Big Data Analytics in Supply Chain Optimisation

Only 14 per cent of the supply chain management executives had realised the importance of big data in 2014. By 2022, the percentage had increased to 50 per cent and has been increasing ever since, thus proving the change that big data analytics has brought to the scene of supply chain management.

From closely monitoring quality in real-time to predicting and preventing risks, massive data sets have become the new solution to optimising supply chains.

Transformative Impact of Big Data on Supply Chain Management

For optimisation of production planning

While it can be quite daunting a task to create the most realistic production plan and schedule, Data Analytics can help tackle this challenge. Also known as logistics management, it helps clear up the scheduling constraints that manufacturing units often face due to a multiplicity of schedules. With the help of data analytics, the manufacturing facility can compare the prepared schedule and the real-time output to ensure that the plan most accurately represents the actual quantity that can or will be produced. 

Using integrated data sourced from the supply chain, companies can perform both catalogue management and planning for restocking, as well as monitor delivery situations. Since the time lapse between the scheduled delivery and actual delivery, both early and delayed can be expensive, data signals can be used to track the delivery speed and location of goods and identify the best routes for delivery, staffing and so on. 

Analysis of consumer behaviour 

Behaviour analysis of customers has formed a significant part of data analytics due to the volatile market conditions and the imminent possibility of a recession. By using data analytics to understand customers’ preferences, organisations can adjust their schedules and production stocks. By predicting the demands, it is easier to predict the requirements of the customers and provide them with a unique experience.

Behavioural analytics can be applied to internal stakeholders and vendors as well. The executive officers who work in the domain of supply chain management can assess the behaviour and requirements of every participant in the business, and offer support while mending gaps wherever required. 

Maintenance of machines

Maintenance of big machines has been much easier with the aid of data analytics. Supply chain enterprises often have to encounter the risk of sudden equipment breakdown, malfunctioning due to rust, and other unexpected obstacles. The maintenance department can benefit from combining large data systems with the Internet of Things (IoT) to transmit alerts for any kind of irregularity detected in the equipment. For instance, they can use detectors to monitor production, find any abnormality, and inform the controller about the need for routine maintenance when the time comes. 

The detectors are installed to mirror the operations of the machine and the data retrieved in real-time can help predict machine failure, and thus augment the efficacy of the maintenance department. In the long run, this is a highly cost-saving move for it minimises expenditure on repairs and prevents unscheduled downtimes.

Management of supplier relationship

IndusSupplier Relationship Management can be greatly improved with the aid of Data Analytics. If the company can efficiently collect supplier data and analyse it with data analytic tools, then it can proactively monitor supplier behaviour and minimise obstacles.

For instance, decisions made on purchase orders are hugely influenced by supplier lead time, especially concerning timing and sizing. Considering that there can be fluctuations in supplier lead times, professionals can use big data analytics to accurately forecast lead times and avoid considerable variations. Qualitative data in the form of assessments and audits can be used by the companies to keep track of the suppliers’ activities for future purposes as well as to choose the right kind of suppliers. In case the primary supply chain is disrupted or violated, then the companies can easily pick an alternative reliable supplier for delivery and avoid losses.

Product design and quality control through predictive maintenance

Industries such as food processing, agriculture and chemicals often need to be constantly supervised and controlled, especially in specific elements. For instance, temperature control is a significant factor in ensuring the quality of the product, as even the slightest fluctuation in temperature can render the end product completely unusable.

One use of big data analytics is in cold chain monitoring technology, where data logging facilitates logistics for temperature-sensitive products. Furthermore, managers can control the heating and cooling equipment during packaging, transit, and delivery in real-time as needed.

 In supply chain management, product designing is also another important factor, and the designers can use data about the changing preferences of consumers to incorporate changes into product structure.

The Future of Predictive Big Data Analytics in Supply Chain Operations 

According to a survey by Gartner, among the supply chain leaders, 76 per cent have reported that they are increasingly facing supply chain obstacles. Hence, businesses are seeking out more applications of data analytics in predicting and preventing disruptions rather than remedying them. It is assumed that cross-functionality, or collating multiple supply chains to get a singular perspective and thus make quick strategic decisions in a fluctuating market, will be the biggest contribution of big data analytics in supply chain optimisation. Moreover, predictive data analytics will always be an integral part of the three main vital steps in supply chains: procuring goods, tracking inventories, and logistics management. 

Apart from the logistical strategies, big data analytics can also be impactful in endorsing sustainable practices in supply chain management. ESG (Environmental, Social, and Governance) issues, such as the eco-friendliness of the products, and exploitative labour practices can be addressed by the companies who retrieve data from their supply chain networks.

Conclusion

Managing supply chains in a digital mode has become increasingly in demand due to the cost-effectiveness and the greater amount of information to be retrieved. Aspirants seeking to make a career in this lucrative profession can hence train themselves in a Supply Chain Management Certification Course. 

One such programme is the Digital Supply Chain Management with E&ICT, offered by Imarticus Learning in partnership with IIT Guwahati. The classes will be held live virtually and will run for 6 months. The curriculum is focused specifically on industry orientation and students learn to use technology to get real-life experience in supply chain management. After completing this Supply Chain Management Certification Course, students are guaranteed to get placed in companies such as Nestle, Amazon, Microsoft or Paytm.