Manufacturing Practices and Strategy Integration: Effects on Cost Efficiency,Flexibility, and Market‐Based Performance

Manufacturing plant managers have sought performance improvements through implementing best practices discussed in World Class Manufacturing literature. However, our collective understanding of linkages between practices and performance remains incomplete. This study seeks a more complete theory, advancing the idea that strategy integration and enhanced manufacturing capabilities such as cost efficiency and flexibility serve as intermediaries by which practices affect performance. Hypotheses related to this thesis are tested using data from 57 North American manufacturing plants that are past winners and finalists in Industry Week's“America's Best” competition ( Drickhamer, 2001 ). The results suggest that strategy integration plays a strong, central role in the creation of manufacturing cost efficiency and new product flexibility capabilities. Furthermore, strategy integration moderates the influences of product‐process development, supplier relationship management, workforce development, just‐in‐time flow, and process quality management practices on certain manufacturing capabilities. In turn, manufacturing cost efficiency and new product flexibility capabilities mediate the influence of strategy integration on market‐based performance. These findings have implications for practice and for future research.     

Competing on Time: An Integrated Framework to Optimize Dynamic Time‐to‐Market and Production Decisions

This study develops a comprehensive framework to optimize new product introduction timing and subsequent production decisions faced by a component supplier. Prior to market entry, the supplier performs process design activities, which improve manufacturing yield and the chances of getting qualified for the customer's product. However, a long delay in market entry allows competitors to enter the market and pass the customer's qualification process before the supplier, reducing the supplier's share of the customer's business. After entering the market and if qualified, the supplier also needs to decide how much to produce for a finite planning horizon by considering several factors such as manufacturing yield and stochastic demand, both of which depend on the earlier time‐to‐market decision. To capture this dependency, we develop a sequential, nested, two‐stage decision framework to optimize the time‐to‐market and production decisions in relation to each other. We show that the supplier's optimal market entry and qualification timing decision need to be revised in real time based on the number of qualified competitors at the time of market‐entry decision. We establish the optimality of a threshold policy. Following this policy, at the beginning of each decision epoch, the supplier should optimally stop preparing for qualification and decide whether to enter the market if her order among qualified competitors exceeds a predetermined threshold. We also prove that the supplier's optimal production policy is a state‐dependent, base‐stock policy, which depends on the time‐to‐market and qualification decisions. The proposed framework also enables a firm to quantify how market conditions (such as price and competitor entry behavior) and operating conditions (such as the rate of learning and inventory/production‐related costs) affect time‐to‐market strategy and post‐entry production decisions.     

The Impact of Organizational Culture on Time‐Based Manufacturing and Performance

Firms are utilizing an array of manufacturing practices in their quest for survival and success in the marketplace. The implementation of those practices has not always resulted in success stories as the focus had been mostly on technical issues, with little concern for “soft issues.” For example, the enabling role of organizational culture has often been ignored. Using Schein's conceptualization of culture as underlying assumptions, espoused values, and artifacts, we examine a framework that relates culture and manufacturing practices to performance. The underlying assumption of customer orientation is posited to affect espoused values such as beliefs on investing in facilities and equipment to leverage intellectual work and to promote creativity, beliefs on working with others, beliefs on making decisions that are global, beliefs on management control, and beliefs on integrating with suppliers. The espoused values are hypothesized to affect visible attributes of culture (behaviors) such as time‐based manufacturing practices, which firms are employing for competitive advantage. A sample of 224 firms is used for developing research instruments and testing the hypothesized relationships advanced. Results indicate that high levels of customer orientation lead to a set of managerial beliefs that are collaborative and integrative. In turn, certain espoused values support a high level of time‐based manufacturing practice, which leads to high performance.     

Investment in Facility Changeover Flexibility for Early Entry into High‐Tech Markets

A model is introduced to analyze the manufacturing‐marketing interface for a firm in a high‐tech industry that produces a series of high‐volume products with short product life cycles on a single facility. The one‐time strategic decision regarding the firm's investment in changeover flexibility establishes the link between market opportunities and manufacturing capabilities. Specifically, the optimal changeover flexibility decision is determined in the context of the firm's market entry strategy for successive product generations, the changeover cost between generations, and the production efficiency of the facility. Moreover, the dynamic pricing policy for each product generation is obtained as a function of the firm's market entry strategy and manufacturing efficiency. Our findings provide insights linking internal manufacturing capabilities with external market forces for the high‐tech and high‐volume manufacturer of products with short life cycles. We show the impact of manufacturing efficiency and a firm's ability to benefit from volume‐based learning on the dynamic pricing policy for each product generation. The results demonstrate the benefits realized by a firm that works with its manufacturing equipment suppliers to develop more efficient and flexible technology. In addition, we explore how opportunities afforded by pioneer advantage enable a firm operating a less efficient facility to realize long term competitive advantage by deploying an earlier market entry strategy.     

Measuring Modularity‐Based Manufacturing Practices and Their Impact on Mass Customization Capability: A Customer‐Driven Perspective

As uncertainty in markets and technology intensifies, more companies are adopting modular product and process architectures to cope with increasing demands for individually customized products. Modularity‐based manufacturing is the application of unit standardization or substitution principles to create modular components and processes that can be configured into a wide range of end products to meet specific customer needs. This study defines modularity‐based manufacturing practices (MBMP), develops a valid and reliable instrument to measure MBMP, builds a framework that relates customer closeness, MBMP, and mass customization capability, and tests structural relationships within this framework using LISREL. Based on 303 responses from members of the Society of Manufacturing Engineers, statistically significant and positive relationships were found among customer closeness, modularity‐based manufacturing practices, and mass customization capability. Managerial implications of the empirical findings of this study and future research directions are also discussed.     

Impact of Information Technology Integration and Lean/Just‐In‐Time Practices on Lead‐Time Performance*

Managers seeking to improve lead‐time performance are challenged by how to balance resources and investments between process improvement achieved through lean/just‐in‐time (JIT) practices and information technology (IT) deployment. However, extant literature provides little guidance on this question. Motivated by both practical importance and lack of academic research, this article examines empirically the relationships among interfirm IT integration, intrafirm IT integration, lean/JIT practices, and lead‐time performance using data from IndustryWeek's Census of Manufacturers ( IndustryWeek, 2006 ). The results provide several new insights on the relationship between IT integration and lean/JIT practices. First, the study confirms that implementing lean/JIT practices significantly reduces lead time. Second, lean/JIT practices mediate the influence of IT integration on lead‐time performance. This suggests that process improvements that result from lean/JIT practices are important contributors to the success of IT integration. Even companies that have experienced success in reducing lead time through lean/JIT practices may benefit from IT integration practices such as those embodied in enterprise resource planning systems. The findings provide managers with empirical evidence and a theoretical framework on the balance between lean/JIT and IT for effecting improvement in lead‐time performance, thus offering practical guidance on this important question. Future research is needed to extend the lean/JIT practices in this study to supply chain practices and explore the relationship between supply chain practices and IT integration.     

Managing Supply Risk for Vertically Differentiated Co‐Products

The manufacturing complexity of many high‐tech products results in a substantial variation in the quality of the units produced. After manufacturing, the units are classified into vertically differentiated products. These products are typically obtained in uncontrollable fractions, leading to mismatches between their demand and supply. We focus on product stockouts due to the supply–demand mismatches. Existing literature suggests that when faced with product stockouts, firms should satisfy all unmet demand of a low‐end product by downgrading excess units of a high‐end product (downward substitution). However, this policy may be suboptimal if it is likely that low‐end customers will substitute with a higher quality product and pay the higher price (upward substitution). In this study, we investigate whether and how much downward substitution firms should perform. We also investigate whether and how much low‐end inventory firms should withhold to strategically divert some low‐end demand to the high‐end product. We first establish the existence of regions of co‐production technology and willingness of customers to substitute upward where firms adopt different substitution/withholding strategies. Then, we develop a managerial framework to determine the optimal selling strategy during the life cycle of technology products as profit margins shrink, manufacturing technology improves, and more capacity becomes available. Consistent trends exist for exogenous and endogenous prices.     

Managing Differentiation‐Integration Duality in Supply Chain Integration*

Existing research focuses on the positive returns to operational performance of firms’ supply chain integration (SCI) with suppliers, buyers, and customers. We draw on differentiation‐integration duality and contingency theory to suggest that manufacturing firms should seek to achieve both integration through supply chain coordination activities and differentiation through modularity‐based manufacturing practices (MBMP). Using a sample of 261 manufacturing firms, we identify an inverse U‐shaped relationship between SCI and operational performance. Furthermore, we find support for the importance of differentiation‐integration duality as a fit between high levels of SCI and high levels of MBMP results in enhanced operational performance. We find support for a contingency perspective as fit is especially critical at higher levels of environmental uncertainty. Implications for theory, practice, and further research are suggested.     

Using Real‐Time Decision Tools to Improve Distributed Decision‐Making Capabilities in High‐Magnitude Crisis Situations*

Multi‐organizational collaborative decision making in high‐magnitude crisis situations requires real‐time information sharing and dynamic modeling for effective response. Information technology (IT) based decision support tools can play a key role in facilitating such effective response. We explore one promising class of decision support tools based on machine learning, known as support vector machines (SVM), which have the capability to dynamically model and analyze decision processes. To examine this capability, we use a case study with a design science approach to evaluate improved decision‐making effectiveness of an SVM algorithm in an agent‐based simulation experimental environment. Testing and evaluation of real‐time decision support tools in simulated environments provides an opportunity to assess their value under various dynamic conditions. Decision making in high‐magnitude crisis situations involves multiple different patterns of behavior, requiring the development, application, and evaluation of different models. Therefore, we employ a multistage linear support vector machine (MLSVM) algorithm that permits partitioning decision maker response into behavioral subsets, which can then individually model and examine their diverse patterns of response behavior. The results of our case study indicate that our MLSVM is clearly superior to both single stage SVMs and traditional approaches such as linear and quadratic discriminant analysis for understanding and predicting behavior. We conclude that machine learning algorithms show promise for quickly assessing response strategy behavior and for providing the capability to share information with decision makers in multi‐organizational collaborative environments, thus supporting more effective decision making in such contexts.     

Take‐Back Legislation: Consequences for Remanufacturing and Environment

In the last two decades, many countries have enacted product take‐back legislation that holds manufacturers responsible for the collection and environmentally sound treatment of end‐of‐use products. In an industry regulated by such legislation, we consider a manufacturer that also sells remanufactured products under its brand name. Using a stylized model, we consider three levels of legislation: no take‐back legislation, legislation with collection targets, and legislation with collection and reuse targets. We characterize the optimal solution for the manufacturer and analyze how various levels of legislation affect manufacturing, remanufacturing, and collection decisions. First, we explore whether legislation with only collection targets causes an increase in remanufacturing levels, which is argued to be an environmentally friendlier option for end‐of‐use treatment than other options such as recycling. While increased remanufacturing alone is usually perceived as a favorable environmental outcome, if one considers the overall environmental impact of new and remanufactured products, this might not be the case. To study this issue, we model the environmental impact of the product following a life cycle analysis–based approach. We characterize the conditions under which increased remanufacturing due to take‐back legislation causes an increase in total environmental impact. Finally, we model the impact of legislation on consumer surplus and manufacturer profits and identify when total welfare goes down because of legislation.     

Technology‐Based New Product Development Partnerships*

Hypotheses were developed to capture the dynamic capabilities that result from interfirm partnerships during the joint new product development (NPD) process—the ability to build, integrate, and reconfigure existing resources to adapt to rapidly changing environments. These capabilities, in turn, were proposed to have a positive impact on NPD performance outcomes: (a) proportion of new product success and (b) superior new product commercialization. In contexts where the locus of innovation is rapidly changing, the impact of interfirm NPD dynamic capabilities was hypothesized to be diminished in high‐technology contexts, especially for buyers (original equipment manufacturers) and to a lesser extent for suppliers. Still, technology‐based interfirm NPD partnerships were predicted to ultimately outperform low‐technology ones in both NPD performance outcomes. Finally, information technology (IT) support for NPD was hypothesized to influence the interfirm NPD partnership's dynamic capabilities. Using survey data from 72 auto company managers and their suppliers, the proposed model in which IT support for NPD influences the success of interfirm NPD partnerships through the mediating role of interfirm NPD partnership dynamic capabilities in high‐ and low‐technology contexts was generally supported. The results shed light on the nature of technology‐based interfirm NPD partnerships and have implications for their success. Theoretical and managerial implications are discussed.     

Measuring Dynamic Capabilities: Practices and Performance in Semiconductor Manufacturing

Research on dynamic capabilities emphasizes the importance and role of organizational routines in explaining interfirm differences in performance. While performance differences are well documented, few empirical analyses explore the processes inside organizations that lead to dynamic capabilities or attempt to define and measure their performance effects. This paper examines one type of dynamic capability – the development and introduction of new process technologies in semiconductor manufacturing. This dynamic capability is an important source of competitiveness in the semiconductor industry, given the short product lifecycles, rapid price declines, and rapid technological advances that define the industry. Because much of the knowledge that underpins semiconductor manufacturing is idiosyncratic, firm-level R&D organization and information technology practices that facilitate problem solving and learning-based improvement provide important and enduring advantages. We derive models of the rate of improvement in manufacturing yield (i.e. the quality of production) and cycle time (i.e. the speed of production) following the development and introduction of new process technologies in manufacturing facilities, and test the empirical specifications of these models. The ways in which semiconductor manufacturers accumulate experience and articulate and codify knowledge within the manufacturing environment build new process development and introduction dynamic capabilities that improve performance.     

Complementary Drivers of New Product Development Performance: Cross‐Functional Coordination,Information System Capability,and Intelligence Quality

Coordination efforts that access and align relevant cross‐functional expertise are regarded as an essential element of innovation success. In recent years, these efforts have been further augmented through complementary investments in information systems, which provide the technological platforms for information sharing and coordination across functional and organizational boundaries. Somewhat overlooked has been the critical mediating role of the intelligence gained through these efforts and capabilities. This study draws on the theory of complementarity to elaborate on the nature of this mediating concept. Theoretical predictions of the model are tested using instrument variable regression analysis of data collected from a sample of publicly traded US manufacturing firms. The findings suggest that the effects of both internal and external coordination on market intelligence and supply‐chain intelligence are moderated by the firm's information system capability. The effect of both types of intelligence quality on new product development performance was contingent with the effects being enhanced (attenuated) when the market conditions were dynamic (stable). The results are robust to common‐method bias, endogeneity concerns, and alternative estimation methods.     

PRODUCT OFFERING,PRICING, AND MAKE‐TO‐STOCK/MAKE‐TO‐ORDER DECISIONS WITH SHARED CAPACITY

In an era of mass customization, many firms continue to expand their product lines to remain competitive. These broader product lines may help to increase market share and may allow higher prices to be charged, but they also cause challenges associated with diseconomies of scope. To investigate this tradeoff, we considered a monopolist who faces demand curves, which for each of its potential products, decline with both price and response time (time to deliver the product). The firm must decide which products to offer, how to price them, whether each should be make‐to‐stock (mts) or make‐to‐order (mto), and how often to produce them. The offered products share a single manufacturing facility. Setup times introduce disceonomies of scope and setup costs introduce economies of scale. We provide motivating problem scenarios, model the monopolist's problem as a non‐linear, integer programming problem, characterize of the optimal policy, develop near‐optimal procedures, and discuss managerial insights.     

Customer Learning Processes,Strategy Selection,and Performance in Business‐to‐Business Service Firms*

Learning about customers takes place through relevant dialogues with those customers, also known as customer relationship management (CRM). As relationships develop, information about the customer is gathered in the firm's customer information systems (CIS): the content, processes, and assets associated with gathering and moving customer information throughout the firm. This research develops a measure of CIS management capabilities based on learning organization theory and measured by the ability to get, store, move, and use information throughout the business unit. This measure is then used to analyze customer learning processes and associated performance in the context of marketing strategic decision making. This study of 209 business services firms finds that generic marketing strategy positioning (low‐cost and differentiation) and the marketing tactics of personalization and customization are related to CIS development. Customer information systems development in turn is associated with higher levels of customer‐based performance, which in turn is associated with increased business growth. Since the strongest association with customer‐based performance is strategy selection, the long‐term benefits of the knowledge gained from the CIS may be in the ability to assist in measuring customer‐based performance, rather than in the ability to immediately contribute to performance. Finally, for these firms, customization and personalization are not directly associated with performance and thus may not be necessary to support every firm's marketing strategy.     

An Evaluation of the NERJIT Priority Rule in a Kanban‐Controlled Flowshop

Significant progress in production and information technologies and innovations in management of operations during the last couple of decades have made the production of small lots and deployment of Just‐In‐Time (JIT) concepts in flowshops possible. As a result, some researchers and practitioners have been seeking to improve the performance of non‐repetitive systems using JIT concepts. In this process, the JIT concepts that were originally designed for mass production have been modified to adapt JIT to non‐repetitive systems. This article uses a priority rule that is based on real‐time demand and production information for sequencing jobs in a kanban‐controlled flowshop. The analysis of the effect of this priority rule; the number of kanbans; the length of the withdrawal cycle; First‐Come, First‐Served (FCFS); and Shortest Processing Time (SPT) on four performance measures—customer wait time, total inventory, input stock‐point inventory, and output stock‐point inventory, shows that the use of this priority rule results in a significant reduction of customer wait time and a slight decrease in inventory.     

Coordinating Service‐Sensitive Demand and Capacity by Adaptive Decision Making: An Application of Case‐Based Decision Theory*

The subject of this article is the simultaneous choice of product price and manufacturing capacity if demand is stochastic and service‐level sensitive. In this setting, capacity as well as price have an impact on demand because several aspects of service level depend on capacity. For example, delivery time will be reduced if capacity is increased given a constant demand rate. We illustrate the relationship between service level, capacity, and demand reaction by a stylized application problem from the after‐sales services industry. The reaction of customers to variations in service level and price is represented by a kinked price‐demand‐rate function. We first derive the optimal price‐capacity combination for the resulting decision problem under full information. Subsequently, we focus on a decision maker (DM) who lacks complete knowledge of the demand function. Hence the DM is unable to anticipate the service level and consequently cannot identify the optimal solution. However, the DM will acquire additional information during the sales process and use it in subsequent revisions of the price‐capacity decision. Thus, this decision making is adaptive and based on experience. In contrast to the literature, which assumes certain repetitive procedures somewhat ad hoc, we develop an adaptive decision process based on case‐based decision theory (CBDT) for the price‐capacity problem. Finally, we show that a CBDT DM in our setting eventually finds the optimal solution, if the DM sets the price based on absorption costs and adequately adjusts the capacity with respect to the observed demand.     

Single‐Period Two‐Product Assemble‐to‐Order Systems with a Common Component and Uncertain Demand Patterns

We consider a single‐period assemble‐to‐order system that produces two types of end products to satisfy two independent and stochastic customer orders. Each type of product is used to fulfill a particular customer order and these two products share a common component. Furthermore, one customer may confirm her order before the other one, and the manufacturer needs to make a commitment immediately upon the receipt of each customer order on how many products to be delivered. We propose a model for optimizing the inventory and production decisions under the above ATO environment. We also extend our model to the situation where the manufacturer can fulfill the unsatisfied low‐priority demand using the left‐over inventories after fulfilling the high‐priority demand, in case the low‐priority customer arrives first. Numerical experiments are conducted, which provide some interesting insights on the impact of uncertain demand pattern.     

Institutional and Resource‐based Explanations for Subsidiary Performance

Addressing calls to integrate insights from institutional theory and the resource‐based view, we bring together dual theoretical explanations from institutional theory and the resource‐based view to examine the effectiveness of transfer of practice and human capital development as two routes to subsidiary performance. Our study of Hong Kong firms with subsidiaries in Mainland China shows that both routes positively affect subsidiary performance. However, our data show that our sampled firms struggled to successfully transfer practices from their parents. We attribute an explanation for this to the characteristics of practices as organizational capabilities in which transfer is made harder by the difficulty in replicating such capabilities. Consequently, developing subsidiary human capital is an important ally to practice transfer as a means to achieve superior subsidiary performance. Our results raise interesting questions about practice transfer and the resource‐based view relevant to future scholarly research.     

An Agent‐Based Model for Pathogen Persistence and Cross‐Contamination Dynamics in a Food Facility

We used an agent‐based modeling (ABM) framework and developed a mathematical model to explain the complex dynamics of microbial persistence and spread within a food facility and to aid risk managers in identifying effective mitigation options. The model explicitly considered personal hygiene practices by food handlers as well as their activities and simulated a spatially explicit dynamic system representing complex interaction patterns among food handlers, facility environment, and foods. To demonstrate the utility of the model in a decision‐making context, we created a hypothetical case study and used it to compare different risk mitigation strategies for reducing contamination and spread of Listeria monocytogenes in a food facility. Model results indicated that areas with no direct contact with foods (e.g., loading dock and restroom) can serve as contamination niches and recontaminate areas that have direct contact with food products. Furthermore, food handlers’ behaviors, including, for example, hygiene and sanitation practices, can impact the persistence of microbial contamination in the facility environment and the spread of contamination to prepared foods. Using this case study, we also demonstrated benefits of an ABM framework for addressing food safety in a complex system in which emergent system‐level responses are predicted using a bottom‐up approach that observes individual agents (e.g., food handlers) and their behaviors. Our model can be applied to a wide variety of pathogens, food commodities, and activity patterns to evaluate efficacy of food‐safety management practices and quantify contamination reductions associated with proposed mitigation strategies in food facilities.