The cost of inflated planned lead times in MRP systems

Abstract

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.

Optimizing planned lead times for enhancing performance of MRP systems

“…Although (B) and (C) did not incur any backorder costs in the Ϯϱ plentiful capacity scenarios, their inventory costs are significantly higher compared to the proposed method and the result is a significantly higher total cost. These findings are consistent with past studies (Enns, 2001;John, 1985).…”

“…Based on a stochastic study of MRP systems, Buzacott and Shanthikumar (1994) suggest that safety lead time improves performance when forecasts over the operational lead times are accurate. However, inflation of PLT gives rise to longer actual lead times, congestion, and forecast inaccuracy, thereby affecting the inventory and service performance (John, 1985;Vollmann et al, 1992). On the other hand, lower PLTs present the risks of unstable work load and missed orders or back orders (Billington et al, 1983;Teo et al, 2011).…”

Models for production planning under uncertainty: A review

“…Their simulation modelling approach studies the impact on the total cost of four factors: lead time variability, safety stock level, safety lead times, and lot-sizing rules on total cost. John (1985) discusses the cost of inflated lead times on the operation of a MRP system. The author uses a simulation model with several stochastic parameters for customer demand and Rinks (1981Rinks ( , 1982a 2 DU, 3 SU Aggregate planning Expert system using fuzzy linguistic parameters Turksen (1988a, b) Ward et al (1992 Fuzzy logic controller based on Rinks' model Gen et al (1992) Fuzzy multi-objective linear programming Wang and Fang (2001) Fuzzy linear programming Lee et al (1990Lee et al ( , 1991 lead times.…”

A Risk Analysis Model in Concurrent Engineering Product Development

“…Embedded in the integrated FMEA‐GERT models, our simulation models are different from those widely used in manufacturing systems. For example, John et al and Sridharan ( 20‐22 ) developed simulation modeling approaches in a manufacturing resource planning (MRP) system to study the impact on the total cost factors such as lead time variability, safety stock level, safety lead times, and lot‐sizing rules on total cost. Huang et al ( 23,24 ) developed the Q‐GERT simulation model that integrated MRP with plant control in a production process.…”

“…PDM is an advanced database technology to support the CE environment. ( 20 ) PDM provides functions for early warning and monitoring functions. It supports different networks and databases and uses data mining to infer risks.…”