Real-time data processing in supply chain management: revealing the uncertainty dilemma

2019 ◽  
Vol 49 (10) ◽  
pp. 1003-1019 ◽  
Author(s):  
Sabrina Lechler ◽  
Angelo Canzaniello ◽  
Bernhard Roßmann ◽  
Heiko A. von der Gracht ◽  
Evi Hartmann
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Data Processing ◽  
Real Time ◽  
Data Science ◽  
Research Question ◽  
Time Data ◽  
Chain Management ◽  
Content Type ◽  
Real Time Data Processing

Purpose Particularly in volatile, uncertain, complex and ambiguous (VUCA) business conditions, staff in supply chain management (SCM) look to real-time (RT) data processing to reduce uncertainties. However, based on the premise that data processing can be perfectly mastered, such expectations do not reflect reality. The purpose of this paper is to investigate whether RT data processing reduces SCM uncertainties under real-world conditions. Design/methodology/approach Aiming to facilitate communication on the research question, a Delphi expert survey was conducted to identify challenges of RT data processing in SCM operations and to assess whether it does influence the reduction of SCM uncertainty. In total, 14 prospective statements concerning RT data processing in SCM operations were developed and evaluated by 68 SCM and data-science experts. Findings RT data processing was found to have an ambivalent influence on the reduction of SCM complexity and associated uncertainty. Analysis of the data collected from the study participants revealed a new type of uncertainty related to SCM data itself. Originality/value This paper discusses the challenges of gathering relevant, timely and accurate data sets in VUCA environments and creates awareness of the relationship between data-related uncertainty and SCM uncertainty. Thus, it provides valuable insights for practitioners and the basis for further research on this subject.

Customer behavior analysis using real-time data processing

2019 ◽  
Vol 31 (1) ◽  
pp. 265-290 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Fazal Ijaz ◽  
Muhammad Syafrudin ◽  
M. Alex Syaekhoni ◽  
Norma Latif Fitriyani ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Association Rule ◽  
Customer Behavior ◽  
Time Data ◽  
Content Type ◽  
The Real ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Big Data Technology

PurposeThe purpose of this paper is to propose customer behavior analysis based on real-time data processing and association rule for digital signage-based online store (DSOS). The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is utilized to handle the vast amount of customer behavior data.Design/methodology/approachIn order to extract customer behavior patterns, customers’ browsing history and transactional data from digital signage (DS) could be used as the input for decision making. First, the authors developed a DSOS and installed it in different locations, so that customers could have the experience of browsing and buying a product. Second, the real-time data processing system gathered customers’ browsing history and transaction data as it occurred. In addition, the authors utilized the association rule to extract useful information from customer behavior, so it may be used by the managers to efficiently enhance the service quality.FindingsFirst, as the number of customers and DS increases, the proposed system was capable of processing a gigantic amount of input data conveniently. Second, the data set showed that as the number of visit and shopping duration increases, the chance of products being purchased also increased. Third, by combining purchasing and browsing data from customers, the association rules from the frequent transaction pattern were achieved. Thus, the products will have a high possibility to be purchased if they are used as recommendations.Research limitations/implicationsThis research empirically supports the theory of association rule that frequent patterns, correlations or causal relationship found in various kinds of databases. The scope of the present study is limited to DSOS, although the findings can be interpreted and generalized in a global business scenario.Practical implicationsThe proposed system is expected to help management in taking decisions such as improving the layout of the DS and providing better product suggestions to the customer.Social implicationsThe proposed system may be utilized to promote green products to the customer, having a positive impact on sustainability.Originality/valueThe key novelty of the present study lies in system development based on big data technology to handle the enormous amounts of data as well as analyzing the customer behavior in real time in the DSOS. The real-time data processing based on big data technology (such as NoSQL MongoDB and Apache Kafka) is used to handle the vast amount of customer behavior data. In addition, the present study proposed association rule to extract useful information from customer behavior. These results can be used for promotion as well as relevant product recommendations to DSOS customers. Besides in today’s changing retail environment, analyzing the customer behavior in real time in DSOS helps to attract and retain customers more efficiently and effectively, and retailers can get a competitive advantage over their competitors.

scholarly journals Mapping the operations and supply chain management field: a journal governance perspective

2017 ◽  
Vol 37 (7) ◽  
pp. 898-926 ◽  
Author(s):  
Thomas F. Burgess ◽  
Paul Grimshaw ◽  
Luisa Huaccho Huatuco ◽  
Nicola E. Shaw
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Operations Management ◽  
Academic Discipline ◽  
Research Question ◽  
Knowledge Structures ◽  
Academic Journal ◽  
Chain Management ◽  
Content Type ◽  
Advisory Boards

Purpose The purpose of this paper is to address the following research question: how do the interlocking editorial advisory boards (EABs) of operations and supply chain management (OSCM) journals map out the field’s diverse academic communities and how demographically diverse is the field and its communities? Design/methodology/approach The study applies social network analysis (SNA) to web-based EAB data for 38 journals listed under operations management (OM) in the 2010 ABS Academic Journal Quality Guide. Findings The members of EABs of the 38 journals are divided into seven distinct communities which are mapped to the field’s knowledge structures and further aggregated into a core and periphery of the network. A burgeoning community of supply chain management academics forms the core along with those with more traditional interests. Male academics affiliated to the US institutions and to business schools predominate in the sample. Research limitations/implications A new strand of research is opened up connecting journal governance networks to knowledge structures in the OSCM field. OM is studied separately from its reference and associated disciplines. The use of the ABS list might attract comments that the study has an implicit European perspective – however the authors do not believe this to be the case. Practical implications The study addresses the implications of the lack of diversity for the practice of OM as an academic discipline. Social implications The confirmation of the dominance of particular characteristics such as male and US-based academics has implications for social diversity of the field. Originality/value As the first study of its kind, i.e. SNA of EAB members of OSCM journals, this study marks out a new perspective and acts as a benchmark for the future.

scholarly journals Robot-enabled execution system for perishables auction logistics

2017 ◽  
Vol 117 (9) ◽  
pp. 1954-1971 ◽  
Author(s):  
Xiang T.R. Kong ◽  
Ray Y. Zhong ◽  
Gangyan Xu ◽  
George Q. Huang
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Real Time ◽  
Food Supply ◽  
Workflow Management ◽  
Food Supply Chain ◽  
Time Saving ◽  
Chain Management ◽  
Content Type ◽  
Execution Model

Purpose The purpose of this paper is to propose a concept of cloud auction robot (CAR) and its execution platform for transforming perishable food supply chain management. A new paradigm of goods-to-person auction execution model is proposed based on CARs. This paradigm can shift the management of traditional manual working to automated execution with great space and time saving. A scalable CAR-enabled execution system (CARES) is presented to manage logistics workflows, tasks and behavior of CAR-Agents in handling the real-time events and associated data. Design/methodology/approach An Internet of Things enabled auction environment is designed. The robot is used to pick up and deliver the auction products and commends are given to the robot in real-time. CARES architecture is proposed while integrating three core services from auction workflow management, auction task management, to auction execution control. A system prototype was developed to show its execution through physical emulations and experiments. Findings The CARES could well schedule the tasks for each robot to minimize their waiting time. The total execution time is reduced by 33 percent on average. Space utilization for each auction studio is improved by about 50 percent per day. Originality/value The CAR-enabled execution model and system is simulated and verified in a ubiquitous auction environment so as to upgrade the perishable food supply chain management into a new level which is automated and real-time. The proposed system is flexible to cope with different auction scenarios, such as different auction mechanisms and processes, with high reconfigurability and scalability.

High resolution supply chain management: optimized processes based on self-optimizing control loops and real time data

2011 ◽  
Vol 5 (4) ◽  
pp. 433-442 ◽  
Author(s):  
Günther Schuh ◽  
Volker Stich ◽  
Tobias Brosze ◽  
Sascha Fuchs ◽  
Christian Pulz ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
High Resolution ◽  
Real Time ◽  
Time Data ◽  
Chain Management ◽  
Control Loops ◽  
Real Time Data ◽  
Optimizing Control

Modeling big data enablers for operations and supply chain management

2018 ◽  
Vol 29 (2) ◽  
pp. 629-658 ◽  
Author(s):  
Kuldeep Lamba ◽  
Surya Prakash Singh
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Science ◽  
Structural Modeling ◽  
Security And Privacy ◽  
Interpretive Structural Modeling ◽  
Chain Management ◽  
Content Type

Purpose The purpose of this paper is to identify and analyse the interactions among various enablers which are critical to the success of big data initiatives in operations and supply chain management (OSCM). Design/methodology/approach Fourteen enablers of big data in OSCM have been selected from literature and consequent deliberations with experts from industry. Three different multi criteria decision-making (MCDM) techniques, namely, interpretive structural modeling (ISM), fuzzy total interpretive structural modeling (fuzzy-TISM) and decision-making trial and evaluation laboratory (DEMATEL) have been used to identify driving enablers. Further, common enablers from each technique, their hierarchies and inter-relationships have been established. Findings The enabler modelings using ISM, Fuzzy-TISM and DEMATEL shows that the top management commitment, financial support for big data initiatives, big data/data science skills, organizational structure and change management program are the most influential/driving enablers. Across all three different techniques, these five different enablers has been identified as the most promising ones to implement big data in OSCM. On the other hand, interpretability of analysis, big data quality management, data capture and storage and data security and privacy have been commonly identified across all three different modeling techniques as the most dependent big data enablers for OSCM. Research limitations/implications The MCDM models of big data enablers have been formulated based on the inputs from few domain experts and may not reflect the opinion of whole practitioners community. Practical implications The findings enable the decision makers to appropriately choose the desired and drop undesired enablers in implementing the big data initiatives to improve the performance of OSCM. The most common driving big data enablers can be given high priority over others and can significantly enhance the performance of OSCM. Originality/value MCDM-based hierarchical models and causal diagram for big data enablers depicting contextual inter-relationships has been proposed which is a new effort for implementation of big data in OSCM.

Sustainable supply chain management under big data: a bibliometric analysis

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xinyi Zhang ◽  
Yanni Yu ◽  
Ning Zhang
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Bibliometric Analysis ◽  
Data Science ◽  
Sustainable Supply Chain ◽  
Chain Management ◽  
Content Type ◽  
Sustainable Supply Chain Management ◽  
Interdisciplinary Field

PurposeThis study aims to provide a literature review and bibliometric analysis of sustainable supply chain management using big data. We reviewed the literature on sustainable supply chain management under big data from 2012 to 2019 and extracted 777 articles.Design/methodology/approachWe conducted quantitative analysis and data network visualization of the chosen literature, including authors, journals, countries, research institutions and citations.FindingsWe discovered that the development of this interdisciplinary field has gained increasing popularity among researchers around the world, such as China and the US publishing the most articles and Western states having more cooperation, which indicates this research topic is growing in significance globally.Originality/valueScientific and technological revolutions such as big data have been incorporated in various industries. Modern supply chain management has also been combined with the advances in data science to achieve sustainability goals. No studies have reviewed the sustainable supply chain management based on big data. This study fills this gap.

Business management in the football industry from a supply chain management perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhaleh Memari ◽  
Abbas Rezaei Pandari ◽  
Mohammad Ehsani ◽  
Shokufeh Mahmudi
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Business Management ◽  
Relationship Management ◽  
Flow Management ◽  
Manufacturing Firm ◽  
Chain Management ◽  
Content Type ◽  
Proposed Model ◽  
Football Industry

PurposeTo understand the football industry in its entirety, a supply chain management (SCM) approach is necessary. This includes the study of suppliers, consumers and their collaborations. The purpose of this study was to present a business management model based on supply chain management.Design/methodology/approachData were collected through in-depth interviews with 12 academic and executive football experts. After three steps of open, axial and selective coding based on grounded theory with a paradigmatic approach, the data were analysed, and a football supply chain management (FSCM) was developed. The proposed model includes three managerial components: upstream suppliers, the manufacturing firm, and downstream customers.FindingsThe football industry sector has three parts: upstream suppliers, manufacturing firm/football clubs and downstream customers. We proposed seven parts for the managerial processes of football supply chain management: event/match management, club management, resource and infrastructure management, customer relationship management, supplier relationship management, cash flow management and knowledge and information flow management. This model can be used for configuration, coordination and redesign of business operations as well as the development of models for evaluation of the football supply chain's performance.Originality/valueThe proposed model of a football supply chain management, with the existing literature and theoretical review, created a synergistic outcome. This synergy is presented in the linkage of the players in this chain and interactions between them. This view can improve the management of industry productivity and improve the products quality.

Sustainable supply chain management: continuing evolution and future directions

2019 ◽  
Vol 50 (1) ◽  
pp. 122-146 ◽  
Author(s):  
Craig R. Carter ◽  
Marc R. Hatton ◽  
Chao Wu ◽  
Xiangjing Chen
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Theory Development ◽  
Future Research ◽  
Sustainable Supply Chain ◽  
Unit Of Analysis ◽  
Chain Management ◽  
Content Type ◽  
Empirical Validity ◽  
Sustainable Supply Chain Management

Purpose The purpose of this paper is to update the work of Carter and Easton (2011), by conducting a systematic review of the sustainable supply chain management (SSCM) literature in the primary logistics and supply chain management journals, during the 2010–2018 timeframe. Design/methodology/approach The authors use a systematic literature review (SLR) methodology which follows the methodology employed by Carter and Easton (2011). An evaluation of this methodology, using the Modified AMSTAR criteria, demonstrates a high level of empirical validity. Findings The field of SSCM continues to evolve with changes in substantive focus, theoretical lenses, unit of analysis, methodology and type of analysis. However, there are still abundant future research opportunities, including investigating under-researched topics such as diversity and human rights/working conditions, employing the group as the unit of analysis and better addressing empirical validity and social desirability bias. Research limitations/implications The findings result in prescriptions and a broad agenda to guide future research in the SSCM arena. The final section of the paper provides additional avenues for future research surrounding theory development and decision making. Originality/value This SLR provides a rigorous, methodologically valid review of the continuing evolution of empirical SSCM research over a 28-year time period.

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