Designing Global Manufacturing Networks Using Big Data

Gölzer, Philipp; Simon, Lothar; Cato, Patrick; Amberg, Michael

doi:10.1016/j.procir.2015.06.035

Cited by 31 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In-depth and empirical studies, especially, are required to verify the framework and to understand how companies can successfully utilize big data to achieve both use and exchange value. Biswas and Sen, 2016;Bohlouli et al, 2014;Choi et al, 2017;Fan et al, 2015;Ghosh, 2015;Gölzer et al, 2015;Hazen et al, 2016;He et al, 2013;Ittmann, 2015;Leveling et al, 2014;Lu et al, 2013;Robak and Zielonogórski, 2013;Witkowski, 2017;Zhong, Newman, et al, 2016) 25…”

Section: Discussionmentioning

confidence: 99%

Understanding the value of big data in supply chain management and its business processes

Brinch¹

2018

IJOPM

103

View full text Add to dashboard Cite

Purpose The value of big data in supply chain management (SCM) is typically motivated by the improvement of business processes and decision-making practices. However, the aspect of value associated with big data in SCM is not well understood. The purpose of this paper is to mitigate the weakly understood nature of big data concerning big data’s value in SCM from a business process perspective. Design/methodology/approach A content-analysis-based literature review has been completed, in which an inductive and three-level coding procedure has been applied on 72 articles. Findings By identifying and defining constructs, a big data SCM framework is offered using business process theory and value theory as lenses. Value discovery, value creation and value capture represent different value dimensions and bring a multifaceted view on how to understand and realize the value of big data. Research limitations/implications This study further elucidates big data and SCM literature by adding additional insights to how the value of big data in SCM can be conceptualized. As a limitation, the constructs and assimilated measures need further empirical evidence. Practical implications Practitioners could adopt the findings for conceptualization of strategies and educational purposes. Furthermore, the findings give guidance on how to discover, create and capture the value of big data. Originality/value Extant SCM theory has provided various views to big data. This study synthesizes big data and brings a multifaceted view on its value from a business process perspective. Construct definitions, measures and research propositions are introduced as an important step to guide future studies and research designs.

show abstract

Section: Discussionmentioning

confidence: 99%

Understanding the value of big data in supply chain management and its business processes

Brinch¹

2018

IJOPM

103

View full text Add to dashboard Cite

show abstract

“…As another significant data source for big data ecosystem, databases in manufacturing is data-at-rest, which represents static, historical data [159]. This data is mainly used to predict the long-term performance in production planning [62], global manufacturing network design [94], critical event detection in safety [89]. Both historical batching data and real-time streaming data are integrated to train models and monitor real time condition information such as anomaly detection of machines' energy consumption data [139].…”

Section: Driver 2: Datamentioning

confidence: 99%

Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

237

View full text Add to dashboard Cite

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business.Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware.Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security.Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.

show abstract

“…Other approaches like Gölzer et al aim to make complexity in global production networks manageable through big data analyses instead of complexity reduction [14]. A review of recent big data approaches for supply chain management is given by Tiwari et al [15].…”

Section: Discussion Of Excisting Approachesmentioning

confidence: 99%

Reduction of Decision Complexity as an Enabler for Continuous Production Network Design

Schuh¹,

Prote²,

Fränken³

et al. 2018

IFIP Advances in Information and Communication Technology

View full text Add to dashboard Cite

Today, the continuous design of production networks is challenging due to their high complexity and diverging interests of individual sites. In order to increase the efficiency and agility of global producing companies, this paper presents a method that shifts the often-present focus on locations towards a market and production process oriented design approach of production networks. By subdividing the product range into product families, the complexity of decisionmaking processes is reduced. Subsequently, target conflicts between product families are resolved systematically from an overall corporate view by deriving the smallest possible holistic decision scope. The method was applied to a machine tool manufacturer.

show abstract

Designing Global Manufacturing Networks Using Big Data

Cited by 31 publications

References 14 publications

Understanding the value of big data in supply chain management and its business processes

Understanding the value of big data in supply chain management and its business processes

Manufacturing big data ecosystem: A systematic literature review

Reduction of Decision Complexity as an Enabler for Continuous Production Network Design

Contact Info

Product

Resources

About