Data Science,Predictive Analytics,and Big Data: A Revolution That Will Transform Supply Chain Design and Management

We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.     

基于ISO9000族标准的供应链质量管理实施框架

供应链质量管理具有极其重要性,需要借鉴ISO9000中质量管理的定义来建立供应链质量管理实施框架,并且分别从供应链质量策划、供应链质量控制、供应链质量改进和供应链质量保证四个方面来系统地了解供应链中的核心企业在实施供应链质量管理时应采取的措施以及可以借助的质量管理工具。     

Data Science,Predictive Analytics,and Big Data in Supply Chain Management: Current State and Future Potential

While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large‐scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large‐scale survey, and offer insight into its future potential via the discussion of how a research university is training next‐generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.     

Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research

Increased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression‐based process for analyzing big data.     

Click Here for a Data Scientist: Big Data,Predictive Analytics,and Theory Development in the Era of a Maker Movement Supply Chain

Predictive analytics is impacting many diverse areas, ranging from baseball and epidemiology to forecasting and customer relationship management. Manufacturers, retailers, software companies, and consultants are creatively discovering new applications of big data using predictive analytics in supply chain management and logistics. In practice, predictive analytics is generally atheoretical; however, we develop a 2 × 2 model to explain the role of predictive analytics in the theory development process. This 2 × 2 model shows that in our discipline we have traditionally taken one path to theory development, but that predictive analytics can be a salient component of a comprehensive theory development process. The model points to a number of research questions that need to be addressed by our research community. These questions are not just highly relevant to the academic community but also in urgent need of answers to help practitioners execute the right strategies with greater precision and efficiency. We also discuss how one disruptive trend, the maker movement, changes the nature of who the producers are in the supply chain, making big data even more valuable. As we engage in higher levels of dialogue we will be able to make meaningful progress addressing these vital research topics.     

USER INFLUENCE ON THE RELATIONSHIP BETWEEN FORECAST ACCURACY,APPLICATION AND LOGISTICS PERFORMANCE

When companies develop and apply more accurate forecasts in their planning and management activities, they have the potential to improve performance throughout their organization and across the supply chain. To realize these improvements, however, companies must (1) implement techniques and practices that improve forecast accuracy, and (2) integrate the more accurate forecasts into their planning and management activities. While much research has focused on accomplishing the first of these requirements, few studies have investigated the important role that user's play in the application of forecasts for logistics planning and management. This article establishes a connection between forecast performance, user perceptions of the quality of forecasts they receive, the extent that they use the forecasts and the resulting impact on logistics performance.     

Exploring social media use in B2B supply chain operations

This research examines current applications and potential capabilities of a wide array of social media applications such as Facebook, Twitter, LinkedIn, and others within the context of B2B supply chain operations. Specifically, we use social media affordances (SMA) as a framework to explore how social media is used in B2B supply chain settings. We report findings based on a survey of 209 professionals in supply chain areas. These findings include the extent to which social media and different social media tools are used in B2B supply chain operations, the impact of company size on social media use, the areas in which social media is used, the perceptions of social media, and other relevant issues such as social media policy and security. Based on these findings, this study provides a discussion regarding the current and future use of social media in B2B supply chain operations.     

基于供应链的现代库存管理方法之比较研究

库存管理是供应链管理的重要内容,供应链上组织流、信息流、物流的不畅是导致供应链库存不当的重要原因。为消除供应链库存现象,要从主要思想、产生背景、优劣势、实施前提条件、适用范围、模型六个方面进行现代库存管理,即供应商库存管理、联合库存管理、合作计划预测与补给方法。     

广州市发展供应链金融的思考

通过SWOT分析,广州市发展供应链金融有利于促进经济增长和产业的升级转型,也有利于解决中小企业的融资难的问题。对于在发展供应链金融时可能会遇到各银行之间的无序竞争与供应链金融的整体发展规划不符合,各参与主体对供应链金融理解不到位等问题,广州市应制定统一的供应链金融发展战略和发展规划,完善相关的法律、法规和政策细则,要防范和化解系统性金融风险,保障供应链金融各参与方的利益,打造中小企业的融资平台,并进行有效的风险管理。     

Readiness Assessment of the construction supply chain for concurrent engineering

There is growing interest in the adoption of concurrent engineering (CE) in the construction industry. While concurrent engineering (CE) is gaining acceptance, some implementation efforts have not realised their full potential for reducing costs, reducing time, and increasing efficiency, effectiveness and performance for product development efforts. This is due in part to insufficient planning to support the implementation. One approach that has been used successfully to improve CE implementation planning is to conduct ‘readiness assessment’ of an organisation and its supply chain participants prior to the introduction of CE. This helps to investigate the extent to which they are ready to adopt concurrent engineering practices. CE readiness assessment tools and models have been developed and used in other industries such as the manufacturing and software engineering industries. This paper discusses CE and its application to the construction, reviews and compares the existing tools and methods for CE readiness assessment, discusses supply chains generally and construction supply chain specifically, and stresses the need to assess the readiness of the construction supply chain for the adoption of CE. The paper also presents a new readiness assessment model for the construction industry supply chain, and gives examples of its use to assess construction organisations.     

The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources

Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.     

供应链管理研究的若干问题

通过对供应链管理研究成果的回顾,揭示出供应链管理研究中的若干问题,在此基础上提出自己的认识。就现在所处的国际环境,对目前供应链管理的一些热点问题,如库存管理、物流系统规划、战略伙伴关系管理、绿色供应链、基于电子商务的供应链、供应链建模方法、信息支持技术及供应链绩效评估等,把它们分成三类来对其发展现状进行探讨。     

Supply Chain Game Changers—Mega,Nano, and Virtual Trends—And Forces That Impede Supply Chain Design (i.e., Building a Winning Team)

Strategic systems design is essential to structuring and governing a supply chain for competitive advantage. To effectively co‐create value, decision makers must manage the three rights of supply chain design: right players, right roles, and right relationships. Doing this well requires managers discern how the unwritten competitive rules are changing as well as determine firm readiness to compete. As part of this analysis, we briefly explore five emerging “game changers” that represent potential supply chain design inflection points: (1) Big Data and predictive analytics, (2) additive manufacturing, (3) autonomous vehicles, (4) materials science, and (5) borderless supply chains. We also consider four forces that impede transformation to higher levels of value co‐creation: (1) supply chain security, (2) failed change management, (3) lack of trust as a governance mechanism, and (4) poor understanding of the “luxury” nature of corporate social responsibility initiatives. How well managers address sociostructural and sociotechnical issues will determine firm survivability and success.     

Using Country Sustainability Risk to Inform Sustainable Supply Chain Management: A Design Science Study

The sustainability of our global supply chains is an essential concern in strategic supply chain management research. Modern information and communication technologies enable stakeholders to punish buying firms for any sustainability‐related grievances at their suppliers, even in remote locations. This study investigates how the notion of country sustainability risk can inform sustainable supply chain management, in particular with respect to sustainability risk assessment at the individual supplier level. Drawing on institutional theory, we provide insights surrounding the emergence of environmental, social, and governance‐related country‐level sustainability risks and show their implications for and application in sustainable supply chain management. The study employs a design science methodology, based on cooperation with a multidivisional German technology firm, to develop a supply chain sustainability risk (SCSR) map as technological solution design. This article contributes to the study of SCSR by reconciling the scholarly SCSR discourse with the buying firms’ pursuit of efficiency. Moreover, it elucidates the augmentation of a research agenda through a design science approach. In practical terms, the technological solution design can directly inform managers about SCSR at the country level and serves as a decision basis for the management of individual suppliers.     

Mobile Commerce Applications in Supply Chain Management

Abstract

As wireless technologies evolve, the coming mobile revolution will bring dramatic and fundamental changes to supply chain management. When complete, the revolution will impact numerous facets of organizational life. It will provide important data in real time to assist decision makers, exert great influence on the ways businesses communicate and develop relationships with consumers and suppliers, and ultimately transform the way we manage the supply chain. This paper examines issues in integrating mobile commerce and supply chain management. The paper discusses issues such as the absence of killer applications, mobile device limitations, networking problems, infrastructure constraints, security concerns, and user distrust in mobile applications. Research issues, such as usability, user interfaces, mobile access to databases, agent technologies, and mobile business models, are also discussed.     

Emerging Market Retail: Transitioning from a Product-Centric to a Customer-Centric Approach

In an environment with digital disruptions, retailers must adopt a customer-centric approach to survive and compete effectively. Retailers need to be agile and forward-looking in adopting the relevant analytics and performance metrics to bring a customer-centric approach across upstream and downstream activities in the retail value chain. However, retailers in emerging markets (EMs) need clarity on the specific analytics and performance metrics in the value chain that will enable them to transition from their current product-centric state to the desired customer-centric state. Employing a triangulation approach (i.e., literature review, marketplace evidence, and managerial interviews) in the fragmented retail landscape of EMs, this study provides an organizing framework that explains: (i) the need for a customer-centric approach across the retail value chain, (ii) the specific performance metrics that need to be adopted across upstream and downstream activities in the retail value chain to enable EM retailers to achieve their desired customer-centric state, and (iii) the role of analytics in providing insights to achieve these performance metrics and improving monetary and non-monetary firm performance outcomes. We also provide firm-specific and macro-level conditions that can influence the EM retailers’ adoption of relevant analytics and explain the different paths retail formats can follow to adopt analytics. We present a strategy matrix that enables retail managers to identify the appropriate analytics to be adopted at different retail value chain stages to achieve desired performance metrics. We also highlight future research opportunities in retailing in EMs.     

Addressing skills mismatch: Utilizing talent supply chain management to enhance collaboration between companies and talent suppliers

Talent management continues to be a topic of interest for employers who face significant challenges dealing with the uncertainty of the supply and demand of talent in organizations. In particular, employers often speak of a talent gap that exists between the skills possessed by applicants and the skills needed in organizations. Supply chain management (SCM), a field that focuses on matching product supply with consumer demand, offers several concepts and models that could apply to and help resolve issues related to the skills mismatch. In order to address this issue, we base our conceptual development on a theoretical framework used in SCM called the collaborative, planning, forecasting, and replenishment (CPFR) approach. We use this approach to develop a comprehensive model of talent supply chain management (TSCM) that applies concepts related to the field of SCM to managing the development and flow of talent. We further go on to describe how organizations can utilize TSCM to enhance connections with talent suppliers to get their labor demands fulfilled with individuals who have the necessary skills for success.     

Business analytics: Why now and what next?

Business analytics is a revolution that is impossible to miss. At its core, business analytics is about leveraging value from data. Instead of being referred to as the ‘sludge of the information age,’ data has recently been deemed ‘the new oil.’ While data can be employed for purposes such as detecting new opportunities, identifying market niches, and developing new products and services, it is also notoriously amorphous and hard to extract value from. In this Guest Editors’ Perspective, we first present a structural framework for deriving value from business analytics. Extracting value from data requires aligning strategy and desirable behaviors to business performance management in conjunction with analytic tasks and capabilities. We then introduce three special articles that provide in-depth insights regarding how business analytics is being employed in the management of healthcare, accounting, and supply chains.     

Assessing the Efficiency of Risk Mitigation Strategies in Supply Chains

Mitigating supply chain risk is a critical component of a company's overall risk management strategy. Drawing upon Contingency Theory, we posit that the appropriateness and effectiveness of risk mitigation strategies are contingent on the internal and external environments and that there is no one‐size‐fits‐all strategy. While literature on risk management has proposed a variety of tools and techniques for effectively evaluating and managing supply chain risks, comprehensive assessment of the efficiencies of alternative risk mitigation strategies has not been addressed in the literature. Such an assessment will help managers select the appropriate mitigation strategy for a given decision‐making environment. To this end, this study is first of its kind in evaluating and proposing efficient supply chain risk mitigation strategies in the presence of a variety of risk categories, risk sources, and supply chain configurations. We combine an empirically grounded simulation methodology with data envelopment analysis and nonparametric statistical methods to analyze and rank alternative mitigation strategies. We find that the more efficient strategies focus on flexibility rather than on redundancy for supply chain failures. Our research presents several interesting and useful managerial insights for deciding what strategies are most capable of mitigating risks in a variety of contexts.     

THE SUCCESSFUL CROSS‐DOCK BASED SUPPLY CHAIN

A supply chain using the cross‐dock approach can speed goods from upstream suppliers to downstream customers quickly and cost‐effectively, with benefits to the entire chain, if the appropriate type of cross‐dock is chosen and then designed, staffed and managed for effective performance. Unlike a warehouse, which offers storage, a cross‐dock is a high performance rapid transit point that does not provide storage. Although cross‐docks have been studied with some thoroughness, what has not been seen clearly is that a cross‐dock does not operate in isolation and therefore can't be optimized independently from the upstream and downstream processes. To be successful, cross‐dock optimization makes demands for high performance on all members of the supply chain in terms of speed, effective planning, high reliability, near error‐proof processes and a high degree of transparency, visibility and information‐sharing; accordingly, the article refers to this integrative perspective as a “cross‐dock based supply chain.” To make clear this interdependency between the cross‐dock and the chain based on it, a new definition that classifies types of cross‐dock based supply chains is offered. To assist scholars analyzing and industry management choosing a cross‐dock approach, cross‐dock based supply chains are classified as falling into three different types. Lastly, based on field work in industry and on literature review, nine critical success factors for cross‐dock based supply chain operations are offered.