Shailesh S. Kulkarni

Agility in manufacturing systems: an exploratory modeling framework and simulation

It has been widely recognized that agility enables manufacturing systems to respond to dynamic and unpredictable changes in today’s competitive environment. Develops a quantitative analysis framework and a simulation methodology to explore the value of agility in financial terms. Addresses the issues pertaining to the assessment of how an agile system performs in an environment of unanticipated changes, the comparison between two or more systems with different designs and hence different agility levels and the justification of investments in agility. Proposes an exploratory framework for a structured analysis of the various segments of the manufacturing system in which agility at different levels is built‐in through different pathways and links it to a set of aggregate performance measures. Then develops a simulation model that captures dynamic and unanticipated changes in the operating environment and facilitates performance appraisal and investment justification decisions using a quantitative financial metric.

Production economics and process quality: A Taguchi perspective

Abstract Although several studies have analyzed the interaction between the economics of production and process quality, most of them view quality from a very traditional perspective – reject when outside specified limits, or else accept. Recent views on quality have shown that such a definition greatly underestimates the costs of poor quality and leads to sub-optimal decisions. The primary intent in this paper is to revisit this interaction of the economics of production with process quality from a non-traditional yet more realistic “Taguchi” quality cost perspective. Specifically, we investigate the possibility of investing in a process to decrease its variance. Although such an investment reduces the proportion of defects, and when large enough, the Taguchis loss, it also increases the cost of holding inventory. Our model determines the optimal levels of inventory, and the production lot-size that minimizes the sum of inventory and quality-related costs.

The Use of Latent Semantic Analysis in Operations Management Research

In this article, we introduce the use of Latent Semantic Analysis (LSA) as a technique for uncovering the intellectual structure of a discipline. LSA is an emerging quantitative method for content analysis that combines rigorous statistical techniques and scholarly judgment as it proceeds to extract and decipher key latent factors. We provide a stepwise explanation and illustration for implementing LSA. To demonstrate LSAs ability to uncover the intellectual structure of a discipline, we present a study of the field of Operations Management. We also discuss a number of potential applications of LSA to show how it can be used in empirical Operations Management research, specifically in areas that can benefit from analyzing large volumes of unstructured textual data.

Process investment and loss functions: Models and analysis

Abstract Numerous authors explore the relationship between investment in a production process to reduce its variance and improve quality levels. Recent studies also looked at reduction of process variance from a non-traditional Taguchi quality cost perspective. In this paper we propose and compare an alternative to Taguchi variance reduction models. We study the application of a modified form of the Reflected Normal loss function for optimal process investment/variance reduction decisions. We develop analytical models and investigate structural properties of the models. We derive optimal investment levels for reduction in process variance and provide several insights. We also establish guidelines for practitioners to choose amongst the two loss functions when considering investing in process improvement.

On the trade-offs between risk pooling and logistics costs in a multi-plant network with commonality

This paper examines the benefits and costs of two alternative manufacturing network configurations in the presence of component commonality. We evaluate the trade-off between the decreased logistics costs and loss of risk-pooling benefits in plant networks which spread component manufacturing over each plant (product network) as compared to those that consolidate component manufacturing in a single plant (process network). We examine for conditions that mean that a product network would be chosen instead of a process network and vice-versa. We find that the risk-pooling benefit obtained by consolidating common subassembly production is reduced when the cost of acquiring common component capacity is sufficiently high or low. A post-optimality sensitivity analysis for the process network provides insights into subtle substitution effects, which are a direct outcome of cost mix differentials and network structure and complementarity effects, which are induced by the considered sequential assembly system. Our results suggest that the impact of operational cost parameters on strategic decisions can often be non-intuitive. Overall, our analysis provides a link between strategic and operational decision-making in supply chain management, in the context of multi-plant configuration.This paper examines the benefits and costs of two alternative manufacturing network configurations in the presence of component commonality. We evaluate the trade-off between the decreased logistics costs and loss of risk-pooling benefits in plant networks which spread component manufacturing over each plant (product network) as compared to those that consolidate component manufacturing in a single plant (process network). We examine for conditions that mean that a product network would be chosen instead of a process network and vice-versa. We find that the risk-pooling benefit obtained by consolidating common subassembly production is reduced when the cost of acquiring common component capacity is sufficiently high or low. A post-optimality sensitivity analysis for the process network provides insights into subtle substitution effects, which are a direct outcome of cost mix differentials and network structure and complementarity effects, which are induced by the considered sequential assembly system. Our...

Loss-based quality costs and inventory planning : General models and insights

In this paper, we examine a joint lot-sizing and process investment problem with random yield and backorders. We allow for inspection and develop stochastic models which provide the optimal inspection and lot-sizing policy as well as the optimal process investment for variance reduction. The process quality loss profile around the target is captured via a modification of the Reflected Normal loss function. We conduct numerical experiments assuming that the proportion of defectives follows a Uniform distribution while the process quality characteristic follows either a Normal or Uniform distribution. We also develop closed-form solutions that depend on at most the first two moments of any general probability distribution of defective units and which allow us to examine the nature of optimal policies. We demonstrate via numerical experiments the value of our integrated approach for jointly determining optimal inventory, inspection, and investment policies. Overall, our models and analyses provide some interesting insights into this reasonably complex inventory-quality problem and open up several avenues for future work in this area.

Maintenance-outsourcing contracts for a system with backup machines

In this paper, we consider a system with multiple components, each prone to failure, during which production is halted. Minimal repair is performed by an external contractor whenever a component breaks down. The contractor also conducts a general preventive maintenance (PM) for the whole system at pre-determined times. The contractor’s goal is to minimise maintenance-related costs; however, the system (made up of the contractor and the manufacturer, who gains revenue whenever the system is up) profit would be maximised if the revenue is also considered. Since these goals usually require different PM schedules, we propose a cost subsidisation scheme which coordinates the system. We then extend this basic model by considering the existence of a backup machine which will allow the system to continue running (albeit, generating a lower revenue) whenever a component fails. We show that the existence of such a machine reduces the profit difference between uncoordinated and coordinated systems.

The impact of uncertain yield on capacity acquisition in process plant networks

In this paper, we consider the case of process plant networks, which consolidate production of components. It is well known that such networks take advantage of process commonalities thereby accruing economies of scale advantages. It has also been shown in recent work that besides scale economies, process plant networks also enjoy risk-pooling advantages which are a direct outcome of network structure. In this research, motivated by real-world examples, we study capacity acquisition in a process plant network when demand and process yield are both uncertain. We show that the optimal capacity acquired in such situations may be considerably higher or lower than that acquired when yield is assumed perfect. We develop distribution-free policies for optimal capacity acquisition. We illustrate our results by means of a numerical example and study the sensitivity to important model parameters. For specific distributional assumptions we also provide closed-form results for the optimal capacity acquisition. We consider our work as perhaps the first to bridge the gap between operational level quality control studies and strategic level network configuration research. Overall, we believe that this paper opens up several avenues for future research integrating these two important areas.

Capacity investment and the value of operational flexibility in manufacturing systems with product blending

A distinct feature of process industries such as food, chemical and consumer packaged goods is the blending of intermediates into finished goods. In the context of such manufacturing systems the levels of different inputs that can be blended to process a final good define the range of flexibility. Likewise, the cost for using (blending) different inputs defines the mobility element of flexibility. In this paper, we investigate capacity investment and the value of flexibility in the presence of such product blending constraints. We are motivated by recent case studies of food manufacturers, in particular, those manufacturers that seek to increase flexibility via blending of intermediates. We analyse stochastic programs under demand uncertainty of such manufacturing systems. We provide analytical insights into trade-offs when range and mobility are interdependent. Our analytical work gives structural insights into subtle complementarity and substitution effects between dedicated and shared resources in the presence of blending. We analytically show that there is a degradation in the cost performance of such systems with an increase in correlation. We characterise the optimal blending fraction that balances the benefits of higher range with higher costs (lower mobility). Our numerical work shows that a moderate level of blending can significantly improve flexibility and that well-known guidelines for designing limited flexibility change in the presence of blending. For example, blending, even if optimally designed, weakens the appeal of chaining configurations. Overall our work guides resource configuration in industries where product blending is an integral part of the production process.

Optimal ordering decisions under two returns policies

To avoid stockouts and maintain product availability, retailers typically carry excess units and subsequently incur higher cost. In case of style/fashion goods, demand forecasting is extremely difficult due to short selling cycles. The purpose of this study was to minimise the cost of excess stocking without compromising product availability. To achieve these conflicting objectives, our study includes two ordering instances and two returns policies. The time between orders subsequently helps resolve demand uncertainty. Existing studies consider only one type of returns policy, that is, returns on the entire purchase quantity; whereas our study considers two types of returns policies: returns on the first order size and returns on the entire purchase quantity. This study also includes models for the retailer and the supply chain system. Analytical and numerical insights into our study enable the retailer to select his appropriate returns policies to maximise his as well as system’s expected profits. We also show that perfect coordination of partners will help them improve their profits considerably.