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How to Find the Right Supply Chain Strategy? An Analysis of Contingency Variables
Abstract
Contingency variables are characteristics of the business environment that influence the competitive priorities supply chains should pursue for maximizing profits. But which contingency variables should managers focus on when developing a supply chain strategy? On the one hand, if important variables are omitted, the selected strategy may fail to fulfill the needs of the business environment. On the other hand, considering irrelevant variables unnecessarily complicates the strategy formation process, hence preventing well‐suited strategies from being found. As a first step toward resolving this trade‐off, our study analyzes the effects hypothesized to be underlying a set of frequently cited contingency variables referred to as “DWV3” (product lifecycle Duration, delivery time Window, demand Variability, demand Volume, product Variety) as well as contribution margins. We test the hypotheses on archival data from a leading chemical manufacturer using multilevel regression. Our findings indicate that demand variability, the delivery time window, and contribution margins are important for strategy development because they indicate to what extent companies should invest in market mediation. Volume, variety, and lifecycle duration are less important for this purpose, but may instead be used for analyzing the causes of demand variability.
... Importantly, suppliers are aware that that there is no one-size-fits-all supply chain strategy, accentuating the role of trade-offs for achieving operational objectives and such trade-offs may change in times of crisis (von Falkenhausen et al. 2019, Siebert et al. 2020, AlMalki and Durugbo 2023. For instance, there are SCM challenges for trade-offs in the amount of product and process traceability that aids securitisation, regionalisation, and centralisation of supply chain structures during crises like disease outbreaks, food scares, or product contaminations (Lu et al. 2019, Durugbo et al. 2022, Razak et al. 2023, Vega et al. 2023. ...
... Insights on these vulnerabilities and mitigation measures remain central to developing agility and resilience of supply chains. SCM contingencies aid in explaining the efficacy of firms within supply chains (Prajogo et al. 2018) and managers face difficulties identifying relevant variables for formulating well-suited SCM strategies (von Falkenhausen et al. 2019). ...
... these mitigation measures stem from analysing the Evonik-E,Salmonella-C, Horsegate-S, and NotPetya-M cases and these interventions strive to restore normalcy, recovery operations, and regulate networks as partners grapple with exigencies for SCM decisiveness. Although literature notes that there is no one-size-fits-all supply chain strategy(von Falkenhausen et al. 2019, Siebert et al. 2020, researchers stress the need for studies that ascertain variables for developing context-dependent SCM strategies(von Falkenhausen et al. 2019). Accordingly, this research adds to the discourse on chain mitigation variables for SCM in times of crisis but with spotlight on the intense difficulty, extreme trouble, or danger for and within a supply chain.Table 3additionally indicates that underpinning crisis mitigation in the analysed cases is a set of '9Cs' crisis response repertories involving continuous monitoring, comprehensive audits, controlling and securing inventory, close contact and canvasing supply chains, crisis teams and meetings, capacity rebuilding, communication and solution alternatives, clarity and transparency in communications, and cooperation and co-opting independent commissions.Related SCM studies posit on mitigation measures for supply chain disruptions such as insurance, inventory, sourcing, rerouting, demand management, contingency stocks, buffering or bridging, and borrowing or lending materials from organisations within the same sector (Al-Balushi andDurugbo 2020, Kovács and Sigala 2021, Spieske et al. 2022.Third, the findings from the case study indicate that underpinning immediate SCM responses to crises are customer-first mindsets and close discussions with customers, as suggested by the excerpts from the NotPetya-M, Salmonella-C, and Evonik-E cases. ...
Relying on an inductive multi-case logic, this study analyses public discourse involving four supply chain crises in Europe: (i) NotPetya Cyberattack on A.P. Møller-Maersk of 2017, (ii) the Evonik industrial accident of 2012, (iii) Cadbury’s Salmonella Scare of 2006, and (iv) Horsemeat Scandal of 2013. Grounded on contingency theory, the research finds three main operational vulnerabilities (an MSN of threats) surrounding the investigated cases: Market pressures, Sector dependencies, and Network liabilities. The study also identifies four themes of crisis mitigation (4IR measures): (i) intelligence review for reassessments, (ii) integrated relationships for response, (iii) innovation resilience for recovery, and (iv) integrity rebuilding for reassurance. Driving these mitigation measures is a customer-first mindset and close customer discussions that strive to restore normalcy, recover operations, and regulate networks. This research is original in its focus on a ‘supply chain crisis’ paradigm and adds to discourse on competitive and /or restorative capabilities for supply chain management (SCM) in times of crisis. Theoretically, the research advances a ‘coactive’ SCM strategy for improved SCM performance in times of crisis, and managerially, the value of the research lies in insights on ‘best practice’ for supply chain crisis management and decisiveness for confront and contain supply chain crises.
... According to contingency theory, organizations configure their structure and strategy to maintain fit with changing contextual factors to attain high performance (Donaldson, 2001). Thus, there is no "one best way" to manage and organize single companies and supply chains, as different contextual (or contingency) variables require different approaches (Lawrence & Lorsch, 1967;von Falkenhausen et al., 2019). Companies and supply chains must identify reactive and proactive actions to face the external context they operate and understand the degree of fit in terms of interaction between strategies and contextual variables (Venkatraman, 1989). ...
... However, it must also be stated that capabilities are typically developed due to the strategy and structure fit; as such, companies and thus the supply chain can achieve a competitive advantage (Chen, 2019;Stock et al., 1998). In this sense, supply chain capabilities can be ascribed to the response variables that the overall supply chain should pursue to maximize effectiveness in facing contextual (or contingency) variables (Sousa & Voss, 2008;von Falkenhausen et al., 2019). Specifically, supply chain capabilities to withstand uncertainties and contingent challenges are mainly studied in terms of resilience (e.g., Bhamra et al., 2011;Brusset & Teller, 2017;Zsidisin & Wagner, 2010), with the need to further widen this concept to take into consideration specific external megatrends causing instability. ...
... Their full integration indeed assures the most significant effect of these technologies among factories and with supply chain actors fully aligned. Following the call to focus on specific contextual (or contingency) variables for developing supply chain responses (von Falkenhausen et al., 2019), we depicted how supply chains can face changing and turbulent megatrends with different approaches to exploit the potential of I4.0 technologies, assuring that the technologies to be adopted are targeted to enable specific supply chain capabilities. ...
This paper investigates how current megatrends (i.e., aging population, growing urbanization, shifts in consumer demands, geopolitical shifts, depletion of natural resources, climate change) are changing the supply chain landscape and the role of Industry 4.0 (I4.0) technologies to support alignment with these changes. Building on contingency theory, the study employs focus‐group interviews with various experts to generate new insights into fitting supply chain capabilities and enabling technologies. Data collected in the focus groups helped us to identify five supply chain capabilities as prevalent and mostly fitting the external contingencies, i.e., customer‐driven, urban‐centered, resource‐efficient, fast reactive, and human‐centered supply chain. Moreover, this study highlights and compares the potential of I4.0 technologies and their applications in supporting specific supply chain capabilities. The findings of this study can inform supply chain managers in the definition of capabilities to be enhanced at the supply chain level and contribute toward understanding the extent of I4.0 technologies in empowering supply chains to face turbulent and changing conditions.
... opportunity of cross-selling, speed of delivery, and risk of missed deliveries), all of them enjoy a remarkable reduction of the transportation cost. Such an achievement becomes a must when companies deliver goods with a low value density, i.e. with a low unit value and a high volume/weight (Cooper, 1993;Gevaers et al., 2011;Lovell et al., 2005;Von Falkenhausen et al., 2019). This discussion is leading scholars and managers to think about the kind of logistic service that can be offered under reasonable conditions of efficiency, while still providing the client with good levels of speed, dependability, and availability of products for digital purchases. ...
... High speed of delivery and short delivery windows also prevent the consolidation of parcels thus negatively influencing the efficiency of the whole process (Park & Regan, 2004). Furthermore, the value density of the product (i.e. unit value/volume or weight) is traditionally used in logistics to assess the impact of transportation costs on sales profitability (von Falkenhausen et al., 2019;Gevaers et al., 2011;Lovell et al., 2005;Cooper, 1993). ...
E-commerce in the context of sales and distribution strategy has become a heavily used channel and companies need to manage it correctly and integrate it with a variety of other online and offline channels. Based on the literature, two factors are recognized as relevant for the elaboration of a conceptual framework able to explain the actions undertaken by leading companies to effectively get the most from an e-commerce strategy integrated within the overall omnichannel strategy. The two factors are: main distribution channel (direct/indirect) and product value density (high/low). Through the analysis of four case studies (Luxottica, Henkel, Ikea, and Ferragamo), we show that these factors produce different benefits and challenges that companies face when embracing e-commerce. We analyze these benefits and challenges from the viewpoint of both Marketing and Supply Chain Management.
... These examples present distribution channels such as 'click and collect', 'reserve and collect', 'click and drive', and 'pick up at pickup point' (Cao and Li, 2015). Although these channels may differ in some respects (e.g., cross-selling, delivery speed and risk of missed deliveries), they are all characterised by a significant reduction in transportation costs, which is very important in situations where companies deliver low value density goods, i.e., goods with low unit value and high volume/weight (von Falkenhausen et al., 2019). ...
... The relevance of supply chain management (SCM) has been largely highlighted in recent years, and its effects on performance have been broadly discussed and recognized by practitioners and researchers. The demanding task of having a proper design of the SC in accordance with the internal and external characteristics is a very relevant and current topic [8,9]. The concept of SC fit was coined from the research of Fisher [8], who suggested that the match between (1) innovative products with responsive supply chain strategy and (2) between functional products and cost-efficient strategies could generate better results for companies and SCs. ...
This paper analyses how the harmonization between supply and demand uncertainty and supply chain responsiveness (SC fit) impacts business performance. The study analyses data obtained from a sample of 179 manufacturing companies from Portugal. The business performance of companies with different types of SC fit (high-high fit and low-low fit) and misfit (positive and negative) were analyzed and discussed. The results indicate that SC fit is positively related to business performance, economic and productivity, and commercial performance separately. This study advances the literature as the results indicate that SC fit positively affects both commercial and economic, and productivity performance. In contrast, previous empirical studies have mainly addressed the impact only on financial and operational performance.
... Studies have investigated various resilience approaches used to avoid or minimize the damages brought on by natural disasters. Such approaches include cultivating redundant supply sources (Silbermayr & Minner, 2014), maintaining reserve inventory (Morrice et al., 2016), developing a business/disaster recovery plan (Sahebjamnia et al., 2018;Karakasidis, 1997) that includes contingencies in supply chain strategy (Von Falkenhausen et al., 2019), and strategic partnering (Ojha et al., 2014). In particular, Ojha et al. (2013) found support for a positive relationship between financial performance and logistical business continuity planning mediated by disaster immunity. ...
Purpose
Service organization supply chains provide a context that amplifies the complexity of interorganizational interdependencies and the need to build unique capabilities and innovative solutions, especially when confronted with man-made or natural disasters. Using the lens of complex adaptive systems (CAS), this study aims to investigate the role of absorptive capacity (AC), change management capability and information quality in improving a firm’s ability to cope with disasters – disaster immunity (DI). The study uniquely parses absorptive capacity into a three-variable, second-order construct (absorptive human resource management, absorptive complementary knowledge and absorptive infrastructure).
Design/methodology/approach
Using data collected from 264 US service firms in a supply chain context, this paper evaluates the research model using the structural equation modeling approach.
Findings
The second-order, three-dimensional framework for AC has far superior psychometric properties as compared to the previous unidimensional conceptualizations. Results show that AC influences a firm’s DI through change management capability and information quality – two DI enhancing resources.
Originality/value
The paper builds on previous conceptual discussions of absorptive capacity as a multidimensional construct by operationalizing AC as a latent variable with three dimensions (above). Moreover, this paper shows that AC, change management capability, information quality and DI are interrelated parts of a CAS.
... These typologies have in common that they present a discrete choice of one suitable SCS, in a set of two or four. At least one SCS emphasises efficiency/leanness, whereas the others are market mediation SCSs (von Falkenhausen et al., 2019). In another stream of research, scholars such as Yang et al. (2004) use postponement and CODP-positioning to construct strategy continuums. ...
Purpose
Defence supply chains (SCs) aim at operational outcomes, and armed forces depend on them to provide availability and preparedness in peace and sustainability in war. Previous research has focussed on strategies for SCs aiming at financial outcomes. This raises the question of how suitable commercial supply chain strategies (SCSs) are for supply chain design (SCD) in defence. The purpose of this paper is to explain the constructs of SCSs that satisfy military operational requirements and to propose SCSs that are appropriate in defence.
Design/methodology/approach
This paper reports on a Delphi study with 20 experts from Swedish defence authorities. Through three Delphi rounds, two workshops and a validation round, these experts contributed to the reported findings.
Findings
The findings demonstrate that commercial SC constructs are acceptable and applicable in defence but not sufficient. An additional strategy is required to satisfy requirements on availability, preparedness and sustainability. The paper shows that different requirements in peace and war make it challenging to design suitable defence SCs and proposes eight SCSs that satisfy these requirements.
Research limitations/implications
The results emanate from the Swedish defence context and further research is required for generalisation.
Originality/value
This paper extends theory by investigating SCs aiming at operational outcomes. For managers in companies and defence authorities, it explicates how the unique issues in defence must influence SCD to satisfy operational requirements.
... Goal definition is the part of strategy. Supply chain strategy is an endogenous problems as strategies are often resource-based and strive to exploit potential of business entities (von Falkenhausen et al., 2019). Strategy includes goals not only in ongoing perspective but also for the ones to be achieved in the future. ...
The base of knowledge related to supply chain (SC) research is dynamically expanding. The growing trend of publishing scientific papers on the subject is observable, which indicates that the supply chain concept is in the centre of attention of scientists and researchers. It can be assumed that the tendency will continue. This proves the expansive nature of research in the analysed area. The goal of the chapter to present the classify trends and development directions of supply chains. The goal is of cognitive and conceptual character and the research implemented benefits from text mining method. Research is necessary from academic perspective, not only to organize the knowledge but also to identify future research potential. To achieve the research goal, an original methodology was designed by selecting research methods and tools to answer comprehensively to the research questions. The importance of research results for the development of supply chain, management and quality science is indisputable. Research to be conducted strives to prove that heterogeneity, multi-facetedness and multi-directionality of research on supply chain is a difficult but definable phenomenon.
... This research is sponsored by FEDER funds through the program COMPETE-Programa Operacional Factores de Competitividade-and by national funds through FCT-Fundação para a Ciência e a Tecnologia -, under the project UIDB/00285/ 2020. designing the right supply chain (SC) according to the internal and external characteristics of companies is a topic that has been widely addressed by researchers and managers (Fisher, 1997;von Falkenhausen et al., 2019;Zimmermann et al., 2020). More recently, the importance of integrating supply and demand characteristics and SC features gave rise to the concept of SC fit (Wagner et al., 2012;Gligor, 2017), derived from Fisher's (1997) seminal work and from the concept of strategic fit (Venkatraman, 1989). ...
Purpose – This paper investigates the effect of the fit between supply and demand uncertainty (SDU) and supply chain responsiveness (SCR) (SC fit) on business and innovation performance in Brazilian companies.
Design/methodology/approach – The study presented an analysis carried out on an empirical study based on a sample of 150 manufacturing companies. Business and innovation performance of companies with different types of SC fit ( high–high and low–low fits) and misfit (positive and negative) are compared and discussed.
Findings – The results indicated that SC fit had a positive effect on both business and innovation performance. Further analyses suggested that companies with SC fit present similar business performance, independent of the level of SDU that characterizes the environment where they compete, while companies in environments with higher levels of uncertainty tend to present superior innovation performance. Companies with positive and negative misfit present similar performance.
Originality/value – An analysis of the literature showed that there is no consensus when it comes to the definitions and measurements of SC fit. The paper investigates the effects of SC fit on business and innovation performance, while previous empirical studies have mainly addressed its impact on financial performance. Moreover, this study compares the effects of two types of fit and two types of misfit and assesses SC fit in Brazilian manufacturing companies, analyzing the context of an under-researched reality.
... 2.4 Hypotheses development 2.4.1 Product variety and fill rate. The increase in the product variety tends to produce a negative impact on the level of services (Closs et al., 2010;Falkenhausen et al., 2019), setup times (Er and MacCarthy, 2006) and operational costs (Thonemann and Bradley, 2002), as the internal, organizational and structural complexities of the supply chain increase and thereby decrease operational performance (Bozarth et al., 2009). On the other hand, some authors claim that it is possible to increase variety without affecting the fill rate. ...
Purpose
The impact of product variety decisions on fill rate, inventory and sales performance in a consumer goods company has been examined. From a marketing perspective, it is possible to leverage sales, reach new segments and consequently increase competitiveness when there is a greater product variety on the market. However, operations and logistics professionals indicate potential impacts on the supply chain, such as production, storage and distribution complexity. The nature of the product variety-cost-sales performance relationship is not clear, and empirical evidence about whether and how operations cost and sales performance increases with variety is inconclusive.
Design/methodology/approach
The multiple linear regression and the Tobit regression techniques were applied over a seven-year horizon of data from a business intelligence platform of a consumer goods company.
Findings
Our results show that sales performance is negatively associated with product variety. The total effect of product variety on sales performance has been examined, including both the direct effect and the indirect effect through inventory and fill rate. Therefore, the findings provide a comprehensive understanding of the impact of product variety on operations and sales performance.
Originality/value
Several studies have researched the impact of product variety on fill rate, inventory and sales performance separately; however, the research of the impact and the relationship of these factors is scarce and limited.
... It is mainly determined by the collaborations established between purchasing, manufacturing and sales department (Croxton et al., 2001) Support of the demand planning process by the interorganizational flow of information in supply chains Support of the order commitment process by the interorganizational flow of information in supply chains Sharing knowledge on demand forecasting in supply chains Undistorted access to forecasting data On-line access to forecasting data Collaboration between purchasing, manufacturing and sales organizations in demand planning Investigating the role of demand planning and above). The demand variability was measured by the value of standard deviation from the stable demand rate (Talluri et al., 2013), while the demand volume was measured by the percentage of annual sales value represented by the most important product line in the last two years (Falkenhausen et al., 2019). ...
Purpose
The goal of the paper is twofold. First, it aims to empirically conceptualize whether a wide array of fragmented demand planning activities, performed in supply chains, can be logically categorized into actionable sets of practices, which then form a broader conceptualization of the demand planning process. Second, regarding certain contextual factors, our research seeks to investigate the contribution of demand planning, as a higher-order construct, to mitigating disruptions induced by operational risks in supply chains.
Design/methodology/approach
In this study, PLS-SEM was used to estimate the reflective-formative nature of the model. The results of PLS-SEM were additionally complemented by the assessment of the predictive power of our model. Finally, to reveal possible contingency effects, the multigroup analysis (MGA) was conducted.
Findings
The study suggests that demand planning process (DPP) is a second-order construct that is composed of four sets of practices, including goal setting, data gathering, demand forecasting, communicating the demand predictions and synchronizing supply with demand. The study also reveals that the demand planning practices, only when considered together, as a higher-order factor, significantly contribute to mitigating disruptions driven by operational risks. Finally, the research shows that the strength of the impact of demand planning on disruptions is contextually dependent.
Research limitations/implications
While the study makes some important contributions, the obtained findings ought to be considered within the context of limitations. First, the study only investigates disruptions driven by operational risks, ignoring the negative consequences of environmental risks (terrorist attacks, natural disasters, etc.), which may have a far more negative impact on supply chains. Second, the sample is mostly composed of medium and large companies, not necessarily representative of demand planning performed by the entire spectrum of companies operating in the market.
Practical implications
The study shows that to effectively mitigate disruptions induced by operational risks, the demand planning practices should be integrated into a higher-order construct. Likewise, our research demonstrates that the intensity of demand planning process is contingent upon a number of contextual factors, including firm size, demand variability and demand volume.
Social implications
The study indicates that to mitigate disruptions of operational risk, demand planning as a higher-order dynamic capability can be referred to the concept of organizational learning, which contributes to forming a critical common ground, ensuring the balance between formal and informal dynamic routines.
Originality/value
The paper depicts that to fully deal with disruptions, the demand planning practices need to be integrated and categorized into the dedicated higher-order. This may lead to forming demand planning as a higher-order dynamic capability that provides a more rapid and efficient rebuttal to any disruptions triggered by operational risks.
... Goal definition is the part of strategy. Supply chain strategy is an endogenous problems as strategies are often resource-based and strive to exploit potential of business entities (von Falkenhausen et al. 2019). Strategy includes goals not only in ongoing perspective but also for the ones to be achieved in the future. ...
This book proposes essential methods, models, and case studies for Sustainable Logistics and Production in Industry 4.0. In addition to identifying and discussing various challenges and future prospects, it also features numerous case studies and quantitative research from different sectors. The authors (which include academics and managers) present insightful tips on the technical, organizational and social aspects of implementing Sustainable Logistics and Production in Industry 4.0.
In today’s world, changes are coming faster and more unpredictably. Production is becoming more automated, computerized and complex. In short, Industry 4.0 is creating many new opportunities, but at the same time several new challenges.
This book offers a valuable resource for all academics and practitioners who want to deepen their knowledge of Sustainable Logistics and Production in Industry 4.0.
Uncertainty and disruption can significantly impact supply chains, and risk response is a means to diminish or circumvent these challenges. Therefore, this investigation aims to assess lead time variability in the supply chain of active ingredients. The research proposes a decision-making process to evaluate lead time variability in the active ingredient supply chain of the Brazilian chemical industry. It adopts Game Theory and linear programming in its solution. The two major contributions of this study are as follows: first, it presents a procedure to assess lead time variability; second, it proposes a supply chain risk response framework to address the lead time variability problem. Supply chain and lead time variability are emerging areas that offer significant opportunities for exploration, one of which is to evaluate lead time variability from the multiple perspectives presented in this study, thereby contributing to the current literature through a real application.
Keywords:
decision-making process; lead time variability; supply chain risk
This paper analyses how the harmonization between supply and demand uncertainty and supply chain responsiveness (SC fit) impacts business performance. The study analyses data obtained from a sample of 179 manufacturing companies from Portugal. The business performance of companies with different types of SC fit (high-high fit and low-low fit) and misfit (positive and negative) were analyzed and discussed. The results indicate that SC fit is positively related to business performance, economic and productivity, and commercial performance separately. This study advances the literature as the results indicate that SC fit positively affects both commercial and economic, and productivity performance. In contrast, previous empirical studies have mainly addressed the impact only on financial and operational performance.KeywordsSupply chain fitSupply and demand uncertaintySupply chain responsivenessPerformance
The current adverse global conditions have generated the need for organisations to integrate various supply chain paradigms for the purpose of meeting customer demand while holding on to the environmental innovation requirements. The lean and agile supply chain (LASC) strategy is considered a relevant option for supply chain decision-makers. Precisely, the LASC strategy and its impact on innovation performance are considered one of the most currently discussed topics in the supply chain management literature. Hence, based on the resource-based view (RBV), the present paper discusses the impact of LASC on supply chain innovation performance through dynamic capability, relational embeddedness, and innovation capacity. The empirical evidence is operated within the Tunisian automotive components supply chain based on the structural equation modelling approach within 125 companies. The findings generated insights on the importance of the mediating role of the dynamic capability, the relational trust, and the R&D management to foster the impact of LASC innovation performance.
Estimation in (linear) multilevel models usually relies on maximum likelihood (ML) methods. The various computer programs for multilevel analysis employ versions of full information (FIML) and restricted maximum likelihood (REML) methods. Two vital assumptions underlying ML theory are that (a) the residuals are i.i.d. with a distribution from a specified class, usually the multivariate normal, and (b) the sample size is (sufficiently) large. More specifically, the attractive properties of FIML estimators-consistency, (asymptotic) efficiency and (asymptotic) normality-are derived from the supposition that the sample size goes to infinity. In practice, however, these assumptions will frequently be met only approximately, which may lead to severely biased estimators and incorrect standard errors [7]. Resampling methods can be used to obtain consistent estimators of bias and standard errors, and to obtain confidence intervals and bias-corrected estimates of model parameters. A number of general resampling approaches are found throughout the literature, of which we mention the bootstrap and the jackknife, permutation, and cross-validation. Bayesian Markov chain Monte Carlo methods [e.g., 16, 23] and simulation-based estimators for mixed nonlinear models [e.g., 27, 61, 63] are also closely related to these resampling methods. Particularly, bootstrap and jackknife procedures have proven to be methods that yield satisfactory results in small sample situations under minimal assumptions. In this chapter, we discuss resampling of multilevel data by means of bootstrap and jackknife procedures. In cases where the assumptions underlying ML methods for estimating multilevel models are violated, bootstrap and jackknife estimation may provide useful alternatives. The application of the bootstrap and the jackknife to multilevel models is not straightforward. For the bootstrap, there are several possibilities, depending on the nature of the data and the assumptions one is willing to make. Each of them, however, has its own associat d problems. In this chapter, we discuss three different approaches, which are derived from general principles of bootstrap theory and apply concepts adapted from bootstrapping regression models. The application of the jackknife to multilevel analysis is based on a version of the delete-m jackknife approach [57, Section 2.3]. In this procedure, subsamples are obtained from the original sample by successively removing mutually exclusive groups of size m. For the application to multilevel analysis, the delete-m jackknife has been adapted for groups of unequal size. Bootstrap and jackknife estimation in the context of multilevel analysis have been studied by several authors and for various models and situations. Laird and Louis [33, 34] discuss empirical Bayes confidence intervals based on bootstrap samples and Moulton and Zeger [45] study bootstrapping a model for repeated measurements. Bellmann et al. [3] use a parametric bootstrap for a panel data model that is essentially a multilevel model and Booth [4] similarly uses a parametric bootstrap for generalized linear mixed models. Goldstein [24] presents an iterated bootstrap based on the results of Kuk [32]. A theoretical analysis of nonparametric bootstrapping of balanced twolevel models without covariates is given in Davison and Hinkley [14, pp. 100-102]. Our discussion largely follows the lines of the systematic development of resampling methods for multilevel models in Busing et al. [8, 9, 10, 12], Van der Leeden et al. [68], and Meijer et al. [42, 43]. In this chapter, we focus on FIML estimation for multilevel linear models with two levels. The ideas, however, are directly applicable to REML estimators and generalize straightforwardly to models with three or more levels. In Section 11.2, we define the model upon which we center our discussion and we elaborate on the consequences of violating the assumptions of ML estimation in multilevel models. In Section 11.3, we briefly discuss the general ideas of the bootstrap and the jackknife, and the specific issues nvolved in the application of the bootstrap to regression models. Section 11.4 provides an extensive discussion of the three methods for bootstrap implementation, as well as a number of approaches to construct confidence intervals. In Section 11.5, the application of the jackknife to multilevel models is discussed. Section 11.6 briefly discusses the availability of resampling options in existing software for multilevel analysis. In Section 11.7, we discuss some results of evaluation studies of the various resampling approaches and in Section 11.8, we briefly discuss application of the presented approaches to other types of multilevel models, and we mention various possible extensions and alternatives to the (bootstrap) resampling methods presented.
When do short lead times warrant a cost premium? Decision makers generally agree that short lead times enhance competitiveness, but have struggled to quantify their benefits. Blackburn (2012) argued that the marginal value of time is low when demand is predictable and salvage values are high. de Treville et al. (2014) used real-options theory to quantify the relationship between mismatch cost and demand volatility, demonstrating that the marginal value of time increases with demand volatility, and with the volatility of demand volatility. We use the de Treville et al. model to explore the marginal value of time in three industrial supply chains facing relatively low demand volatility, extending the model to incorporate factors such as tender-loss risk, demand clustering in an order-up-to model, and use of a target fill rate that exceeded the newsvendor profit-maximizing order quantity. Each of these factors substantially increases the marginal value of time. In all of the companies under study, managers had underestimated the mismatch costs arising from lead time, so had underinvested in cutting lead times.
Modern day market places are highly varied and cannot be serviced effectively by a single supply chain paradigm. Consequently products and services must be provided to the end consumer via tailored supply chain strategies. This article categorises consumer products and details the specific supply chain management tools and techniques required to service each. A comparison of lean and agile strategies is provided along with a detailed explanation of the integration of the two within a Leagile supply chain. The application of such a strategy for electronic products is provided via a four stage case study. A route map for engineering supply chains to match customer requirements is developed in order to avoid costly and ineffective mismatches of supply chain strategy to product characteristics.
Purpose
In the twenty‐first century business scenario, most organisations supply a range of products to multiple markets, so participate in several often quite different supply chains. Just as the linear chain is a simplification of a supply network, the single channel is a simplification of the true complexity many organisations face. When all products are pushed down a single channel, they are paced by the slowest and customers are charged an average price resulting in many being underserved. Manifestly, for most businesses “one size fits all” is not a viable option in delivery pipeline design and operation. This paper aims to address these issues.
Design/methodology/approach
Traditionally, the requisite number of delivery pipelines operated by a business is determined by “hunch”, as is the range of products flowing down each channel to the marketplace. The information technology (IT) revolution, which in turn has spawned the “analytic corporation” enables pipeline selection and product matching to be placed on a more formal footing. In order to enable the tailoring of value stream pipelines to markets five classification variables are proposed. These are duration of product life cycle ( D ), delivery window ( W ), annual volume (or value) ( V 1 ), product variety ( V 2 ) and demand variability ( V 3 ).
Findings
Through the use of case studies drawn from real‐world situations, the authors are able to highlight the practical value of using appropriate taxonomies to identify appropriate supply chain design strategies. A framework for the implementation of a scheme for value stream classification is proposed.
Practical implications
The proposed analysis, design, and implementation methodology is summarised in flow diagram format. Emphasis is placed on the formation of a “natural group” task force to execute this programme. Production, sales, logistics, and marketing are all essential “players” therein.
Originality/value
It is established that the DWV ³ classification system is suitable for framing pipeline performance improvement programmes. By exploiting the “analytic corporation” IT capability, the system has a dynamic role in determination of channel switching as products progress through their individual life cycle phases.
The issues underlying the appropriate matching of pipelines to marketplace needs under conditions of demand volatility and price pressure are examined. The authors describe a scenario where the philosophy of “one size fits all” does not apply to pipeline design, implementation, and control. Research conducted by the authors and others is used to show how appropriate global supply chain strategies can be developed contingent upon market characteristics and which seek simultaneously to achieve higher levels of customer responsiveness at less total cost to the supply chain as a whole. The selection of the right strategy within a supply chain lends itself to a taxonomic approach. We find that three dimensions (leading to eight possible configurations) are adequate for this purpose. These key dimensions are product characteristics, demand characteristics and replenishment lead-time.
Purpose
The purpose of this paper is to address the increasingly important question of supply chain design for global operations. With the rise of off‐shore sourcing and the simultaneous need for improved responsiveness to customer demand, the choice of supply chain strategy is critical.
Design/methodology/approach
The paper draws its conclusions from case‐based research supported by survey data.
Findings
The paper provides evidence that the choice of supply chain strategy should be based upon a careful analysis of the demand/supply characteristics of the various product/markets served by a company. It presents the basis for a taxonomy of appropriate supply chain strategies.
Research limitations/implications
The case studies and empirical research reported in this paper are specific to the clothing manufacturing and fashion industries and there would be benefit in extending the research into other sectors.
Practical implications
Given the increasing trend to out‐sourcing and off‐shore sourcing, the choice of supply chain strategy is of some significance and clearly impacts competitive performance.
Originality/value
Whilst there is a growing recognition of the need to match the supply chain to the market, there is still limited research into what criteria should be utilised to aid the choice of supply chain strategy. This paper attempts to extend our understanding of the issues.
This article challenges Fixed Effects (FE) modeling as the ‘default’ for time-series-cross-sectional and panel data. Understanding different within and between effects is crucial when choosing modeling strategies. The downside of Random Effects (RE) modeling— correlated lower-level covariates and higher-level residuals—is omitted-variable bias, solvable with Mundlak’s (1978a) formulation. Consequently, RE can provide everything that FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Pluemper and Troeger’s FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions and complex variance functions. In the appendix, an empirical example shows that disregarding these extensions can produce misleading results. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context/heterogeneity, modeled using RE. The implications extend beyond political science to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.
See also subsequent publication https://www.researchgate.net/publication/299604336_Fixed_and_Random_effects_models_making_an_informed_choice
Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative).
A central tenet in the theory of lean production is that the implementation of lean practices will reduce waste and thereby decrease costs. However, not all lean implementations have produced such results. Apparently, this effect is moderated by several factors, potentially even to the point of reversal. It is important to increase our understanding of how this might occur. In this paper, we explore how novelty, complexity, instability, and buffering affect the relationship between lean implementation and production costs. An interest in these factors drew us to study the case of Lockheed Martin's production system for the F-22, an extremely complex and innovative product. To build theory, we synthesize our empirical data from the case with other existing theory, such as theories of learning and complexity. Through this analysis, we develop a revised framework that reconceptualizes the effect of lean on production costs and use it to develop 11 propositions to direct further research. Included among these are propositions about how the timing, scale, and extent of lean implementation can regulate the benefits of lean. Furthermore, when the objective of lean is construed as the provision of value, we propose that this value is an emergent property of a complex process, different from the mere sum of the values provided by its constituent tasks. Therefore, the elimination of tasks will not guarantee cost reduction, and lean may provide even greater value by incorporating some aspects of agile manufacturing. Overall, we develop a fuller range of the effects of lean practices on production costs and illuminate how operations managers might control key variables to draw greater benefits from lean implementation.
Due to the inherently hierarchical nature of organizations, data collected in organizations consist of nested entities. More specifically, individuals are nested in work groups, work groups are nested in departments, departments are nested in organizations, and organiza-tions are nested in environments. Hierarchical linear models provide a conceptual and statistical mechanism for investigating and drawing conclusions regarding the influence of phenomena at different levels of analysis. This introductory paper: (a) discusses the logic and rationale of hierarchical linear models, (b) presents a conceptual description of the estimation strategy, and (c) using a hypothetical set of research questions, provides an overview of a typical series of multi-level models that might be investigated. Hierarchically ordered systems are an integral and defining aspect of organiza-tions. For example, Hall (1987) defined organizations as follows: An organization is a collectivity with a relatively identifiable bound-ary, a normative order, ranks of authority, communication systems, and membership-coordinating systems; this collectivity exists on a relatively continuous basis in an environment and engages in activities that are usually related to a set of goals; the activities have outcomes for organizational members, the organization itself, and for society. (P. 40).
Customer loyalty is an important strategic objective for all managers. Research has investigated the relationship between customer satisfaction and loyalty in various contexts. However, these predominantly cross-sectional studies have focused on customer retention as the primary measure of loyalty. There has been little investigation into the impact on share of wallet. Using data from the Canadian banking industry, this research aims to (1) provide the first longitudinal examination of the impact of changes in customer satisfaction on changes in share of wallet and (2) determine the moderating effects of customer age, income, education, expertise, and length of relationship. Data from 4319 households using 12,249 observations over a five-year period indicate a positive relationship between changes in satisfaction and share of wallet. In particular, the initial satisfaction level and the conditional percentile of change in satisfaction significantly correspond to changes in share of wallet. Two variables, income and length of the relationship, negatively moderate this relationship. Other demographic and situational characteristics have no impact.
The order penetration point (OPP) defines the stage in the manufacturing value chain, where a particular product is linked to a specific customer order. Different manufacturing environments such as make-to-stock (MTS), assemble-to-order (ATO), make-to-order (MTO) and engineer-to-order all relate to different positions of the OPP. These may be considered as product delivery strategies, having different implications for manufacturing objectives such as customer service, manufacturing efficiency and inventory investment. Furthermore, the OPP may differ between products and over time for a particular manufacturing firm. In this paper, the positioning of the OPP is treated from a strategic perspective. Market, product, and production factors are identified that affect the OPP positioning and the shifting of the OPP upstream or downstream in the manufacturing value chain. The major factors are demand volume and volatility, and the relationship between delivery and production lead times. These factors are included in a model that allows the manufacturing firm to choose the right product delivery strategy. Different manufacturing strategies must be developed for pre-OPP operations (i.e. upstream; forecast-driven) vs. post-OPP operations (i.e. downstream, customer-order-driven), since these two stages are fundamentally different. As a consequence, a manufacturing firm that has an ATO product delivery strategy must differentiate between MTS operations (upstream the OPP) and MTO operations (downstream the OPP). For example, the competitive priorities differ: price for pre-OPP operations but delivery speed and flexibility for post-OPP operations. Therefore, decision categories, such as production planning and control, and performance measurement must be designed accordingly. Guidelines are provided for this strategic choice.
In this paper, we examine the problem of endogeneity in the context of operations management research. Whereas the extant literature has focused primarily on the statistical aspect of the problem, a comprehensive treatment requires an examination of theoretical and pragmatic considerations as complements. The prevailing problem with the focus on statistical techniques is that the standards tend to be derived from idealizations: the correlation between a regressor and a disturbance term must be exactly zero, or the analysis will be invalid. In actual empirical research settings, such a knife-edge assumption can never be satisfied, indeed it cannot even be directly tested. Idealizations are useful in helping us understand what it would take to eliminate endogeneity, but when applied directly and unconditionally, they lead to unreasonable standards that may unnecessarily stifle substantive inquiry. We believe that it is far more productive and meaningful to ask: “What can we realistically expect empirical scientists to be able to achieve?” To this end, we cover and revisit some of the general technical material on endogeneity, paying special attention to the idiosyncrasies of operations management research and what could constitute reasonable criteria for addressing endogeneity in empirical operations management studies.
Purpose
The purpose of this paper is twofold. First, the authors provide a survey of operations management (OM) research applications of traditional hierarchical and nonhierarchical clustering methods with respect to key decisions that are central to a valid analysis. Second, the authors offer recommendations for practice with respect to these decisions.
Design/methodology/approach
A coding study was conducted for 97 cluster analyses reported in six OM journals during the period spanning 1994-2015. Data were collected with respect to: variable selection, variable standardization, method, selection of the number of clusters, consistency/stability of the clustering solution, and profiling of the clusters based on exogenous variables. Recommended practices for validation of clustering solutions are provided within the context of this framework.
Findings
There is considerable variability across clustering applications with respect to the components of validation, as well as a mix of productive and undesirable practices. This justifies the importance of the authors’ provision of a schema for conducting a cluster analysis.
Research limitations/implications
Certain aspects of the coding study required some degree of subjectivity with respect to interpretation or classification. However, in light of the sheer magnitude of the coding study (97 articles), the authors are confident that an accurate picture of empirical OM clustering applications has been presented.
Practical implications
The paper provides a critique and synthesis of the practice of cluster analysis in OM research. The coding study provides a thorough foundation for how the key decisions of a cluster analysis have been previously handled in the literature. Both researchers and practitioners are provided with guidelines for performing a valid cluster analysis.
Originality/value
To the best of the authors’ knowledge, no study of this type has been reported in the OM literature. The authors’ recommendations for cluster validation draw from recent studies in other disciplines that are apt to be unfamiliar to many OM researchers.
After examining evidence from asymptotic analyses and simulation experiments, MacKinnon comments that he
would be very surprised to encounter a bootstrap test that did not work well in the context of a single-equation regression model … ,provided the regressors are exogenous or predetermined and the underlying error terms are homoskedastic and serially uncorrelated (MacKinnon, 2002, p. 625).
Many of the new pressures from today's manufacturing environment are turning manufacturing managers' attention to the virtues of developing a flexible manufacturing function. Flexibility, however, has different meanings for different managers and several perfectly legitimate alternative paths exist towards flexible manufacturing. How managers in ten companies view manufacturing flexibility in terms of how they see the contribution of manufacturing flexibility to overall company performance; what types of flexibility they regard as important; and what their desired degree of flexibility is. The results of the investigations in these ten companies are summarised in the form of ten empirical “observations”. Based on these “observations” a check‐list of prescriptions is presented and a hierarchical framework developed into which the various issues raised by the “observations” can be incorporated.
The main purpose of this research was to build on Fisher's (1997) seminal article. First, it expands on the Fisher framework by empirically establishing the link between the firm's environmental characteristics and the firm's level of supply chain fit (SCF). The results indicate a negative relationship between environmental uncertainty and SCF indicating that, as the level of environmental uncertainty increases it becomes increasingly challenging to match a product's supply and demand characteristics with its supply chain design characteristics. Second, the key contribution of this research is the introduction of supply chain agility as a capability that helps mitigate the negative relationship between aspects of environmental uncertainty and SCF. Finally, this research contributes to theory development and managerial practice by exploring the complex relationship between SCF, supply chain agility, and financial performance. Developing high levels of supply chain agility and SCF requires deployment of resources for the focal firm. This manuscript provides a better understanding of how such expenditures can generate financial benefits for organizations.
In this article, we evaluate the relationship between supply chain design decisions and supply chain disruption risk. We explore two supply chain design strategies: (i) the dispersion of supply chain partners to reduce supply chain disruption risk versus (ii) the co-location of supply chain partners to reduce supply chain disruption risk. In addition, we assess supply chain disruption risk from three perspectives: the inbound material flow from the supplier (supply side), the internal production processes (internal), and the outbound material flow to the customer (customer side) as a disruption can occur at any of these locations. We measure disruption risk in terms of stoppages in flows, reductions in flow, close calls (disruptions that were prevented at the last minute), disruption duration (time until normal operation flow was restored), and the spread of disruptions all the way through the supply chain. We use seemingly unrelated regression (SUR) to analyze our data, finding that lead times, especially supply side lead times, are significantly associated with higher levels of supply chain disruption risk. We find co-location with suppliers appears to have beneficial effects to the reduction of disruption duration, and, overall supply side factors have a higher impact when it comes to supply chain disruption risk than comparable customer side factors.
One challenge in using multilevel models is determining how to report the amount of explained variance. In multilevel models, explained variance can be reported for each level or for the total model. Existing measures have been based primarily on the reduction of variance components across models. However, these measures have not been reported consistently because they have some undesirable properties. The present study is one of the first to evaluate the accuracy of these measures using Monte Carlo simulations. In addition, a measure based on the full partitioning of variance in multilevel models was examined. With the exception of the Level 2 explained variance measure, all other measures performed well across our simulated conditions.
A This paper proposes a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters. The proposed model is useful for situations where the variable of interest is continuous and restricted to the interval (0, 1) and is related to other variables through a regression structure. The regression parameters of the beta regression model are interpretable in terms of the mean of the response and, when the logit link is used, of an odds ratio, unlike the parameters of a linear regression that employs a transformed response. Estimation is performed by maximum likelihood. We provide closed-form expressions for the score function, for Fisher's information matrix and its inverse. Hypothesis testing is performed using approximations obtained from the asymptotic normality of the maximum likelihood estimator. Some diagnostic measures are introduced. Finally, practical applications that employ real data are presented and discussed.
Fisher (1997), in his seminal article, has argued that efficient supply chain (SC) strategies are appropriate for functional products and responsive strategies are needed for “innovative” products. The purpose of this article is to review articles which were published after Fisher (1997), and which relate market demand for a product to its supply chain design, in an effort to synthesise these articles and suggest future research directions. Literature review was carried out following the methodology of content analysis. Both deductive and inductive coding has been carried out, followed by a descriptive analysis and a synthesis of the literature. The contribution of the article is a critical review, a synthesis of the literature, and directions for future research.
Today's supply chain managers have been bombarded with a wide variety of the socalled leading-edge supply chain strategies. New terminologies and initiatives are being developed constantly. However not all these initiatives or strategies are appropriate for all firms. Companies need to first understand the uncertainties faced by the demand and supply of its products and then try to match these uncertainties with the right supply chain strategies. Based on an analysis of the uncertainties of supply and demand faced by the firm, this article develops a framework that can assist managers in developing the right supply chain strategy for their products.
Under certain conditions aggregate-level data provide unbiased estimates of individual-level relationships. Here I present these conditions in the form of a single theoretical decision rule: bias is absent when, and only when, the group mean of the independent variable (X) has no effect on Y, with X controlled. This paper introduces this rule, demonstrates it for the general n-variable case, compares it with prior discussions of cross-level inference, and illustrates it with the 1930 census data used by Robinson (1950). The final section discusses the implications of this rule for the converse type of cross-level inference: the use of individual-level data to estimate aggregate-level relationships.
Traditionally, researchers have claimed agility as an attribute closely tied to the effectiveness of strategic supply chain management. Because of its association with customer effectiveness, some researchers have considered agility to be fundamentally different from lean, which has been linked to cost efficiency (Goldsby et al., 2006). Therefore, the relationship between agility and cost efficiency is not clear due to limited empirical scrutiny from researchers. Since elimination of waste is the cornerstone of lean, unravelling the relationship between agility and efficiency can also offer a better perspective on relationship between the fundamental paradigms of agility and lean. The manuscript makes a key contribution to the agility literature by examining the association between supply chain agility (FSCA), cost efficiency and customer effectiveness across various environmental situations. We use archival data to examine the moderating effects of environmental munificence, dynamism, and complexity. It has been argued that firms should embrace agile strategies when operating in highly uncertain environments, and embrace lean strategies when operating in more stable environments (Lee, 2002 and Sebastiao and Golicic, 2008). We empirically question this premise to determine whether supply chain agility can also lead to superior performance for firms operating in stable environments. The study results also provide a better understanding of how FSCA contributes to firm financial performance. We evaluate the impact of FSCA on the firm's Return on Assets using archival data from the Compustat database. Thus, we provide evidence to managers that deploying resource to enhance FSCA can positively impact the firm's bottom line.
Supply-chain management is a complex process because of the many uncertainties it involves. The uncertainty associated with inter-organizational coordination comes about when the activities of supply-chain participants are not in harmony. Recent developments in e-commerce have contributed to Internet-based solutions to this problem. Applying e-commerce solutions to the supply chain can increase the efficiency of coordination and resource integration among partners. Because the effectiveness of e-commerce systems depends on whether the problems that disrupt supply-chain integration can be overcome, diagnosing supply-chain problems is important in deploying e-commerce solutions. This study uses a structural approach to measure supply-chain uncertainty. The results produce a validated uncertainty scale that can help in diagnosing supply-chain problems. The suggested uncertainty constructs can be incorporated into a performance-evaluation scheme for e-commerce systems that can help in monitoring and assessing supply-chain performance to ensure that the objective of supply-chain integration is met.
Preface PART I: TESTS FOR LINEAR REGRESSION MODELS Introduction Tests for the Classical Linear Regression Model Tests for Linear Regression Models Under Weaker Assumptions: Random Regressors and Non-Normal IID Errors Tests for Generalized Linear Regression Models Finite-Sample Properties of Asymptotic Tests Non-Standard Tests for Linear Regression Models Summary and Concluding Remarks PART II: SIMULATION-BASED TESTS: BASIC IDEAS Introduction Some Simple Examples of Tests for IID Variables and Key Concepts Simulation-Based Tests for Regression Models Asymptotic Properties of Bootstrap Tests The Double Bootstrap Summary and Concluding Remarks PART III: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME STANDARD CASES Introduction A Monte Carlo Test of the Assumption of Normality Simulation-Based Tests for Heteroskedasticity Bootstrapping F Tests of Linear Coefficient Restrictions Bootstrapping LM Tests for Serial Correlation in Dynamic Regression Models Summary and Concluding Remarks PART IV: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH IID ERRORS: SOME NON-STANDARD CASES Introduction Bootstrapping Predictive Tests Using Bootstrap Methods with a Battery of OLS Diagnostic Tests Bootstrapping Tests for Structural Breaks Summary and Conclusions PART V: BOOTSTRAP METHODS FOR REGRESSION MODELS WITH NON-IID ERRORS Introduction Bootstrap Methods for Independent Heteroskedastic Errors Bootstrap Methods for Homoskedastic Autocorrelated Errors Bootstrap Methods for Heteroskedastic Autocorrelated Errors Summary and Concluding Remarks PART VI: SIMULATION-BASED TESTS FOR REGRESSION MODELS WITH NON-IID ERRORS Introduction Bootstrapping Heteroskedasticity-Robust Regression Specification Error Tests Bootstrapping Heteroskedasticity-Robust Autocorrelation Tests for Dynamic Models Bootstrapping Heteroskedasticity-Robust Structural Break Tests with an Unknown Breakpoint Bootstrapping Autocorrelation-Robust Hausman Tests Summary and Conclusions PART VII: Simulation-Based Tests for Non-Nested Regression Models Introduction Asymptotic Tests for Models with Non-Nested Regressors Bootstrapping Tests for Models with Non-Nested Regressors Bootstrapping the LLR Statistic with Non-Nested Models Summary and Concluding Remarks PART VIII: EPILOGUE Bibliography Author Index Subject Index
In recent years, it has become widely accepted that optimal supply chain design depends on the type of product the chain is catering to. Moreover, as opposed to carrying one product type only, firms deliver a variety of both functional and innovative products in parallel, complicating product-to-supply chain alignment. In this manuscript, we analyze in detail a firm's optimal supply chain portfolio as a function of its product portfolio. Our results indicate that supply chain and product portfolio alignment holds the potential for tremendous cost savings. Due to the large number of factors influencing the optimal setup, however, profound quantitative analysis is required to exploit its full potential.
Increasingly manufacturers implement lean practices to improve operational performance. In addition, manufacturers operate in ever more complex and volatile environments. This research investigates the effects of environmental complexity and dynamism on lean operations and lean purchasing practices. It empirically examines these relationships using archival and survey data from 126 manufacturers. The results show that environmental complexity positively moderates the effects of lean operations and lean purchasing on performance. However, environmental dynamism reduces the benefits of lean operations on performance, but enhances the benefits of lean purchasing on performance. Robustness tests further confirm the contingent effects of complexity and dynamism on lean operations and lean purchasing. This research offers a more nuanced understanding of the effect of external environmental context on lean practices, and suggests that practitioners should carefully consider the external environment when implementing different types of lean practices.
The bottom-line financial impact of supply chain management has been of continuing interest. Building on the operations strategy literature, Fisher's (1997) conceptual framework, a survey of 259 U.S. and European manufacturing firms, and secondary financial data, we investigate the relationship between supply chain fit (i.e., strategic consistencies between the products’ supply and demand uncertainty and the underlying supply chain design) and the financial performance of the firm. The findings indicate that the higher the supply chain fit, the higher the Return on Assets (ROA) of the firm, and that firms with a negative misfit show a lower performance than firms with a positive misfit.
Many enterprises have pursued the lean thinking paradigm to improve the efficiency of their business processes. More recently, the agile manufacturing paradigm has been highlighted as an alternative to, and possibly an improvement on, leanness. In pursuing such arguments in isolation, the power of each paradigm may be lost, which is basically that agile manufacturing is adopted where demand is volatile, and lean manufacturing adopted where there is a stable demand. However, in some situations it is advisable to utilize a different paradigm on either side of the material flow de-coupling point to enable a total supply chain strategy. This approach we have termed the Leagile Paradigm. This paper therefore considers the effect of the marketplace environment on strategy selection to ensure optimal supply chain performance. Real-world case studies in the mechanical precision products, carpet making, and electronic products market sectors demonstrate the new approach to matching supply chain design to the actual needs of the marketplace.
Purpose
– This paper aims to go some way towards addressing the dearth of research into performance measurement systems and metrics of supply chains by critically reviewing the contemporary literature and suggesting possible avenues for future research.
Design/methodology/approach
– The article provides a taxonomy of performance measures followed by a critical evaluation of measurement systems designed to evaluate the performance of supply chains.
Findings
– The paper argues that despite considerable advances in the literature in recent years, a number of important problems have not yet received adequate attention, including: the factors influencing the successful implementation of performance measurement systems for supply chains; the forces shaping their evolution over time; and, the problem of their ongoing maintenance.
Originality/value
– The paper provides a taxonomy of measures and outlines specific implications for future research.
Purpose
– Aims to identify the importance of segmentation both as part of the network design process and as an operational tool for correctly allocating products to appropriate supply chains.
Design/methodology/approach
– The allocation is based upon a wide range of possible factors relating to the characteristics of the product, to the market, to the source and to the geographic/commercial context. The application of this framework is presented in a case study of a global electronics company, where large costs savings were achieved through the segmentation of supply chains.
Findings
– A logical basis for segmentation is derived and an operational framework developed, which highlights the importance of product value density (PVD), throughput volume and product availability.
Originality/value
– Demonstrates the paramount importance of throughput, demand variability/service factor and PVD as the key drivers in the segmentation process.
Bief-the core idea The Idea in Practice-p utting the idea to work 3 What Is the Righr Supply ChaiD fo. your product? 15 Further Readilg A iist ofrelated materials, with annotations to guide further exploration of the articlds ideas and applications Product 8509 Are you frequently saddledwith exces5 in-ventory? Do yoir suffer poduct sholtages that have cultorner5 leaving sioles in a hufi Do these supplychain headaches pet' ti{ despite your investments in technolo-gies such as alnornated walehousing and Gpid logirtics? lf so, you rnay be using the wrong supply chain forthe type of poduct you sell.sup-pose your offering is funational*it sati{ies basic,unchanging needt and has a long life cycle, Iow margins. and stable demand. {Think paper towels or lighl bulbs) In this case, you need an efRcienl 5u pply chain-which minimizes productron, transpona-tion, and Storaqe coJts. BLn what il your product is mnovolive-il has great vanety, a short life cycle, high profit rlargins,and volatile denEnd? (A line oflaptopswhh a lange of novel features is one example) Forthis offering, you require a responsive supplychain. Fast and flexi' ble, i helps)lcu rnanage uncenainty through strategiet trJch as cutting lead tirnes and establishinq inventory or excess-capacny buffers. Design the right supplychain for your prod_ uct, and youl profrt! soal Forexample, by building responsiveness into lls chain, inno-vative skrwear comFEny Spon obetrmyet Rduced t5 o/er-Bnd underpoduc(ion .o!ts by half-boosling prcfits 60%.
Contemporary strategies in operations management suggest that successful firms align supply chain assets with product demand characteristics in order to exploit the profit potential of product lines fully. However, observation suggests that supply chain assets often are longer lived than product line decisions. This suggests that alignment between supply chain assets and demand characteristics is most likely to occur at the time of initial market entry. This article examines the association between product demand characteristics and the initial investment in a supply chain at the time of market entry. We characterize supply chains as responsive or efficient. A responsive supply chain is distinguished by short production lead-times, low set-up costs, and small batch sizes that allow the responsive firm to adapt quickly to market demand, but often at a higher unit cost. An efficient supply chain is distinguished by longer production lead-times, high set-up costs, and larger batch sizes that allow the efficient firm to produce at a low unit cost, but often at the expense of market responsiveness. We hypothesize that a firm's choice of responsive supply chain will be associated with lower industry growth rates, higher contribution margins, higher product variety, and higher demand or technological uncertainty. We further hypothesize that interactions among these variables either can reinforce or can temper the main effects. We report that lower industry growth rates are associated with responsive market entry, but this effect is offset if growth occurs during periods of high variety and high demand uncertainty. We report that higher contribution margins are associated with responsive market entry and that this effect is more pronounced when occurring with periods of high variety. Finally, we report that responsive market entry also is correlated positively with higher technological demand uncertainty. These results are found using data from the North American mountain bike industry.
Cluster analysis is a statistical technique that sorts observations into similar sets or groups. The use of cluster analysis presents a complex challenge because it requires several methodological choices that determine the quality of a cluster solution. This paper chronicles the application of cluster analysis in strategic management research, where the technique has been used since the late 1970s to investigate issues of central importance. Analysis of 45 published strategy studies reveals that the implementation of cluster analysis has been often less than ideal, perhaps detracting from the ability of studies to generate knowledge. Given these findings, suggestions are offered for improving the application of cluster analysis in future inquiry.
Research on satisfaction, loyalty and word of mouth (WOM) behaviour has made considerable progress within recent years, but important aspects remain neglected. In this paper, it is argued that, within the customer base of service providers, certain groups can be identified which differ from each other with regards to these variables. Hypotheses are developed and tested in a sample of 765 clients of a large German energy provider. The results show that recently acquired customers (switchers) differ from long-term customers (stayers), and that switchers acquired through customer referrals differ from switchers recruited through advertising or direct mail in their satisfaction, loyalty and WOM behaviour. The paper ends with some important implications for management and future research. Copyright © 2004 Henry Stewart Publications Ltd.
Supply chain disruptions and the associated operational and financial risks represent the most pressing concern facing firms that compete in today's global marketplace. Extant research has not only confirmed the costly nature of supply chain disruptions but has also contributed relevant insights on such related issues as supply chain risks, vulnerability, resilience, and continuity. In this conceptual note, we focus on a relatively unexplored issue, asking and answering the question of how and why one supply chain disruption would be more severe than another. In doing so, we argue, de facto, that supply chain disruptions are unavoidable and, as a consequence, that all supply chains are inherently risky. Employing a multiple-method, multiple-source empirical research design, we derive novel insights, presented as six propositions that relate the severity of supply chain disruptions (i) to the three supply chain design characteristics of density, complexity, and node criticality and (ii) to the two supply chain mitigation capabilities of recovery and warning. These findings not only augment existing knowledge related to supply chain risk, vulnerability, resilience, and business continuity planning but also call into question the wisdom of pursuing such practices as supply base reduction, global sourcing, and sourcing from supply clusters.
Tel : +44 (0)1234 751122 Fax : +44 (0)1234 752158 e-mail : m.g.christopher@cranfield.ac.uk Journal of Business Logistics, Vol 26, No. 2, 2005, pp 73-96 2 Authors James Aitken spent the first 12 years of his industrial/commercial experience involved in all aspects of the supply chain ranging from the installation of a manufacturing postponement facility to the establishment of a pan-European transport and warehousing network. He completed his PhD at Cranfield University on the subject of supplier associations. In 2000 he opened a new agile manufacturing facility to specialise in bespoke products. Dr Aitken now operates as an independent consultant specialising in lean and agile manufacturing supply chains. Paul Childerhouse is a senior lecturer at the University of Waikato, New Zealand. He obtained a PhD from Cardiff University whilst a researcher in the Logistics Systems Dynamics Group. His research interests include enabling supply chain change and supply chain classification. He has first-hand industrial experience of the automotive, aerospace, dairy, construction and retail sectors.
Supply chain management has become one of the most popular approaches to enhance the global competitiveness of business corporations today. Firms must have clear strategic thinking in order to effectively organize such complicated activities, resources, communications, and processes. An emerging body of literature offers a framework that identifies three kinds of supply chain strategies: lean strategy, agile strategy, and lean/agile strategy based on in-depth case studies. Extant research also suggests that supply chain strategies must be matched with product characteristics in order for firms to achieve better performance. This article investigates supply chain strategies and empirically tests the supply chain strategy model that posits lean, agile, and lean/agile approaches using data collected from 604 manufacturing firms in China. Cluster analyses of the data indicate that Chinese firms are adopting a variation of lean, agile, and lean/agile supply chain strategies identified in the western literature. However, the data reveal that some firms have a traditional strategy that does not emphasize either lean or agile principles. These firms perform worse than firms that have a strategy focused on lean, agile, or lean/agile supply chain. The strategies are examined with respect to product characteristics and financial and operational performance. The article makes significant contributions to the supply chain management literature by examining the supply chain strategies used by Chinese firms. In addition, this work empirically tests the applicability of supply chain strategy models that have not been rigorously tested empirically or in the fast-growing Chinese economy.
Using data from the U.S. bicycle industry, we examine the relation among product variety, supply chain structure, and firm performance. Variety imposes two types of costs on a supply chain: production costs and market mediation costs. Production costs include, among other costs, the incremental fixed investments associated with providing additional product variants. Market mediation costs arise because of uncertainty in product demand created by variety. In the presence of demand uncertainty, precisely matching supply with demand is difficult. Market mediation costs include the variety-related inventory holding costs, product mark-down costs occurring when supply exceeds demand, and the costs of lost sales occurring when demand exceeds supply. We analyze product variety at the product attribute level, noting that the relative impact of variety on production and market mediation costs depends to a large extent on the attribute underlying the variety. That is, some types of variety incur high production costs and some types of variety incur high market mediation costs. We characterize supply chain structure by the degree to which production facilities are scale-efficient and by the distance of the production facility from the target market. We hypothesize that firms with scale-efficient production (i.e., high-volume firms) will offer types of variety associated with high production costs, and firms with local production will offer types of variety associated with high market mediation costs. This hypothesis implies that there is a coherent way to match product variety with supply chain structure. Empirical results suggest that firms which match supply chain structure to the type of product variety they offer outperform firms which fail to match such choices.
Numerous papers in finance study the conditional mean of some proportion or fraction with a mass point at zero. We argue that most, if not all, of these studies use mis-specified statistical models, especially when firms or individuals choose to not do something for different reasons. To address these issues, we develop a new statistical model, the zero-inflated beta model, and apply it to the analysis of corporate capital structure decisions to demonstrate its applicability.
The remarkable growth in intermodal transportation over the past two decades has not been matched by a comparable level of academic activity, especially in the context of transporting hazardous materials (hazmats). In this paper, we present a first attempt for the development of an analytical framework for planning rail–truck intermodal transportation of hazmats. A bi-objective optimization model to plan and manage intermodal shipments is developed. To represent the current practice, the routing decisions in the model are driven by the delivery-times specified by the customers. An iterative decomposition based solution methodology which takes advantage of the problem structure is provided. A realistic problem instance based on the intermodal service network in eastern US is solved. This framework is used for developing a number of managerial insights, and for generating elements of the risk-cost frontier.
This paper estimates the shareholder wealth affects of supply chain glitches that resulted in production or shipment delays. The results are based on a sample of 519 glitches announcements made during 1989–2000. Shareholder wealth affects are estimated by computing the abnormal stock returns (actual returns adjusted for industry and market-wide influences) around the date when information about glitches is publicly announced. Supply chain glitch announcements are associated with an abnormal decrease in shareholder value of 10.28%. Regression analysis is used to identify factors that influence the direction and magnitude of the change in the stock market’s reaction to glitches. We find that larger firms experience a less negative market reaction, and firms with higher growth prospects experience a more negative reaction. There is no difference between the stock market’s reaction to pre-1995 and post-1995 glitches, suggesting that the market has always viewed glitches unfavorably. Capital structure (debt–equity ratio) has little impact on the stock market’s reaction to glitches. We also provide descriptive results on how sources of responsibility and reasons for glitches affect shareholder wealth.
The paper describes the evolution of focused demand chains over an extended period of time as a major UK lighting manufacturer has sought to remain an international player in a fast changing business environment. Analysis and design procedures make use of the concepts of Wickham Skinner and Marshall Fisher to answer the strategic questions “what facilities are required and how should they be laid out to enable the necessary focused demand chains?” and to answer the tactical question “which focused demand chain is appropriate for this product?” The case study then details how the company has been transformed from operating within a traditional supply chain to driving change via the engineering of four focused demand chains. The paper concludes with a comparison of operations enablers, customer choice, and business performance metrics covering the transition period culminating in the current focused demand chain scenario. By matching products to the appropriate value stream there is a consequential reduction in product development time of 75%; manufacturing costs reduction of up to 27%; and up to 95% reduction in delivery lead times.