Performance Impacts of Strategy,Information Technology Applications,and Business Process Outsourcing in U.S. Manufacturing Plants

This paper develops a conceptual model to study the role of outsourcing strategies and plant‐level information technology (IT) application infrastructure in the outsourcing of production and support business processes, as well as their subsequent impact on overall plant performance. We validate this model empirically using cross‐sectional survey data from U.S. manufacturing plants. We find that some IT applications are more effective at enabling the outsourcing of business processes than others. For example, the implementation of enterprise management systems is associated with the outsourcing of both production and support processes, whereas operations management systems are not associated with the outsourcing of plant processes. Plants with a low‐cost outsourcing strategy are more likely to outsource support processes than plants with a competency‐focused outsourcing strategy. However, both cost‐ and competency‐based strategies have a positive and similar impact on the outsourcing of production processes. In terms of implications for plant performance, our findings indicate that the outsourcing of production and support processes is associated with higher gross margins. Although plant IT infrastructure is positively associated with favorable on‐time delivery rates, there is no positive association between the incidence of plant outsourcing and on‐time delivery rates. These results have implications for crafting plant‐level outsourcing strategies and for investments in IT systems to facilitate the outsourcing of business processes for enhanced plant performance.     

Longitudinal Analysis of Inhibitors of Manufacturer Delivery Performance

This article examines demand, manufacturing, and supply factors proposed to inhibit manufacturer delivery execution. Extant research proposes many factors expected to harm delivery performance. Prior cross‐sectional empirical research examines such factors at the plant level, generally finding factors arising from dynamic complexity to be significant, but factors arising from detail complexity to be insignificant. Little empirical research examines the factors using product‐level operating data, which arguably makes more sense for analyzing how supply chain complexity factors inhibit delivery. For purposes of research triangulation, we use longitudinal product‐level data from MRP systems to examine whether the factors inhibit internal manufacturing on time job rates and three customer‐oriented measures of delivery performance: product line item fill rates, average delivery lead times, and average tardiness. Our econometric models pool product line item data across division plants and within distinct product families, using a proprietary monthly dataset on over 100 product line items from the environmental controls manufacturing division of a Fortune 100 conglomerate. The data summarize customer ordering events of over 900 customers and supply chain activities of over 80 suppliers. The study contributes academically by finding significant detail complexity inhibitors of delivery that prior studies found insignificant. The findings demonstrate the need for empirical research using data disaggregated below the plant‐level unit of analysis, as they illustrate how some factors previously found insignificant indeed are significant when considered at the product‐level unit of analysis. Managers can use the findings to understand better which drivers and inhibitors of delivery performance are important.     

Improving operational plant performance in international manufacturing networks: the effects of integrative capabilities and plant roles

Abstract

This study adopts the resource-based view to examine how two integrative capabilities of a plant embedded in a manufacturing network enhance operational performance: the capability to connect with other plants in its manufacturing network (a networking capability, NC), and the capability to integrate manufacturing processes with new product development processes (a new product development capability, NPDC). Our model tests the indirect effect of NC on cost, quality, flexibility and delivery through NPDC. Furthermore, we test if the relationship between NC and NPDC depends on the strategic role of the plant. Using survey data from a sample of 521 plants, we confirmed the mediating effect of NPDC. However, the moderation effect was not confirmed. Our findings reveal that plants can deploy NPDC capabilities, fostered by NC, independently from their role in the network. This study contributes to the understanding of plant capabilities and plant roles in manufacturing networks.     

Measuring management quality in the factory

The last decade has seen a growing consensus that manufacturing matters. The factory as the producing unit is the core of the firm's manufacturing activities. However, even now, it is not agreed on or even fully understood what constitutes a well-managed factory. This article attempts to create a causal model of management quality in the plant, identifying key managerial levers for improving plant performance. The model is based on an integrated view of three core processes: supply chain, product and process development, and strategy deployment. We postulate that the joint performance of the three processes is determined by the quality with which they are managed. Management quality is operationalized as consisting of six types of action: delegation (or decentralization), integration (or coordination), measurement, employee participation, communication, and employee development.This management quality model has been applied to the design of the Top Usine 1995 Best Factory Award administered in cooperation with the French weekly L'Usine Nouvelle. Data collected from participating plants do substantiate our view that plant performance is substantially driven by management quality: the six management quality dimensions are shown to be statistically linked to performance improvement rates in the plant, and we provide further evidence of this link by detailing the case example of the award winner. Our survey also shows that supply management appears to be a common problem area for the factories participating in the Top Usine award, including the best ones.     

Optimal maintenance intervals for a multi-component system

In the manufacturing industry, preventive maintenance (PM) is carried out to minimise the probability of plant unexpected breakdown. Planned PM is preferred as disruption to operation is then minimised. Suggested PM intervals are normally prepared by the original equipment manufacturers (OEMs), however due to the multifaceted relationship between operating context and production requirement for different plants, it is unlikely that these suggested intervals as prescribed by the OEMs are optimal. Reliability, budget and breakdown outages cost are some of the critical factors that will affect the calculation of optimal maintenance intervals. Maintenance managers are required to determine optimal maintenance intervals with the above different requirements set by management. In this paper three models are proposed to calculate optimal maintenance intervals for multi-component system in a factory subjected to minimum required reliability, maximum allowable budget and minimum total cost. Numerical examples are provided to illustrate the application and usefulness of the proposed models.     

The impact of knowledge properties on international manufacturing transfer performance

Abstract

This paper examines how knowledge properties of a manufacturing activity transfer in international manufacturing network impact performance during the transfer itself and after steady state has been reached. Hierarchical regression was used to test the relationship on survey data from 178 companies. Knowledge properties as a group was significantly affected by both performance measures when controlling for the effects of sender unit experience, sender unit size and receiver unit experience. The activities transferred thus impact the success of the transfer. The control variables of sender unit experience and receiver unit experience have their relatively strongest performance effects after steady state has been reached. Independency was the single knowledge property dimensions with the strongest relative performance effect. This is one of the first survey studies to cover both the performance of the transfer itself and after reaching steady state of manufacturing transfers. Several strands of further research were therefore identified.     

Lead factory operationalisation and challenges

Abstract

This paper deepens the understanding of the lead factory concept by examining how the lead factory role is operationalised and what challenges are associated with it. The research is based on an explorative case study of eight Swedish lead factories in the manufacturing industry. The empirical findings suggest that the understanding of the lead factory concept should be extended as it is not restricted to one type of set-up. The findings show a spectrum ranging from an entire manufacturing plant, parts of a plant, to a virtual plant considered to be the lead factory. The research also shows a broad range of challenges experienced by lead factories. Several of these are related to and originate from unclear role, responsibility and mandate of the lead factory. The lack of dedicated resources for lead factory activities, specifically long-term development and difficulties in measuring the benefits of the role, were other challenges faced.     

Lowest Cost May Not Lower Total Cost: Using “Spackling” to Smooth Mass‐Customized Production

Consider a manufacturer who mass customizes variants of a product in make‐to‐order fashion, and also produces standard variants as make‐to‐stock. A traditional manufacturing strategy would be to employ two separate manufacturing facilities: a flexible plant for mass‐customized items and an efficient plant for standard items. We contrast this traditional focus strategy with an alternative that better utilizes capacity by combining production of mass‐customized and standard items in one of two alternate spackling strategies: (1) a pure‐spackling strategy, where the manufacturer produces everything in a (single) flexible plant, first manufacturing custom products as demanded each period, and then filling in the production schedule with make‐to‐stock output of standard products; or (2) a layered‐spackling strategy, which uses an efficient plant to make a portion of its standard items and a separate flexible plant where it spackles. We identify the optimal production strategy considering the tradeoff between the cost premium for flexible (versus efficient) production capacity and the opportunity costs of idle capacity. Spackling amortizes fixed costs of capacity more effectively and thus can increase profits from mass customization vis‐à‐vis a focus strategy, even with higher cost production for the standard goods. We illustrate our framework with data from a messenger bag manufacturer.     

MODELS FOR OPTIMAL LEAD TIME REDUCTION

Most inventory and production planning models in the academic literature treat lead times either as constants or random variables with known distributions outside of management control. However, a number of recent articles in the popular press have argued that reducing lead times is a dominant issue in manufacturing strategy. The benefits of reducing customer lead times that are frequently cited include increased customer demand, improved quality, reduced unit cost, lower carrying cost, shorter forecast horizon, less safety stock inventory, and better market position. Although the costs of reducing lead times in the long term may be relatively insignificant compared with the benefits, in the short term these costs can have a significant impact on the profitability of a firm. This article develops a conceptual framework within which the costs and benefits of lead time reduction can be compared. Mathematical models for optimal lead time reduction are developed within this framework. The solutions to these models provide methods for calculating optimal lead times, which can be applied in practice. Sensitivity analysis of the optimal solutions provides insight into the structure of these solutions.     

EXPLAINING PLANT-LEVEL DIFFERENCES IN MANUFACTURING OVERHEAD: STRUCTURAL AND EXECUTIONAL COST DRIVERS IN THE WORLD AUTO INDUSTRY

Using data from 62 automobile assembly plants worldwide, we examine the extent to which various structural cost drivers (plant scale, automation, and product mix complexity) and exe-cutionalcost drivers (product manufacturability, management policies, and production practices) account for plant-level differences in manufacturing overhead. Our analysis of structural drivers finds that overhead hours per vehicle are negatively associated with production volumes, consistent with economies of scale in overhead functions. However, automation appears to have little influence on overhead levels. Product mix complexity's effect on overhead requirements varies with the complexity's form and the plant's manufacturing capabilities. Option and parts complexity (measures of peripheral and intermediate product variety) both exhibit adverse effects on overhead, reflecting the considerable logistical, coordination, and supervisory challenges that accompany an increased number of parts and more complex manufacturing tasks. In contrast, model mix complexity (a measure of fundamental variety) appears to have little impact on direct or overhead labor requirements in auto assembly plants. Our analysis of executional drivers provides empirical support for the claim that advanced manufacturing practices such as the reduction of buffers, multiskilled production workers, and the use of teams can lower overhead costs. We also find preliminary evidence that the lower overhead costs in Japanese auto assembly plants are due primarily to the use of multiskilled work teams and the shifting of traditional overhead activities to production workers. Overall, our results indicate that manufacturing overhead is a function not only of the structural cost drivers that have dominated the academic literature but also of executional cost drivers that are harder to duplicate and therefore potentially more valuable for achieving competitive advantage.     

DIAGNOSING ORDER PLANNING PERFORMANCE AT A NAVY MAINTENANCE AND REPAIR ORGANIZATION,USING LOGISTIC REGRESSION

We present a tool to diagnose the behavior of planners in complex production processes and to establish improvement potential for the delivery performance by changing the planning behavior. Scientific literature on production control offers valuable knowledge, but the complexity of real‐life processes makes it impossible to directly apply this knowledge in real‐life. The presented tool identifies possible deficiencies in the current way of managing the business processes, by matching the scientific knowledge on order planning with data reflecting the real‐life processes via logistic regression. A case study at a maintenance organization illustrates the diagnosis tool.     

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.     

REVISITING ALTERNATIVE THEORETICAL PARADIGMS IN MANUFACTURING STRATEGY

Testing and cross‐validation of theories and paradigms are necessary to advance the field of manufacturing strategy. When the findings of one study are also obtained in other studies, using entirely different databases, we become more confident in the results. Replication alleviates concerns about spurious results and is one motivation for this study. We examine aspects of the tradeoffs concept, production competence paradigm, and a manufacturing strategy taxonomy framework. In regard to the tradeoffs concept, we found evidence of tradeoffs between some, but certainly not all, manufacturing capabilities of quality, cost, delivery, and customization. The relationships get sharper when controlling for process choice. For example, the tradeoff between cost and customization is particularly strong between plants that have different process choices. We find that such tradeoffs can change, or even disappear, however, once the process choice is in place. With respect to the production competence paradigm, our analysis shows a statistically significant correlation between production competence and operations performance in batch shops, but not in plants with other process choices. Finally, using variables similar to those of Miller and Roth, our data produced three similar clusters even though their unit of analysis was much more macro than ours. Controlling for process choice is consistent with the current manufacturing strategy literature that emphasizes dynamic development of capabilities within the context of path dependencies. A major argument of this strand of research is that operations decisions not only affect current capabilities, but also set the framework for development of capabilities in the future. That being the case, controlling for process choice (or other factors such as industry or markets) should contribute to the understanding of capability‐development paths adopted by different manufacturing plants. In short, we found at least partial support for each of the theories examined here, even though the theories seem on the surface to be contradictory and mutually exclusive. Controlling for process choice or other measures of dependency goes a long way in uncovering consistency across different theories and empirical studies in operations management.     

An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

In a JIT manufacturing environment it may be desirable to learn from an archived history of data that contains information that reflects less than optimal factory performance. The purpose of this paper is to use rule induction to predict JIT factory performance from past data that reflects both poor (saturated or starved) and good (efficient) factory performance. Inductive learning techniques have previously been applied to JIT production systems (Markham et al. , Computers and Industrial Engineering, 34 , 717-726, 1998; Markham et al. , International Journal of Manufacturing Technology Management, 11 (4), 239-246, 2000), but these techniques were only applied to data sets that reflected a well-performing factory. This paper presents an approach based on inductive learning in a JIT manufacturing environment that (1) accurately classifies and predicts factory performance based on shop factors, and (2) identifies the important relationships between the shop factors that determine factory performance. An example application is presented in which the classification and regression tree (CART) technique is used to predict saturated, starved or efficient factory performance based on dynamic shop floor data. This means that the relationship between the variables that cause poor factory performance can be discovered and measures to assure efficient performance can then be taken.     

Cost Allocation in Manufacturing–Remanufacturing Operations

For firms remanufacturing their products, the total life‐cycle costs and revenues from new and remanufactured products determine their profitability. In many firms, manufacturing/sales and remanufacturing/remarketing operations are carried out in different divisions. Each division is responsible for only part of the product's life cycle. Practices regarding transfer pricing across divisions vary significantly among companies, affecting the life‐cycle profit performance of the product. In this research, we identify characteristics of transfer prices that achieve the firm‐wide optimal solution. To this end, we consider a manufacturer who also undertakes remanufacturing operations and we focus on price (quantity) decisions. We determine that a cost allocation mechanism that allocates a portion of the initial production cost to each of the two stages of the product life cycle should be used. We also conclude that cost allocation should be implemented as a fixed cost allocation, where charges to the remanufacturing division should be determined independently of the actual quantity of units remanufactured.     

生产性企业的概念性学习和操作性学习研究

本文用一个最优控制模型定量刻画了生产性企业计划期内的概念性学习和操作性学习与生产率、单位成本、废品率以及计划期内的总利润等生产经营指标之间的关系。模型的特点在于其能够规范地描绘众多变量之间的复杂关系。并简要阐述了由模型推导出的一些关于最优概念性学习和操作性学习策略的结论,详细分析了这些策略在生产性企业组织学习实践中的应用,获得了不同于传统理论的结果。     

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.     

生产性企业组织学习最优控制模型及其理论分析

运用最优控制理论研究生产性企业组织学习活动的动态最优决策问题.以企业的概念性学习投资率和操作性学习投资率为决策变量,累积知识量、生产率、单位成本和废品率等为状态变量,计划期内的总利润为指标函数,建立了一个最优控制模型,其特点在于规范地描绘概念性学习和操作性学习对企业累积知识量、生产率、单位成本、质量以及企业利润的动态影响.根据生产性企业组织学习和生产经营的实际情况,提出了一些定量化表达的假设和定义.在假设和定义的基础上利用最大值原理分析了所建立的模型,获得了关于动态最优概念性学习投资策略和操作性学习投资策略性质的一些结论,将这些结论与实际问题相结合,指出了在生产性企业组织学习实践上的含义.     

Multistage Stochastic Optimization for Production‐Inventory Planning with Intermittent Renewable Energy

A growing number of companies install wind and solar generators in their energy‐intensive facilities to attain low‐carbon manufacturing operations. However, there is a lack of methodological studies on operating large manufacturing facilities with intermittent power. This study presents a multi‐period, production‐inventory planning model in a multi‐plant manufacturing system powered with onsite and grid renewable energy. Our goal is to determine the production quantity, the stock level, and the renewable energy supply in each period such that the aggregate production cost (including energy) is minimized. We tackle this complex decision problem in three steps. First, we present a deterministic planning model to attain the desired green energy penetration level. Next, the deterministic model is extended to a multistage stochastic optimization model taking into account the uncertainties of renewables. Finally, we develop an efficient modified Benders decomposition algorithm to search for the optimal production schedule using a scenario tree. Numerical experiments are carried out to verify and validate the model integrity, and the potential of realizing high‐level renewables penetration in large manufacturing system is discussed and justified.     

Inter‐organizational Cooperation and Environmental Change: Moderating Effects between Flexibility and Innovation Performance

The paper investigates the moderator effect of inter‐organizational cooperation in the relationship between workplace flexibility and innovation performance. This research question is important because innovation is dependent on the strategic integration of technological knowledge, requiring organizations to acquire new capabilities rapidly or to ensure the presence of knowledge that may be beyond existing internal capabilities. Inter‐organizational cooperation constitutes a relevant mechanism for a firm to increase its knowledge base concerning new products and processes. High‐cooperation firms may have more opportunities to take advantage of flexibility for innovation performance because it facilitates the access and dispersion of knowledge within the firm. We test the research hypotheses in a sample of manufacturing and service firms. The results contribute to the literature on flexibility and innovation because they demonstrate that inter‐organizational cooperation moderates the relationship between flexibility and innovation performance. We discuss the implications of these results for future research and managerial practice.