Inaccuracies in calculated product costs have existed since the development of costing systems. A key contributor to the issue is the use of inappropriate bases for the application of overhead costs. This research proposes and provides preliminary evaluation in a virtual environment for a new allocation base that is believed to be better matched to the consumption rate of the indirect costs being allocated. Using a generalized manufacturing operational framework incorporating multi-period simulation, this research investigates the relationship between allocated cost categories and production or sales order activity. The existing cost allocation methods of full absorption and activity based costing (ABC) are used for comparison to the proposed method. Results show that at the aggregate reporting level (that is, income statement), the use of sales order or production order activity as an allocation base tracks closely with performance levels experienced using more traditional allocation bases. However, the results indicate that the impact on calculated product costs would influence decision making within a firm in terms of sales emphasis, mix, and markets in which to expand and from which to exit. This approach toward cost allocation would be equal to other Enterprise Resource Planning system based solutions in terms of simplicity of maintenance while offering product cost accuracies relatively equal with unit-level focused ABC systems, without requiring the substantial maintenance costs.

Many researchers have identified the negative impact that accounting methods have on reported profits as inventories are being rapidly reduced. This research explores the magnitude and duration of the negative impact on reported profits experienced during a lean manufacturing implementation.The effect on reported profit is evaluated under five accounting methods (full absorption costing, activity-based costing, direct costing, throughput costing, and order activity costing) and three levels of inventory reduction rate. The findings reported here indicate that the period-by-period gains in operational efficiency, resulting from process improvements brought by a lean program, will not counteract the negative impact from the accounting system on the income statement while inventories continue to be reduced. This could lead to the early termination of a lean program that is, in fact, bringing operational improvement in the present time, but the improvement is being erased by poor inventory control practices from past periods.This research uses a multi-period simulation model of a production operation that incorporates a manufacturing planning and inventory tracking system. A hybrid simulation approach is employed using Microsoft((R)) Excel to model the Manufacturing Resource Planning (MRPII) function, while ProModel simulation software is used for the development and operation of the model production environment. Microsoft((R)) Visual Basic((R)) is used to create a bridge between systems for schedule dissemination and inventory updates. The integrated computer simulation modeling approach developed to conduct this research is novel in the sense that multi-period simulation, incorporating MRP, has not been widely used based on available literature.

The effect Lean Manufacturing programs have on profit and loss statements during the early months of their implementation often causes them to be viewed as failures. The length of time it will take traditional financial reports to reflect lean manufacturing improvements depends upon how poorly the operation was doing in terms of inventory managemen

The onset of global competition in the 1970s began to change the manufacturing environment drastically. The advent of the computer created an opportunity for the developers of material requirements planning (MRP) concepts to automate many of the manual practices employed in manufacturing for acquiring and tracking of materials. This resulted in more efficient manufacturing operations in terms of labor for planning activities and better material control. Technology was expanded to include capacity planning and production schedule control. The acceptance in industry for the new “tool” was monumental and soon a new industry was born. For some time MRP allowed manufacturers to perform at higher levels of proficiency. However, the demands and expectations of the customers have continued to change and manufacturers wishing to keep pace with competition are beginning to question if MRP is still a valid tool for production planning and control. As a result new concepts have begun to emerge. These new approaches call for an abandonment of some of the foundational components of MRP.

Click to increase image sizeClick to decrease image sizeKey Words: Average run lengthMultiple stream processesStatistical process control Additional informationNotes on contributorsScott D. GrimshawIs an assistant professor in the Department of Statistics at Brigham Young University. His research develops and evaluates statistical methods for applications that generate large data sets. He is a member of ASQ and ASA.G. Rex BryceIs currently chair of the Department of Statistics at Brigham Young University. He has worked in industry directly and as a consultant since 1963. His research interests are in the application of statistical science to quality and productivity measurement and improvement. Professor Bryce is a member of ASQ and a Fellow of the ASA.David J. MeadeIs a senior statistician at Advanced Micro Devices. He serves as an internal statistical consultant supporting the use of designed experiments, statistical process control, design of custom statistical software, and analysis of manufacturing data. He is a member of ASA.