与技术都与传统制造业有所不同。为此作者研究了电信运营商基于3PL_HUB的协同物流组织与运作模式构建、基于3PL_HUB协同运作下补货决策技术及3PL_Hub协同下IT支撑系统构建等用以支承电信运营商基于3PL_Hub库存管理的关键技术,并在中国电信部分省公司的协同物流建设应用。     

The cost of inflated planned lead times in MRP systems

Much of the current literature in the field of production and inventory control systems stresses the need to revise traditional forms of thinking regarding production processes, the role of inventories for work in process, and the need for reduced lead times or flow times. Group technology, manufacturing cells, and other means of incorporating repetitive manufacturing techniques into traditional job-shop settings constitute the leading edge in system development.Still, there is resistance to these dramatic changes, and traditional “business as usual” methods still predominate. This study attempts to illustrate graphically the cost justification associated with reduction in lead times which generally results from these new concepts. In most job shops today, lead times are much longer than they need to be due to inflation of lead time estimates. Actual lead times for the manufacture of fabricated and assembled products have been shown to be a direct consequence of the planning lead times used in the MRP planning process—a form of self-fulfilling prophesy.The research employs a simulation model of a factory using MRP as a planning tool in a multiproduct, multilevel production environment. Manufacturing costs constitute the dependent variable in the experiments, defined as the sum of material costs (including expedite premiums), direct labor costs (including overtime premiums), inventory carrying costs, and overhead costs. The independent variable being manipulated is the planned lead time offset used in the MRP planning process. Twenty values of planned lead time are evaluated ranging from a value that includes no slack time at all (pure assembly line) up to a value that allows 95% slack (queue) time which, unfortunately, is not uncommon in many job shops today. Stochastic variables in the model include customer demand and actual processing times—the sum of set-up and run times.The result of the study is a cost curve formed over the range of independent lead time variables that is constructed using nonlinear regression techniques. The conclusions from the resultant graph clearly indicate the cost consequences of long lead times, with exponential cost increases beyond the 80–90% queue time level. Total costs are 41% higher at the maximum lead time allowance compared to the minimum. Clearly, this study demonstrates the need for lead time reduction, either through downward adjustment of MRP planned lead times or by introducing new manufacturing concepts.     

Rationing, defective inputs and Bayesian updates under central planning

The enterprise manager anticipates that he will be rationed in his input markets and is required to meet an output target. In order to avoid a penalty for missing the output target, he can purchase the inputs at an earlier time, when rationing is not in effect, but then he must incur an inventory cost. The inputs themselves are defective with a known mean rate but unknown variability; the manager has a prior density over this parameter. He can solve the relevant expected-cost minimisation problem in three ways: (a) by simultaneously determining the optimal amounts to be ordered on the two dates, (b) by dynamic programming, and (c) by dynamic programming combined with Bayesian learning. The paper investigates the properties of the optimal solution under the three scenarios with respect to variations in the defective rate, the level of uncertainty and the relative costs of inventories and of missing the target.We are indebted to the National Science, Olin and Guggenheim Foundations and the National Council on Soviet and East European Research for support. We are also indebted to Gregory C. Chow, J.-S. Pischke, and two referees for several insightful comments.     

Planning horizons for master production scheduling

Production and operations planning in organizations quite often is a multi-level sequential process, involving aggregate planning, master production scheduling, and detailed operations planning and scheduling. To obtain good planning results, it is desirable to have a proper planning horizon for each level of planning. There have been a considerable number of studies dealing with planning horizons for aggregate planning or production smoothing problems. There are also many planning horizon studies for single-item lot sizing problems. No study has addressed the issues associated with the planning horizons for master production schedules (which is a multi-item lot sizing problem in nature), particularly with respect to the relationship to the aggregate plan.This study addresses the issue of planning horizons for companies employing a make-to-stock competitive strategy facing a seasonal demand for their products. We formulate the aggregate planning problem and the master scheduling problem as two separate mathematical programs to approximate the two-stage process that typically takes place in practice. Rolling planning horizons are used to approximate the periodic updates of the plans commonly done in practice. The models also incorporate resource requirements planning concepts to estimate loads on the critical work centers.The planning process is simulated as a single pass procedure where the results of aggregate planning are passed to the master production scheduling model once per month and the results of the master scheduling model (i.e., the portion of the master schedule actually implemented) are passed back to the aggregate planning model for the next planning session.The experimental results show that when the planner faces extreme cost structures such as high smoothing costs/high setup costs or low smoothing costs/low setup costs, the planning horizon effects are reduced to a minimum. Master schedule planning horizons need not be as long as aggregate planning horizons. Alternatively, non-extreme cost structures such as high smoothing costs/low setup costs and low smoothing costs/high setup costs should be handled with equal planning horizons for both aggregate planning and master scheduling.It is also found that the firm's cost structure has an impact on the appropriate planning horizon for both aggregate planning and master scheduling. Some cost conditions allow for smaller master schedule horizons. The best horizon choice seems to be equal planning horizons for both aggregate planning and master scheduling, even though the cost savings is slight in some cases.Finally, the proper length of the planning horizon for master scheduling is affected by the planning horizon of the aggregate plans.     

Improving perishable product inventory management using goal programming

The multiple objectives of perishable product inventory management are examined in this paper. These objectives include: (1) satisfying demand by carrying sufficient inventories, (2) holding down inventory carrying costs, (3) keeping the amount of product spoilage (outdating) at an acceptable level, and (4) maintaining quality by using the product while it is still fresh, and (5) keeping the cost of rotation low. Some of the above objectives are in conflict. Thus, certain redistribution policies may help attain one or more of these objectives to a greater extent. Redistribution involves the transfer of the product from outlets where demand is low to outlets where demand is high. A goal programming model for solving redistribution problems is presented. An example is provided and sensitivity analysis is performed in order to determine when redistribution is advantageous. Applicability of the model is discussed.     

基于时间和成本的加工装配线物流平衡方法

本文提出一种基于时间和成本的加工装配线上物流的规划方法，在对现有的生产装配线重新分配工件的同时保证最大效益的改善，执行花费／效益分析，同时该平衡方法借助于数据库和电子表格的联合应用，可以在短时间内完成。文中以哈锅阀门股份有限公司生产线的改造为例，详细介绍了该方法的使用。计算结果表明，这种物流平衡方法提供了企业一种效率高，费用低的现有加工装配线改造方法。     

Improving health care means spending more time with patients and less time with inventory

In most hospitals and medical establishments, about 35 percent of their budgets are spent on supplies and labor to manage the inventories, material, and information flows; typically, these are managed as multiechelon systems. The importance of reducing inventory costs, among others, has become relevant in today's health care management. This research applies just-in-time concepts to health care inventories.     

Optimal maintenance center inventories for fault-tolerant repairable systems

In many military and commercial contexts, complex equipment which is expected to perform very reliably is often designed to be fault-tolerant, that is, able to function although some of the parts have failed. A popular fault-tolerant design is the m-out-of-n system, where there are n identical parts, at least m of which must be functional for machine operation. Complex equipment of this type often undergoes scheduled maintenance overhauls at regular intervals during which all failed components are replaced. Failure to have replacements on hand for failed parts requires emergency measures at premium cost. When repairable parts are highly reliable and expensive, both holding and shortage costs are high. A reasonable objective is to choose initial spares inventory to minimize the sum of holding costs and expected shortage costs.We first develop a model to determine the optimal repairable parts inventory for a maintenance center servicing machines containing a single m-out-of-n system. The model is then extended to handle a related problem, finding optimal maintenance center inventories for machines containing several m-out-of-n systems of different parts, minimizing total expected costs subject to a constraint on total inventory investment.We assume that there is a fleet of machines, which experience identical workloads. There is a cycle time of T days between overhauls for an individual machine. A machine arrives at the maintenance center for overhaul each day. At the overhaul, all failed parts are removed and sent to a repair shop, from which they eventually return to the maintenance center to be used again as spares. The total number of spares undergoing repair and on hand is a constant. There are no backorders; if the number on-hand spares is insufficient to meet demand at an overhaul, a shortage penalty is assessed which depends on the number and type of spares required.While computing holding costs is straightforward, computing expected shortage costs is more complex. Expected shortage costs are dependent upon several factors, including component failure rates, the values of m and n, part repair rates, and the initial number of spares on hand. We assume that the system of interest is well specified, so that the parameters of the model are known except for the number of initial spares of each type, which are the decision variables. We model the on-hand inventory of each type of part as a Markov chain with the number of spares on hand at the end of each day as the states, under the assumptions that failure rates are constant and repair times follow independent exponential distributions. We then calculate the steady-state probabilities of stockout of various numbers of spares, as a function of the initial spares inventory. The expected shortage costs for a given type of spare may then be calculated by finding the product of the penalty cost for lacking p spares and the probability of lacking p spares and summing over all possible p values.Solutions to the problem of finding optimal initial inventory level for a machine containing a single m-out-of-n system may be found easily by enumeration. Solutions to the constrained problem where the machine contains several independent m-out-of-n systems, may be found by dynamic programming. Sensitivity analysis of costs to changes in the inventory investment constraint is clear, and computational effort is reasonable. A simple example is included to illustrate the solution method for both problems.     

What inventories tell us about aggregate fluctuations—A tractable approach to (S,s) policies

We estimate a DSGE model with (S,s) inventory policies. We find that (i) taking inventories into account can significantly improve the empirical fit of DSGE models in matching the standard business-cycle moments (in addition to explaining inventory fluctuations); (ii) (S,s) inventory policies can significantly amplify aggregate output fluctuations, in contrast to the findings of the recent general-equilibrium inventory literature; and (iii) aggregate demand shocks become more important than technology shocks in explaining the business cycle once inventories are incorporated into the model. An independent contribution of our paper is that we develop a solution method for analytically solving (S,s) inventory policies in general equilibrium models with heterogeneous firms and a large aggregate state space, and we illustrate how standard log-linearization methods can be used to solve various versions of our inventory model, generate impulse response functions, and estimate the model׳s deep structural parameters.     

Staffing and equipment decisions for services: An experimental analysis

Many service-oriented firms face demand/work load variations that occur both within and between days, and seasonally during the year. These service firms tend to develop staffing plans based on peak periods, resulting in substantial idle time during low demand periods. To counter this, some firms hire part-time employees to work peak periods to improve productivity. In this way, the cost per customer/transaction (unit) can be reduced.However, the staffing decision is more complex than just determining the number and timing of the work force. The development of good staffing plans must consider the impact of available work stations like telephone consoles and data entry terminals. To use effectively more personnel during peak periods, the firm must have sufficient work stations to assign this work force. Effective staff scheduling must consider simultaneously the work load variations, employ capabilities, and equipment requirements. This article presents a model that incorporates these factors into the decision-making process and examines their interaction.The model analysis illustrates how uncertainty in work load forecasts and staffing flexibility influence expected cost performance and equipment requirements. A set of computer simulation experiments are conducted using operating data from the proof and encoding departments for Ohio National Bank and Chemical Bank. The results suggest that increased staffing flexibility reduces the needed equipment investment, since part time staff can be scheduled more conveniently to meet varying work loads. Also, basing capital equipment decisions on peak work load levels can substantially increase total operating costs. With these costs explicitly estimated, managers can weigh them against desired service level goals to determine the appropriate balance.     

Applying management science in developing countries: ABC analysis to plan public drug procurement.

Despite the vital role of pharmaceuticals in the prevention and treatment of major causes of death and disability in the developing world, high costs and frequent shortages remain chronic problems for drug supply programs. Yet, management techniques developed to optimize the use of scarce resources have had limited application in the settings of greatest need. An important determinant of the cost and supply of drugs is the procurement pattern. This study reviews procurement patterns in selected public supply programs and, using management science techniques, compares alternative procurement patterns in terms of inventory costs and shortages. Using drug cost and quantity estimates from two countries, a simulated ABC analysis was performed. This analysis showed drug inventories to be typical of industrial inventories: Over 80% of the consumption in dollars was accounted for by less than 20% of the drugs. Procurement patterns with more frequent purchasing or delivery of high usage drugs could reduce average inventories 20-50% over the commonly observed annual purchasing pattern. Sensitivity analysis of the results confirmed that variability in the delivery time and consumption pattern has a significant impact on the efficiency and economy of a procurement system. Closer supplier monitoring and better forecasting should reduce this variability.     

A new focus on inventory performance and bottom-line profits

This article is about a new way to measure inventory performance and about putting a dollar focus on inventory management priorities. It reviews the financial impact of inventories and shows how the information from any materiel requirements planning (MRP), MRP II, ERP, DRP, or orderpoint system can be used for setting individual objectives for inventory reduction, measuring performance by inventory segment, and monitoring continuous improvement. It will also consider the merits of paying performance bonuses to materiel planners and review some inventory reduction results.