Lee J. Krajewski

Improving performance using workforce flexibility in an assembly environment with learning and forgetting effects

Competitive pressures for assemble-to-order products and products with shorter life cycles have driven the need for more flexibility in assembly operations. This paper explores the use of workforce flexibility as a means for improving the performance of assembly operations. Two decisions that immediately affect the performance of assembly operations are the amount of cross training to give to each worker and the dynamic deployment policies for assigning workers to tasks. In environments where learning and forgetting are present, these decisions are interrelated. The effectiveness of these two decisions is a function of the product line profiles of the assembled products. We define the product line profile using two factors: product variety and task complexity. Using a simulation model of an assembly line and an experimental design that incorporates product variety and task complexity, we show that as task complexity increases, deployment should be restricted while only low cross training is needed, and as...

A decision model for corrective maintenance management

Proper management of maintenance offers many companies significant potential for improving productivity and profitability. Traditional management thinking regards maintenance costs as accidental, rather than planned and controllable. Additionally, research in maintenance management has focused on preventive maintenance and has ignored corrective maintenance even though the latter is also considered to be a critical activity in industry. This study proposes a decision model that could assist in a comparative evaluation of alternative corrective maintenance policies. This decision model consists of a simulation model and economic analysis. The simulation model predicts inventory costs and delivery performance of a corrective maintenance policy in various production systems. Based on simulation results, an economic analysis, consisting of a net present value model and breakeven models, determines the economic value of alternative maintenance policies. A detailed example is offered to evaluate two particular ...

Planning horizons for master production scheduling

Abstract 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 firms 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.

The effects of environmental factors on the design of master production scheduling systems

Abstract In uncertain environments, firms often use a rolling schedule to implement a master production schedule (MPS). The rolling schedule involves replanning the MPS periodically and freezing a portion of the MPS in each planning cycle. Two important decisions which have a significant impact on the cost performance of a rolling schedule are the choice of a replanning interval ( R ), which determines the replanning frequency of the MPS, and the selection of a frozen interval ( F ), which represents the number of periods the MPS is frozen in each planning cycle. The determination of the appropriate F and R values has been an important research issue, especially in an uncertain environment. This paper examines the effects of environmental factors such as cost structure, bill of material (BOM) structure, cumulative lead time, magnitude of MPS change costs, and the magnitude of forecast error on the choice of F and R for a single end item in an uncertain environment where a rolling schedule is used. Results of our investigation show that the choice of F affects total system costs per period (TC) but the effects of the choice of R on TC is situational. In addition, the analysis indicates that the magnitude of the MPS change costs, the BOM structure, and the cumulative lead time of the product are important considerations in the design of MPS systems. Results also show that the magnitude of the forecast errors and the cost structure play relatively minor roles in the choice of F and R . We also show that even when MPS change costs are high, greater freeze intervals and more frequent replanning may be cost effective.

Comparative Evaluation Of Three Interactive Multiobjective Programming Techniques As Group Decision Suppport Tools

AbstractThe difficulty involved in finding a compromise solution to a multiobjective problem increases drastically when the participation of multiple decision makers becomes necessary. This is primarily due to the unique preference structures of the decision makers whose individual judgments of the ‘best compromise’ rarely coincide. In spite of the difficulty associated with such multiple-objective, multiple decision maker problems, nominal and/or interacting groups have been foimd to improve overall decision making effectiveness.This paper compares the performance of thlree interactive multiobjective programming procedures in determining a compromise solution to a group decision problem. Three multiobjective programming techniques originally designed for a single decision maker are augmented with a practical preference aggregation component in order to capture the preferences of several individual decision makers.Analysis of variance was used to study data collected through a repeated measures design in ...

Period batch control in group technology

Abstract Group technology (GT) offers many advantages to firms engaged in batch manufacturing, including lower setup times, reduced lot sizes, lower lead times, and easier production planning and control. Period Batch Control (PBC) has been proposed in the literature as a simple production planning and control system for a GT environment, based on the choice of a cycle length. Unfortunately, there are no published studies providing guidelines for the choice of an optimal cycle length. In this paper we develop a cost minimization model for examining the choice of cycle length, and demonstrate its use by applying it to four published data sets. The analysis shows that the cell design itself directly affects total inventory and overtime costs and the choice of cycle length; that demand increases can be very expensive for a given cell design; and that the degree of demand uncertainty is an important factor in PBC design. The key managerial consideration is the amount of slack capacity to build into the cell d...

Interfacing aggregate plans and master production schedules via a rolling horizon feedback procedure

Aggregate planning and master scheduling are two important levels of a hierarchical production planning process. They serve as the front end of production and operations planning and control systems. It is imperative to have the front end well planned and coordinated. In this study, we demonstrate that a rolling horizon feedback procedure can be an effective coordination mechanism for interfacing aggregate planning and master scheduling. Each time the schedule is rolled ahead, the feedback of performance results from master scheduling provides the aggregate planning subsystem useful information for improving its planning activities. Our experiments show that there are at least three factors that make a rolling horizon procedure attractive: (i) the use of a horizon with a fractional portion of a seasonal cycle, (ii) high smoothing costs, and (iii) high setup costs. Using rolling horizons reduces the total aggregate costs in these situations relative to not using rolling horizons. The potential impacts of the rolling-horizon strategy are greater under some circumstances than others.

An integrated approach for manpower planning in the service sector

The manpower planning problem receives considerable attention by the management of service organizations because they are typically labor intensive. Not only must optimal staff sizes be determined over an extended horizon, but optimal staff schedules which assign labor to tours of duty and specific tasks, given the available staff size, must also be found. Adding to the complexity arising from the interrelatedness of the staff-sizing and staff-scheduling problems is the existence of the conflicting multiple objectives of minimizing costs and maximizing customer service. The few methodologies reported in the literature which treat the complete manpower planning problem as stated above have several shortcomings. Some techniques place a severe strain on computational capabilities. Also, these methods confound the specification of the goal levels with the analysis of the relative goal weightings. Often the specification of the desired goal levels is made at a different level in the organizational hierarchy than the specification of the relative importance of the various goals. This paper suggests an approach which overcomes these shortcomings. The methodology utilizes a simulation model with an imbedded heuristic procedure for the staff-scheduling problem to identify realistic aggregate staff-size goals to be used in a multiple-objective staff-sizing model. The methodology is applied to the manpower planning problem of a large sectional center post office and the managerial benefits are discussed.

A heuristic algorithm for managing inventory in a multi-echelon environment

Abstract The proper management of finished goods inventory in a multi-echelon environment is an extremely difficult problem to solve. Optimization approaches for solving this problem are intractable, and currently available heuristic techniques have serious deficiencies. Pull systems and independent demand based push systems do not adequately deal with the lumpy demand caused by the dependent relationships of stocking locations in a multi-echelon environment. Although distribution requirements planning (DRP) can be modified to handle uncertainties by adding safety stock, the process of deriving demands at lower echelons implicitly assumes deterministic conditions. In addition, no heuristic method directly considers the capacity of transportation and storage resources, or includes transportation costs. The incorporation of these additional complexities is left to the discretion of management. This study introduces a new heuristic algorithm that addresses these additional complexities. The algorithm is an improvement heuristic that can be implemented as an add-on module to a DRP system. At the core of this heuristic are two search routines ( Savings and Change ) for improving an initial solution determined by the DRP explosion process. We demonstrate this heuristic algorithm with two simple problems to provide some insight concerning its operation. The heuristic algorithm is then tested against an alternative rolling horizon mixed-integer linear programming (MILP) procedure that solves each linkage between locations optimally for each planning horizon. The example scenario used in this research consists of four distribution centers ordering from two regional warehouses, which in turn order from a central warehouse. Computer simulation is used to compare the two alternative procedures with rolling horizons and demand uncertainty. The performance of the procedures is compared for total costs, customer service, and the number of orders placed by locations within the control of the model. Multivariate analysis of variance (MANOVA) techniques are used to analyze these performance measures. The experimental results suggest that the heuristic algorithm performs extremely well when compared to the MILP based procedure. Demand uncertainty is found to have a significant effect on customer service performance, but safety stock can be added to distribution centers in actual applications to control this situation. In addition, a qualitative comparison between the MILP approach and the heuristic algorithm in this study suggests that the introduction of demand uncertainty has the effect of reducing the experimental differences between the two techniques. This result suggests that the heuristic algorithm presented in this research works best (relative to the MILP approach) in the actual environments for which it is intended. This study is of considerable value to managers concerned with the management of finished goods in a multi-echelon environment. It represents an initial step toward the development of a heuristic algorithm that incorporates additional real-world complexities and is tractable for realistically large problems. The findings from this study provide encouragement that similarly designed heuristics can be implemented within multi-echelon inventory control systems in the future.

The effects of workforce strategies on manufacturing operations

Abstract This paper tests the effects of three workforce strategies on a multistage, multiproduct manufacturing system under various operating conditions. These three strategies are those that are predominant in todays world economics. One type is a chase strategy, often used by firms that employ low skilled workers and faced with seasonal product demands, where workforce levels fluctuate according to increases and decreases in production requirements. A level-flexible strategy, commonly called the Toyota system, keeps the aggregate workforce at a constant size but by having flexibly trained workers it can allow transfers of workers between various departments and processes as production requirements dictate. The third major type is a level-inflexible strategy, such as that used on mass-assembly lines employed by American automobile manufacturers. Under this strategy, the number of workers remains constant in each department as well as at the aggregate level regardless of short term changes in the production requirements. The manufacturing system is envisioned as a sequential planning process with interrelated decisions made at the levels of aggregate planning, master production scheduling, and departmental planning. This process is modeled as a zero-one mixed integer program. The operating conditions under which the strategies are tested are the variability of demand, the level of service, and the degree of inventory investment. The strategies are statistically tested as to their effects on five criteria: average weekly workforce size, average quarterly inventory investment, average weekly overtime, total setups and the average weekly ratio of departmental load to capacity. Using four different products and an experimental manufacturing environment described within the paper, we tested for any statistical differences between the three strategies. We found that none of the strategies had significantly different workforce sizes. Also, the chase strategy had the smallest average quarterly inventory investment. This implies that the “Japanese” level-inflexible strategy does not have the smallest workforce or lowest inventory as claimed by some. We also tested the effects of the operating conditions on the workforce strategies. The level-flexible strategy was most insulated from the seasonality, inventory restrictions, and service level. Thus again the claims made for the level-inflexible strategy about being insulated from its environment were not substantiated. It was found that the seasonality of demand had the greatest impact on the three workforce strategies. Also, the level of service greatly affected the utilization of labor resources. The major overall conclusion is that the level-flexible strategy which is associated with some Japanese manufacturing firms does not achieve the claims that some of its adherents have made for it. It does not have the smallest workforce or the lowest inventory nor is it best insulated from its environment. Furthermore, the seasonality of demand and the level of service do effect this strategy.