MITS6002 Business Analytics

Introduction

The paper being reviewed informs on the role of Big Data in Supply Chain Management and entails, “Understanding Big Data Analytics Capabilities in Supply Chain Management,” written in 2018 by Arunachalam, Deepak, Niraj Kumar, and John Paul Kawalek. It is a highly articulated article that poses an interesting topic of study related to organizations and businesses in the contemporary world [1]. This article aims to offer a systematic assessment of Big Data Analytics (BDA) abilities in the supply chain.  As a result, this aspect provides a guideline towards developing a capabilities maturity model. To review and summarize this article, there must be a clear understanding of the article's purpose. The first position will speak of Big Data Analytics applications in the existing business settings [2]. Notably, data collection formulas and the variable will be expounded more. In the second part, various analytical tools would be mentioned and their benefits extracted. Most essentially, the final segment will inform on the various recommendations on improving BDA application in the supply chains.

Business Domain Analysis 

Big Data Analytics has sufficient potential to transform many fields, business being one of them. For this reason, a lot of business operations are adopting BDA, especially in the field of research. Nevertheless, the supply chain operators are yet to experience the complete and accurate potential issues, challenges, and implications that are likely to arise in adopting BDA practices. This article provides insight for experts to advance operations revolving around big data in addition to supply chain [1]. Additionally, it propositions efficient and dependable guidance via various models and tools to implement future BDA project research in supply chain management.  

In the modern world, business operations are striving to shed light on whether they can attain competitive advantages over their competitors using BDA models. It covers three key aspects of a business setting: velocity, volume, and variety, concerning operations. Business operation velocity entails the speed at which data can be collected. BDA comes into play to address emerging issues of handling this data at high speed from devices, such as sensors. Data volumes speak of how BDA aid in the many avenues of data collection, such as social media and other sources [10]. Primarily, data collected comes either in structured or unstructured formats such as texts, videos, and numerical, which are highly mixed up. BDA is used in converting these complex formats to simple ones.

The author adopts continuous quantitative statistical data as the research is based on what can be measured. It is also worth observing that the data used was collected from existing records and documents from the publishers. The article adopts both dependents that refer to factors over which the researcher has no control and independent, an influential factor variable [1]. The two share some relationship in that the independent variable is manipulated in the article to measure change on the dependent variable. For example, in the report, the top contributing authors are the dependent variable. On the other hand, several publications, citations, and affiliations are the independent variable.

Analytical Tools

Moreover, corporations have long been involved in the analysis of wanting to know their performance over time. From the article, one of the business analytics models used is descriptive analytics. It entails data mining and aggregation to provide insight into the past. In other words, it summarizes the raw data and turns them into facts that business persons can easily interpret. The model creates an opportunity for establishment to learn from their preceding performances. For instance, the model is believed to show total stock inventory and changes in sales over the years [3]. By doing this, it will enable them to acquire information that would influence the future business outcome.

Thematic analysis is also adopted as a dependable business analytic model. This model advocates for the examination and recording of the various themes within the given data. This is a critical method used by most researchers in capturing the intricacies of meanings within a set of data. In this business scenario, themes refer to specific patterns across groups of data and are crucial when describing certain occurrences [6]. Correspondingly, they are essential, especially in the process of deriving detailed research questions. Notably, to achieve better results, the analysis must be subjected to the process of coding. By way of identifying, comparing, and graphically representing theme occurrences coding’s in business operations.

Essentially, operating any kind of business calls for the ability to make decent decisions frequently. Owners of the business must realize that one wrong choice would affect the entire company. Business intelligence as a tool for business decision-making works to achieve aims various aims [4]. This tool can be identified as a business procedure of transfiguring raw meaningless data into more helpful information that can later be used to create meaningful insights in business operations. Most importantly, it provides decision-makers with a great deal of information.

 Furthermore, it helps in the provision of timely and quality information. Again, the processing of external information from external parties depends on this tool. To sum up, it is used to process transactional information in an efficient and practical approach. Another decision-making tool identified in the paper encompasses Business Analytics. This tool allows business professionals to make verdicts based on statistical facts. It refers to the procedure of transforming obtained data into functions. This is realized via scrutiny and awareness in the perspective of administrative verdict-making. For example, it is applicable in improving employee productivity and reducing employee turnover [5]. By identifying these insights, a business manager can be able to determine effective strategies. 

Recommendations 

Dynamic capabilities notion lies within the business capacity to formulate, incorporate as well as reconfigure both its internal in addition to external competencies. Data generation capabilities are an essential aspect in improving the level of management. Therefore, businesses must gather data on customers and are encouraged to go a step further in analyzing consumer’s interaction with past purchasing habits and business websites such as email tracking [7]. Adopting proper data generation capabilities would extensively allow firms their workflow operations and improve the employees' performance.

Notably, attempts by firms to improve their data integration and management are slowed by a common mistake. The mistake that emerges during the data integration process is neglecting the fact that integration plans may evolve beyond their initial integration process. Thus, it is recommended that businesses try to shift away from the thinking that integration is only a matter of selecting the right software, for instance, accommodating cloud applications [9]. They should start thinking about it as a holistic business strategy that requires firms first to understand their goals and objectives.

Likewise, the achievement of data-driven culture capabilities depends mainly on the positioning of the senior supply chain operators. A data-driven culture is all about professionals embracing the utilization of data in decision-making. For the culture to thrive, a business should start treating data as a strategic asset of the enterprise by making data accessible and available. To create a data-driven culture, top business officials must remain involved and always engage data with viable business instincts to make the right decision [8]. For example, they should get rid of the binders of unnecessary reports and spreadsheets that have been generated. Achieving data-driven culture would bring accountability and transparency to the whole firm.

Conclusion 

In conclusion, it is now clear that business starters should adopt business analytics to make accurate decisions. Decisions that are derived from this model would impact the entire firm by way of profit increase. An analytic data model would essentially enable companies to answer questions like; is the business profitable? If it is not, what is the industry doing wrong? Through business analytics, it would be easier for the business owner to know whether a customer is likely to return in the future. BDA is the most recent buzzword worldwide, considering the amount of data generated by consumers and businesses every day. The beneficial components of BDA towards the supply chain cannot be assumed. Supply chains make use of BDA and quantitative methods in enhancing their decision-making. Also, it is an important model used for a better understanding of consumer’s behavior and needs. This would be useful for making better marketing insights for future reference.

References

[1] D. Arunachalam, N. Kumar and J. P. Kawalek. “Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice.” Transportation Research Part E: Logistics and Transportation Review. vol. 114, pp. 416-36, June 2018.

[2] M.W. Barbosa, A.D. Vicente, M.B. Ladeira and M.P. Oliveira. “Managing supply chain resources with Big Data Analytics: a systematic review.” International Journal of Logistics Research and Applications, vol. 21, pp. 177-200, May 2018.

[3] J.W. Hamister, M.J. Magazine and G. G. Polak. “Integrating analytics through the big data information chain: A case from supply chain management.” Journal of Business Logistics. vol. 39, pp. 220-230, Sep. 2018.

[4] E. Hofmann. “Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect. International Journal of Production Research, vol.55, pp. 5108-5166, Sep.2017.

[5] S. Chehbi-Gamoura, R. Derrouiche, D. Damand and M. Barth. “Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model.” Production Planning & Control, vol. 31, pp. 355-382, Apr. 2020.

[6] S. Gupta, N. Altay and Z. Luo. “Big data in humanitarian supply chain management: A review and further research directions.” Annals of Operations Research, vol. 283, pp. 1153-1173, Dec. 2019.

 [7] K.M. Sam and C.R. Chatwi. “Understanding adoption of Big data analytics in China: From organizational users’ perspective”. In2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), vol. 16, pp. 507-510, Dec. 2018.

 [8] S. Maheshwari, P. Gautam and C.K. Jaggi. “Role of Big Data Analytics in supply chain management: current trends and future perspectives.” International Journal of Production Research, vol. 21, pp. 1-26, Jul 2020.

[9] S. Tiwari, H.M. Wee and Y. Daryanto. “Big data analytics in supply chain management between 2010 and 2016: Insights to industries.” Computers & Industrial Engineering, vol. 115, pp. 319-330, Jan. 2018.

[10] T. Nguyen, Z.H. Li, V. Spiegler, P. Ieromonachou and Y. Lin. “Big data analytics in supply chain management: A state-of-the-art literature review.” Computers & Operations Research, vol. 98, pp. 254-264, Oct. 2018.

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