Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory,Resource‐Based View and Big Data Culture

The importance of big data and predictive analytics has been at the forefront of research for operations and manufacturing management. The literature has reported the influence of big data and predictive analytics for improved supply chain and operational performance, but there has been a paucity of literature regarding the role of external institutional pressures on the resources of the organization to build big data capability. To address this gap, this paper draws on the resource‐based view of the firm, institutional theory and organizational culture to develop and test a model that describes the importance of resources for building capabilities, skills and big data culture and subsequently improving cost and operational performance. We test our research hypotheses using 195 surveys, gathered using a pre‐tested questionnaire. Our contribution lies in providing insights regarding the role of external pressures on the selection of resources under the moderating effect of big data culture and their utilization for capability building, and how this capability affects cost and operational performance.     

Big data in family firms: A socioemotional wealth perspective

Since about 2010, big data analysis has drastically changed the landscape of information management by becoming a central topic in the academic literature of several fields. Despite the significant contribution of family firms to the economic fabric worldwide and their unique decision-making processes, there is a lack of research investigating big data in family-owned businesses. To address this gap, this article draws on the socioemotional wealth (SEW) perspective and its FIBER model to conceptually investigate its role in family firms’ decision to implement big data. We introduce a set of propositions and a framework linking the FIBER dimensions to the likeliness of implementing big data in family firms. Our research thus contributes to a more fine-grained understanding of the decision-making process in family firms.     

Redistributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective

Abstract

Digitalization and the growth of big data promise greater customization as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasize mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing (RDM). Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigated evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of RDM concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organizing thinking about emerging interrelationships between big data and manufacturing.     

服务供应链管理、顾客满意与企业绩效

本研究构建了服务供应链管理活动同顾客满意及企业绩效间的结构方程模型,并以来自中国民航服务业的数据进行了实证分析。结果显示服务企业的领导力不仅对服务供应链的战略管理和运作管理活动有正影响效应,还对企业服务信息系统的构建有积极影响;企业文化对企业战略层面的服务供应链管理计划、协作关系的构建、整合服务资源等服务供应链战略管理活动有显著影响;服务供应链战略管理活动、运作管理活动和顾客信息系统的构建通过有效提升顾客满意感、可以增加企业绩效。     

Big Data and Predictive Analytics in Fire Risk Using Weather Data

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—-fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R² value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R² value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.     

Big Data Analytics Capabilities and Innovation: The Mediating Role of Dynamic Capabilities and Moderating Effect of the Environment

With big data analytics growing rapidly in popularity, academics and practitioners have been considering the means through which they can incorporate the shifts these technologies bring into their competitive strategies. Drawing on the resource‐based view, the dynamic capabilities view, and on recent literature on big data analytics, this study examines the indirect relationship between a big data analytics capability (BDAC) and two types of innovation capabilities: incremental and radical. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which in turn positively impact incremental and radical innovation capabilities. To test their proposed research model, the authors used survey data from 175 chief information officers and IT managers working in Greek firms. By means of partial least squares structural equation modelling, the results confirm the authors’ assumptions regarding the indirect effect that BDACs have on innovation capabilities. Specifically, they find that dynamic capabilities fully mediate the effect on both incremental and radical innovation capabilities. In addition, under conditions of high environmental heterogeneity, the impact of BDACs on dynamic capabilities and, in sequence, incremental innovation capability is enhanced, while under conditions of high environmental dynamism the effect of dynamic capabilities on incremental innovation capabilities is amplified.     

Linking inventory efficiency,productivity and responsiveness to retail firm outperformance: empirical insights from US retailing segments

This paper establishes an empirical model linking a retail firm’s inventory management effectiveness to superior competitive operational performance for specific product-line retail segments. Using 16?years of US retail firm financial data from the COMPUSTAT Fundamentals database across 12 distinct competitive retailing segments, we develop and test a time-series model that links several inventory management execution measures to the competitive operational outperformance of retail firms. The analysis presented provides strong evidence that measures of inventory management performance are not ‘one size fits all’ for the retail industry, and helps to explain why extant research has had difficulty linking inventory control policy effectiveness to operational performance advantages in retailing. We discuss the implications of these empirical findings on the study of inventory policy execution, and offer some guidance for further research.     

Impact of customer loyalty and service operations on customer behaviour and firm performance: empirical evidence from UK retail sector

Abstract

Retail networks are striving to achieve competitive advantage by increasing value through loyalty and efficiency with a focus on service operations. As sales promotions have become an integral part of the retail supply chain planning, customer behavioural aspects based on loyalty and service operations have been challenged greatly. Subsequently, management capabilities, such as planning and timely replenishment, have become complicated tasks for many retail store managers. This study develops a model integrating retail network value and efficiencies with customer behaviour and performance. We validate the model using survey data from prominent U.K. retail store customers. Our data analysis shows that both loyalty and service operation attributes have positive significant impact on customer behaviour, while the service operation mediates the relationship between loyalty and customer behaviour. This result gives a new outlook to build managerial capability based on customer loyalty and service operations. Our results specifically show that the service operation attributes will indirectly influence the customers’ buying behaviour even in the presence of loyalty attribute such as promotion schemes. This result sends a strong signal to retail supply chain managers to offer customised promotions considering local community rather than having uniform sales promotion nationwide.     

Rethinking Resilience Analytics

The concept of “resilience analytics” has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data‐driven analytic techniques, as well as the prevalence of high‐profile natural and man‐made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber–physical–social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.     

中国成功零售企业定位点的实证研究

本文通过对全国样本的零售企业顾客满意度影响因子和评价数据的分析,对中国市场成功零售企业的定位点表现进行了实证研究,本文得出了有价值的五个结论:(1)成功的零售企业有明确的定位点;(2)成功的零售企业一般拥有1个主要、1个次好两个定位点;(3)商品、服务、价格、购物环境等,成为成功零售企业定位点选择的要素;(4)同一零售业态的成功企业可以有不同的定位点;(5)成功零售企业的非定位点都高于行业平均水平,而趋向成功的零售企业达到行业平均水平。     

Business Performance Analytics: exploring the potential for Performance Management Systems

Business Performance Analytics (BPA) entails the systematic use of data and analytical methods (mathematical, econometric and statistical) for performance measurement and management. Although potentially overcoming some traditional diagnostic issues related to Performance Management Systems (PMS), such as information overload, absence of cause-effect relationships, lack of a holistic view of the organisation, research in the field is still in its infancy. A comprehensive model for operationalising analytics for diagnostic and interactive PMS is still lacking. Adopting an action research approach, this paper addresses this gap and develops a five-step framework applied to a company operating in the construction industry. The results show that in addition to encouraging dialogue, BPA can contribute to identifying critical performance variables, potential sources of risk and related interdependencies. A number of critical issues in implementing data-based approaches are also highlighted, including data quality, organisational competences and cultural shifts.     

History,Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations

Big data and analytics (BDA) are gaining momentum, particularly in the practitioner world. Research linking BDA to improved organizational performance seems scarce and widely dispersed though, with the majority focused on specific domains and/or macro‐level relationships. In order to synthesize past research and advance knowledge of the potential organizational value of BDA, the authors obtained a data set of 327 primary studies and 1252 secondary cited papers. This paper reviews this body of research, using three bibliometric methods. First, it elucidates its intellectual foundations via co‐citation analysis. Second, it visualizes the historical evolution of BDA and performance research and its substreams through algorithmic historiography. Third, it provides insights into the field's potential evolution via bibliographic coupling. The results reveal that the academic attention for the BDA–performance link has been increasing rapidly. The study uncovered ten research clusters that form the field's foundation. While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross‐disciplinary collaborations. Moreover, the study identified several research topics undergoing focused development, including financial and customer risk management, text mining and evolutionary algorithms. The review concludes with a discussion of the implications for different functional management domains and the gaps for both research and practice.     

服务质量管理研究的回顾与现状探析

服务经济时代,服务质量管理是服务运作和服务营销的重要研究问题之一。通过对服务质量形成机制、评价及管理三个领域文献的搜集与整理发现,此类研究可归纳为由两个条件和两部分内容组成的服务质量管理模型:Gronroos的顾客感知服务质量和PZB的五差距等差距模型反映了服务质量的核心形成机制,顾客特征及心理因素是其重要影响因素;SERVQUAL作为典型的服务质量测量表,常被与模糊数学、DEMATEL法综合运用于服务质量评价与改进;服务质量管理包括以服务接触相关要素管理为主的功能质量管理和以服务质量系统改进为主的技术质量管理。此外,网络环境下,服务质量具有特殊的形成机制,决定了其评价与管理的新特点。当前,在服务科学框架下进行服务质量管理研究渐成趋势,未来研究可结合网络特征和新兴服务业,关注服务质量动态特征及科学评价方法,展开行业或城市层面的服务质量管理研究。     

Foreword: Special Issue on Retail Operations

This article draws on my field research in the retailing industry to identify the operational challenges faced by retailers and the relevance of those challenges to senior retail managers and researchers in operations management. It summarizes areas of research in retail operations that have evolved recently and are likely to be important in the future.     

Rejects reduction in a retail bank using Lean Six Sigma

Lean Six Sigma (LSS) is a majestic process improvement methodology that has been proved to be a powerful management strategy across services. The influential synergy of Six Sigma and Lean aims at improving the processes, focusing on both rapid and robust improvements, reducing waste and variation in the process. LSS generates successful results in key performance indicators (KPIs) based environments, where process data gets measured and leveraged for making essential management decisions. The aim of this paper is to highlight the importance of LSS in banking industry through a real-time process improvement study. The article establishes the literature for the need for LSS in banks detailing on customer facing metrics and process KPIs. An action-research study conducted in a retail bank is presented in LSS DMAIC methodology which reaped a benefit of INR 1.6 million and is a classic example of how LSS can bring bottom-line impact to an organisation, alongside contributing to the process improvement mind-set in employees.     

Data-driven operations management: organisational implications of the digital transformation in industrial practice

Abstract

The ongoing digital transformation on industry has so far mostly been studied from the perspective of cyber-physical systems solutions as drivers of change. In this paper, we turn the focus to the changes in data management resulting from the introduction of new digital technologies in industry. So far, data processing activities in operations management have usually been organised according to the existing business structures inside and in-between companies. With increasing importance of Big Data in the context of the digital transformation, the opposite will be the case: business structures will evolve based on the potential to develop value streams offered on the basis of new data processing solutions. Based on a review of the extant literature, we identify the general different fields of action for operations management related to data processing. In particular, we explore the impact of Big Data on industrial operations and its organisational implications.     

数据挖掘技术在CRM中的应用

客户关系管理(CRM)是数据挖掘技术的重要应用领域,也正是因为具有数据挖掘技术的支持才使CRM具有越来越广泛的市场价值和研究价值。本篇论文综述了面向CRM的数据挖掘应用的总体研究情况。包括面向CRM数据挖掘的体系结构;从客户生命周期的角度和行业应用角度分析了CRM中数据挖掘的应用状况;最后结合当前数据挖掘技术的发展指出了CRM中数据挖掘应用的进一步发展趋势以及我国在该领域的研究方向。     

华罗庚管理科学与工业大数据分析的系统工程

本文简介了攀钢顾问华罗庚教授亲自指导完成的攀枝花钒钛资源综合利用中的国家重大攻关项目成果,以“国家科技进步一等奖”中的典型大数据分析案例为例,来论述华罗庚教授所创建的管理科学理论对钢铁工业大数据分析、智能优化算法和工艺智能制造上的指导作用。通过对具体案例和算法的介绍,能够帮助我们更深刻地理解华罗庚管理科学36字方针的丰富内涵。特别是在今天数字经济时代,如何应用华罗庚管理科学理论指导工业生产线智能制造技术的研发,具有普遍的指导意义。     

Data analytics effects in major league baseball

The use of data analytics has enjoyed resurgence over the last two decades in professional sports, businesses, and the government. This resurgence is attributable to Moneyball, which exposed readers to the use of advanced baseball analytics by the Oakland Athletics, and how it has resulted in improved player selection and game management. Moreover, it changed managerial vocabulary, as the term “Moneyballing” now commonly describes organizations that use data analytics. The first research question that this study examines is whether the organizational knowledge related to baseball data analytics has provided any advantage in the competitive Major League Baseball (MLB) marketplace. The second research question is whether this strategic advantage can be sustained once this proprietary organizational knowledge becomes public. First, I identify “Moneyball” teams and executives, i.e., those who rely on baseball data analytics, and track their pay/performance over time. Next, using econometric models, I analyze whether these “Moneyball” teams and GMs, have enjoyed a pay-performance advantage over the rest of MLB, and whether this advantage persists after the information becomes public.