Supply Chain Challenges in Adopting Big Data Analytics
Analytics techniques offer pronounced advantages in the transformation of the economy. But it also raises several challenges with capturing the data, storage of that vast information, and analysis and visualisation of the data. It also incorporates issues with data inconsistency and information incompleteness, timeliness and security, and safety of the data information .
Challenges faced can be categorised into two categories such as Organisational Challenges and Technical Challenges :
i. Organisational challenges:
- • Time delay: Big data are featured for its humongous volume of data. The complex intricacy of Supply Chain with the goals for the interpretation of the data sets in a company with the external components like the timely absence of access to the data that constitute in making the process of analytics more time-consuming.
- • Insufficiency of resources: The accessibility of real-time information is very pivotal for the betterment of results. Supply Chain is a platform that produces complex multi-functional data for inter-related entities, requires collection, and storage of multi-functional information to be well-organised and streamlined.
- • Privacy and security concerns: Sharing of Data through Supply Chain Network is a major component in data and information collection from numerous different sources, analysing it and providing insights. But due to several laws related to Security and Privacy constraints towards the data sharing process, the accuracy of the insights gathered through Big Data Analytics is affected.
- • Behavioural issues: If the decision-makers act on all the unsubstantial changes, the supply chain and inventory cost can be at an increased risk. As big data are associated with a variety and huge volume, there exists an elevated risk of recognising insignificant associations.
- • Issues on return on investment (ROI): Big data as we are aware are associated with a huge variety and volume of data and information. This aspect of big data makes it very enigmatic to evaluate the value of the data that are collected. Executing analytics on big data necessitates a considerable quantity of expenditure for constructing the infrastructure that is required. There might exist an elevated risk on the ROI because of scepticism on the value of the data.
- • Lack of skills: The data produced by Supply Chain Sources are very complex. And, it recommends an amalgamation of good analytical skills on domain knowledge and the potential to comprehend the usability of the information. Finding such conjunction along with experience is strenuous.
ii. Technical challenges :
- • Scalability of data: In the course of making use of analytic technology in any organisation, scalability of information and data is observed as a prime technical problem. The inefficacy of several systems to transit from conventional limited databases to distributed or cloud-based databases negatively influence the insights received from Big Data Analytics as the quality and quantity of relative data obtained are jeopardised.
- • Quality of data: The implementation and outcome of the analytics techniques determine the data and information quality that is stored and utilised. Data are featured as intangible, complex, and multi-dimensional on the grounds of its origin and implementation. The data quality is required to be consistent to obtain harmonious and dependable results for the intention of decision making. The quality of data and information gathered is determined by the diversity and origin of data.
• Deficit of techniques: impotence and inability of systems and organisations to exploit the information gathered badly control the robustness of the insights developed after evaluating the data sets. The mechanisms and methodology that are used to explore, evaluate, assess, predict, and conceptualise data and information demand to be modified and enhanced with respect to the magnitude and intricacy of data.
Future of Supply Chain Analytics
The emerging need to manage a voluminous amount of data and information and utilise the insights derived from it is establishing the pressure of the need for supply chain analytics. The heightening popularity among several organisations of the advantages of big data in the supply chain analytics is playing a significant role in escalating the demand for analytic solutions to enhance the perceptibility all over the supply chain enablers.
Big data in Supply chain analytics is capable of assisting a firm, organisation, or company in taking rapid, intelligent, cost-effective, and more efficient decisions.
The benefits include the following:
- • Better apprehension of risks: By recognising the known risks, the supply chain analytics has the ability to predict the upcoming future risks by observing and perceiving trends and patterns all around the supply chain.
- • Enhance precision in planning: Big data guides and assists an organisation in predicting the future demand better. It evaluates and analyses the customer data to ensure precise prediction. It provides several insights that help many organisations determine which products must be pruned when they are less beneficial and which products are in demand.
- • All-around supply chain: Supply Chain technologies are used by organisations to gather customer requirements, perform warehouse tracking and supervising, and collect responses of users and customers to make knowledgeable judgements and decisions.
- • Achieve a profitable ROI: With a better understanding of the data in the organisation throughout the supply chain, organisations can make smarter investments and receive higher returns .
Artificial intelligence is seen as the next step in supply chain analytics. It has been built with the ability to process the enormous volume of data and information (structured as well as unstructured) and afford insights in real-time . Along with data retention and process automation, systems with AI are built with the potentiality to think, cogitate, reason, rationalise, learn, and improve like humans.
Advances of blockchain technologies along with AI  integration provide organisations with the ability to actively analyse and make predictions.
Several key factors such as shrinking of product life cycles, reduced supply chain visibility, unproductive supplier networks, increased warehousing expenses, a surplus of unrequired forecasts, and oscillating customer demands necessitate the optimisation of the supply chain.
In conclusion, Supply chain analytics technology for big data assists several enterprises and organisations in attaining growth , improving profitability and gain, and increasing market shares by making use of the derived insights for taking strategic decisions. With the help of the case studies mentioned above in the paper, we can conclude that solutions offered by supply chain analytics provide a holistic approach to supply chain and enhances sustainability, decreases the inventory cost, and accelerates the time taken to market the products.
Enhanced outcomes and improved cost-effectiveness obtained as a consequence of adopting supply chain analytics encourages the application of solutions in various applications, such as retail and consumer goods, healthcare, and production.
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