Big Data Analytics in Supply Chain Management
Read this paper for an overview and examples of how big data is used in specific areas, such as supply chain management, risk management, and logistics of business in industry. One of the biggest issues for analysts with big data is knowing how to separate the valuable data from that which does not help answer their requirements.
Sometimes people describe intelligence as "connecting the dots", but it is rarely simple like a "paint-by-numbers" art project. The dots are not just lying around waiting to be connected. More appropriately, it has been described as filtering out the right radio signals from the fray in a huge city. You have to be carefully tuned to your requirements, which will be discussed at length in Unit 2 and again in Unit 8, as these are the guide stars that keep you on track to finding the right data to answer the questions you need to focus on.
2. Big data in manufacturing systems
2.3. Smart manufacturing, strategy development, and agile manufacturing
Big data analytics can be used in smart manufacturing to solve company problems at the speed the business requires. However, there are some organizational and technological barriers that may prevent manufacturing companies from using big data solutions to initiate a smart factory. Big data analytics has been proven to be a valuable tool for manufacturers to help them develop strategies, share data, design predictive models, and connect factories in order to control processes. A study by Bumblauskas et al. studies big data applied to designing a smart maintenance decision support system, which is shown to improve an asset's lifecycle. Liu et al. used big data analytics for routing order pickup and delivery as well as assigning orders to laundry terminals in smart laundry service enterprises. Big data applications in strategy development and agile manufacturing have also been studied by Opresnik & Taisch, Waller & Fawcett, Guha & Kumar, and Gunasekaran et al.. Ren et al. reviewed the available research in big data applications that support sustainable smart manufacturing. Agility in a manufacturing system is the capability to better deal with unpredictable events, and deal with them in a business environment that can even turn these events into benefits.
Several other studies developed quality research on using big data analytics in the manufacturing field. Some of the selected journal articles and conference proceedings are summarized in Table 1. The criterion for us consider a paper in the current review is that it must have been cited, on average, more than 10 times each year.
Table 1 High-quality articles using big data in manufacturing processes.
|
Author / Journal |
Contribution |
Study approach |
Case study (NA stands for Not Applicable) |
Future research topic(s) in the article |
|
Lee et al./ Manufacturing letters |
Studying the applications of big data in predictive manufacturing systems |
Analytical study |
NA |
-Developing systems to integrate, manage, and analyze machinery data during different stages of machine life cycle |
|
O'Donovan et al./Journal of Big Data |
Studying the requirements for implementing equipment maintenance |
Analytic field study / Simulation |
DePuy manufacturing facility in Ireland |
-Deployment of big data pipeline in DePuy |
|
-Using data pipeline to feed predictive maintenance applications |
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|
Dubey et al./The International Journal of Advanced Manufacturing Technology |
Studying the role of big data in sustainable manufacturing |
Statistical analysis / Field study |
NA |
-Using big data to redefine the focus of advanced manufacturing technology |
|
-Using big data innovations like new materials development |
||||
|
Kumar et al./International Journal of Production Research |
Solving a data imbalance problem in cloud-based manufacturing systems |
RHadoop programming / MapReduce framework |
Steel plate manufacturing company |
-Executing the dissimilar types of feature selection approaches |
|
-Improving performance of classifiers |
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|
Mourtzis et al./Procedia CIRP |
Studying the applications of the Internet of Things in developing industrial big data |
Analytic field study |
Mould-making industry |
NA |
|
Zhang et al./Journal of Cleaner Production |
Integrating big data analytics and service-driven patterns to create cleaner manufacturing and maintenance processes |
Analytical study / Product life cycle analysis |
Unnamed axial compressor manufacturer |
-Using big data analytics to work out a mathematical model to discover rules for making cleaner production decisions |
|
-Representing and visualizing knowledge gained from big data |
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|
Zhong et al./International Journal of Production Research |
Creating a RFID-enabled intelligent shop floor using the Internet of Things |
Smart manufacturing objects / Wireless network |
Unnamed collaborative company |
-Developing a mathematical model to formulate physical internet-based logistics systems |
|
-Developing a systematic procedure to examine big data analytical approaches |
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|
Gunasekaran et al./International Journal of Production Research |
Studying the role of big data in agile manufacturing |
Analytic field study |
Four organizations in United Kingdom |
-Studying the applications of the Internet of Things, Industry 4, and Blockchain technologies in developing agile manufacturing systems |
|
Moktadir et al./Computers & Industrial Engineering |
Studying the barriers to applying big data analytics in manufacturing supply chains |
Delphi-based analytic hierarchy process (AHP) / Sensitivity analysis |
Five manufacturing companies in Bangladesh |
-Using international data to examine big data analytics barriers |
|
-Utilizing the extensions of AHP method to further explore the direction of the studied research |
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|
Popovič et al./Information Systems Frontiers |
Using a qualitative approach to study the impact of big data analytics in manufacturing sector |
Comparative analytic field study |
Three manufacturing companies in Europe |
-Studying the impact of big data analytics on low-performing firms |
|
-Studying failed cases instead of successful cases |
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|
Tao et al./The International Journal of Advanced Manufacturing Technology |
Developing a method using a digital twin to design a product, manufacture, and service it |
Product life cycle analysis |
Author created applications as case |
-Digital twin data construction and management |
|
-Developing smart service analysis based on digital twin data |
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|
Dubey et al./Technological Forecasting and Social Change |
Studying the impact of big data analytics on the social performance and environmental performance of manufacturing companies |
Partial Least Squares / Hypothesis testing |
Sample of 205 manufacturing companies in India |
-Studying the exact role of the flexibility or control orientation on big data and predictive analytics on manufacturers' social and environmental performance |
|
Moktadir et al./Computers & Industrial Engineering |
Studying the critical barriers to the adoption of big data analytics in manufacturing systems |
Delphi-based analytic hierarchy process |
Five manufacturing companies in Bangladesh |
-Using international data in the same study |
|
-Using other decision-making techniques to study the interaction among barriers |
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|
(Raut et al., 2019) / Journal of cleaner production |
Using big data analytics to improve manufacturing sustainability in terms of operations management |
Structural equation modelling-artificial neural network |
Survey data from 316 Indian experts |
-Studying this same issue in other geographical locations besides India |
|
-Studying other technologies that firms may adopt for sustainable purposes |