Bringing Business into Sociotechnical Theory and Practice

Current developments in the sociotechnical framework address a number of vital issues that failed to occupy the high ground in its formative years. These include: (i) the purpose of the systems, to create customer value under social and resource constraints, (ii) the context or external business environment, and (iii) the dynamics of the sociotechnical system. Due attention to the dominant issues of purpose, context, and their dynamics make it more meaningful to speak of sociotechnical business systems (STBS) and organizational learning instead of sociotechnical systems (STS) and individual learning. In STBS each unit in an organization has business responsibilities and goal-based couplings with its environment and focuses on the creation of customer value. The implications of this shift from STS to STBS are discussed and illustrated with current research and two summary case studies.     

LOAD CUBING AT R. J. REYNOLDS

R. J. Reynolds Tobacco USA (RJR) is currently implementing a microcomputer-based decision support system to computerize and optimize the selection of patterns for loading cases of finished product into truck trailers at RJR's Central Distribution Center. This system allows for the efficient loading of trucks with less supervision. Total annual savings from reduced personnel and shipping costs is approximately $850,000. In addition to these benefits, the system is a stepping stone for trailer loading automation and the integration of a comprehensive load planning system.     

A MODEL FOR SWITCHING DISPATCHING RULES IN REAL TIME IN A FLEXIBLE MANUFACTURING CELL

We model choice of dispatching rules in real time (system state dependent) as a pattern recognition problem, using a modified version of Data Envelopment Analysis. A data base of system state and performance values is created from extensive simulation, and this data base is used to train the pattern-recognition model. Our results show that the model is very effective in choosing a mix of dispatching rules over a period of time, varying the mix with system objectives, and performing better than the strategy of using fixed rules. We show how “If-Then” decision rules can be created from the model and portrayed in a decision-tree-like diagram. Since such decision rules are based on rigorous mathematical foundations, optimization will be ensured in our approach.     

PRODUCTION FLOW CONTROL FOR A MANUFACTURING SYSTEM WITH FLEXIBLE ROUTINGS

We study the dynamic routing problem for a flexible manufacturing system consisting of two unreliable machines and a finite buffer. One product-type is produced which requires two operations in sequence. The demand rate is assumed to be constant. Each machine is capable of performing both operations. The objective is to trace the demand while keeping the work-in-process low and the cycle-time short. An optimal control formulation is established for the dynamic routing problem. A production flow control algorithm is developed based on a combination of mathematical modeling and heuristics. The control policy is simulated and a comparison with the numerical optimal solution shows that it performs well for the instances under consideration.     

PRODUCTION SMOOTHING UNDER ENVIRONMENTAL CONSTRAINTS

This is an investigation into how optimal production rates and optimal price levels react to the introduction of an environmental tax on emissions. While, in the case of perfect competition, a linear tax has no effect, I show that in the monopolistic case the optimal production and emissions rates decrease in all instances-without an additional smoothing effect. The desirable effect, emissions peaks being cut off, is only achieved when a progressive tax is imposed.     

QUANTIFYING THE IMPACT OF INVENTORY HOLDING COST AND REACTIVE CAPACITY ON AN APPAREL MANUFACTURER'S PROFITABILITY

This paper was motivated by the operational problems faced by Northco, a school uniform manufacturer in the Northeastern United States. Northco was facing high working capital costs while also incurring high stockout and markdown costs. This paper models the impact of inventory holding cost and reactive capacity on Northco's targeted understocking and overstocking cost and offers a solution methodology for such problems. We quantify the impact of varying inventory carrying costs (and hence, high working capital costs) on stockout costs and the value of additional capacity. Our results illustrate that apparel manufacturers with high working capital costs, and hence high inventory carrying costs, should target higher stockout costs and achieve lower capacity utilization. The results presented have application beyond Northco because high working capital cost is endemic to many supply chains.     

A MODEL FOR BATCH ADVANCED AVAILABLE‐TO‐PROMISE

The available‐to‐promise (atp) function is becoming increasingly important in supply chain management since it directly links production resources with customer orders. In this paper, a mixed integer programming (mip) ATP model is presented. This model can provide an order‐promising and ‐fulfillment solution for a batch of orders that arrive within a predefined batching interval. A variety of constraints, such as raw material availability, production capacity, material compatibility, and customer preferences, are considered. Simulation experiments using the model investigate the sensitivity of supply chain performance to changes in certain parameters, such as batching interval size and customer order flexibility.     

DYNAMIC FEEDING IN A STOCHASTIC PARALLEL PROCESSING SYSTEM

I consider a dynamic input scheduling problem of a stochastic parallel processing system consisting of n identical flexible machining cells. The processing times at each cell are independent random variables. Previous study has indicated the NP complexity of the problem. In this paper, I prove the separability under an ideal just-in-time input condition. Using the separability, I then construct an approximation procedure for most realistic applications where the separability condition is violated. The approximation procedure requires only linear time and performed quite well on an extensive test with numerical examples.     

OPTIMALITY OF STATE-DEPENDENT (s,S) POLICIES IN INVENTORY MODELS WITH MARKOV-MODULATED DEMAND AND LOST SALES

Markov-modulated processes have been used in stochastic inventory models with setup costs for modeling demand under the influence of uncertain environmental factors, such as fluctuating economic and market conditions. The analyses of these models have been carried out in the literature only under the assumption that unsatisfied demand is fully backlogged. The lost sales situation occurs in many retail establishments such as department stores and supermarkets. We use the analysis of the Markovian demand model with backlogging to analyze the lost sales case; in particular, we establish the optimality of an (s, S)-type policy under fairly general conditions.     

FREQUENCY OF REPLANNING IN A ROLLING HORIZON MASTER PRODUCTION SCHEDULE FOR A PROCESS INDUSTRY ENVIRONMENT: A CASE STUDY

This study addresses the problem of replanning frequency for a rolling horizon master production schedule (MPS) in a process industry environment under demand certainty. The major contribution of this paper is the demonstration of how the appropriate replanning frequency for a MPS can be determined under the conditions of minimum batch-size production restrictions in a rolling planning horizon setting. In addition, the problem environment for this study is an actual MPS operation that includes features such as multiple production lines, multiple products, capacity constraints, minimum inventory requirements, and multiple goals. Actual data from a paint company are used to determine the appropriate replanning frequency for a rolling horizon MPS. Results indicate that a 2-month replanning frequency was the best at this firm because of the significant cost savings it provided when compared to actual company performance and the other replanning intervals.