How Big Data Analytics is Helping in Supply Chain?

Big Data is heavily talked about, yet only a few are harnessing its benefits, particularly in supply chain risk management. Stats suggest that out of 97% of supply chain analysts who believe that big data is a significant asset for the supply chain, only a mere 17% actually use it. Big Data in the supply chain can provide substantial amounts of data related to customers, businesses, and their operations. This data, if well-analyzed and capitalized on by businesses, can open the door for endless opportunities and get them a competitive advantage too. Let’s understand how the supply chain can get benefit from Big Data Analytics in risk management and/or how you can mitigate supply chain risks with Data Analytics.

The Need for Data Analytics for Supply Chain Risk Management

Certain data and quantitative methods are leveraged by analysts for enhanced decision-making. This has been possible with the evolution of datasets for analytics from the conventional (and unstructured) data stored on EPR and Supply Chain Management systems.

These insights become extremely helpful, especially in this age of increased interconnectivity where cyber threats or cyber-attacks have become pervasive, and new risks are introduced every now and then, making the supply chain more vulnerable than ever. Also, the Covid-19 pandemic exposed scads of supply chain vulnerabilities which a majority of the companies weren’t aware of earlier. Supply and demand were adversely affected. Sooner, companies realized that they need to come up with new strategies and approaches to manage the supply chain risks.

With Big Data in place, these threats or risks can be detected and prevented. Furthermore, the processing of supply chain data can play a crucial role in improving customer service by better preserving products during transportation and preventing shipment delays due to unforeseen circumstances.

The Role of Data Science in Supply Chain Risk Management Strategy

Risk management strategies have evolved of late. The conventional approach that was primarily characterized as the sectoral and fragmented view of risks has been replaced by a new philosophy that involves the entire organizational structure and affects operational as well as strategic processes. It is referred to as Enterprise Risk Management and is used for integrated risk management by analyzing business contingencies and evaluating uncertainties with further risk management solutions.

There has to be a predictive and proactive approach for successful supply chain risk management. Identifying and addressing the possible risks before their negative impact can help cut down unnecessary operational and financial costs. This approach to managing the supply chain with the help of Big Data comprises three key elements-

1. Better control and visibility over suppliers’ network

Big data possesses the ability to provide valuable insights into the performance of each supplier for enhanced risk management. This proves extremely beneficial for companies that deal with a slew of suppliers.

2. Integration and Alignment

Supply chain risk management can often be carried out by individual supply chain members. With Data Analytics, this can be turned into a collective effort rather than individual members.

3. More Agility and Resilience

The combination of supply chain and big data analytics helps achieve a certain level of resilience through the analysis of a substantial amount of data in the supply chain.

Mitigating Internal and External Supply Chain Risks

Internal supply chain risk management generally refers to mitigating the predictable risks based on the internal supply chain data. Monitoring these risks helps detect emerging risks and deal with them beforehand. In this case, a company has better control over the risks and thus those risks are easier to manage. These risks can arise due to machine issues, transportation risks, delivery chain disruptions, import/export restrictions, or some IT issues. Mitigating internal risk is usually a three-step process- Data analytics, Supply chain monitoring, and adopting the emergency plan.

The external supply chain risks, on the other hand, are comparatively difficult to manage as the data remains unstructured and expands rapidly. Its origin can be professional databases, public media, social networks, etc. Moreover, the resources (both technical and human) required are huge for data analytics in supply chain external risk management. As the term suggests, these risks are caused by external factors such as weather conditions, digital threats, social hazards, etc. The process to manage external supply chain risks involves defining and monitoring the domain, data collection, and risk analysis.

Key Approaches

There are two key approaches to supply chain risk management-

1. Putting a supply chain to tests

Harvard Business Review classifies the approach into two key metrics- TTR and TTS. TTR or time to recover is the time required for a specific supply chain node to be restored and fully operational after it is disrupted. On the other hand, TTS, or time to survive is the maximum time for a supply chain to meet the supply with demand after a specific facility gets disrupted.

This approach enables companies to form an estimate of the disruptions’ cost and prepare risk mitigation plans for the integral parts of the supply chain.

2. Analytics and Mapping

This lets you trace the entire supply chain, and monitor the production units, routes, and nodes. Ultimately, you will be able to identify routes that have bottlenecks that result in delays, track how your warehouses are performing, get a clear understanding of the inventory, and detect the demand patterns for different products.

Summing Up

The supply chain produces a huge amount of data every year. With the introduction of new devices at various stages like production, transportation, and selling, a high volume of data is created that can be quite useful insights. IoT has a big role to play here and is slowly becoming one of the dominating technologies for the supply chain management. For instance, a lot of data can be obtained through RFID tags and sensors that can track the precise location of the items and track their movement. In addition to this, the GPS data combined with the data from RFID sensors can help increase transit validity during transportation and optimize routes for delay prevention. And this data can be used in many further areas. This is just one example of how data analytics affects supply chains.

Companies benefit greatly from big data in their supply chain risk management. Leveraging advanced analytics helps them make their supply chains more demand-driven, customer-oriented, and responsive. Besides, using it to mitigate your supply chain risks makes you more proactive, thus improving your overall supply chain management process.