Supply chain risk management with data analytics

chain risk
with data

risk management

Transforming supply chain risk management through Big Data analytics.

Everyone is talking about big data, but only some are trying to benefit from it, notably in supply chain risk management. According to statistics, 97% of supply chain risk managers and analysts believe that big data can be handy for managing supply chain risk. Still, only 17% are using it. A supply chain is an excellent source of data from customers, the business itself, and its operations.

By analyzing and capitalizing on this data, businesses open themselves to endless possibilities and obtain a considerable competitive advantage. So, let us look closer at how the supply chain can benefit from big data analytics in risk management and how everything works.

Big data analytics for supply chain risk management: why it’s essential for business

The incredible amount of data produced by the global and supply chain disruption– chain can be transformed into valuable insights to help identify both issues and opportunities to transform the company’s business strategy from reactive to proactive. Supply chain analytics use data and quantitative methods for enhanced decision-making. It becomes possible with the evolution of datasets for analytics from the conventional, in many cases unstructured, data stored on both Enterprise Resource Planning and Supply Chain Management Systems.

These insights become especially important for supply chain security and risk management in the age of increased interconnectivity. New risks, like cyber threats, arise along with traditional ones, making the supply chain more vulnerable than ever. Big data and artificial intelligence can help considerably detect and prevent these hazards. Moreover, processing supply chain data can improve customer service – it can help better preserve products during transportation and avert shipment delays due to unforeseen circumstances.

Introducing data science into your company’s risk management strategy

Recently, risk management strategies have evolved. The conventional approach, mainly characterized as a sectoral and fragmented view of risks (“silo” of supply chain resilience and risk management strategy), has been replaced by new supply chain risk management philosophy that involves the whole organizational structure and affects strategic and operational processes. It is known as enterprise risk management (ERM). It is used for integrated risk management by analyzing business contingencies and evaluating uncertainty with further supply chain risk management solutions.

Successful supply chain risk identification and management rely on a proactive and predictive approach. Identifying and mitigating risks before their negative impact can significantly cut unnecessary operational and financial losses. This approach to managing risk mitigation in the supply chain with the help of big data includes three key elements:

  1. Increased visibility and control over the suppliers’ network. Big data has the power to provide insights into the performance of each supplier for enhanced risk management. This is even more useful for companies that work with hundreds of suppliers.
  2. Supply chain integration and alignment. Supply chain risk management can often be carried out separately by each supply chain member. Here data analytics can turn this process into a coordinated effort where the whole supply chain benefits rather than single members.
  3. Increased agility and resilience. In this case, merging big data analytics and supply chains can contribute to achieving a certain level of resilience in the supply chain by analyzing vast volumes of data.

Incorporating data science into a company’s risk management strategy revolutionizes risk identification and mitigation and fosters a more integrated, proactive, and resilient supply chain. By leveraging the power of big data, companies can gain unprecedented visibility into their supplier network, promote alignment across the entire supply chain, and enhance their agility and resilience, ultimately transforming their risk management from a reactive to a predictive model.

Big data supply chain risk management application

If operated correctly, big data produced by the supply chain can considerably help sales, inventory, and operations planning. Inventory data, point of sale data, and production data real-time analytics can be used to identify other potential risk factors that mitigate the mismatches between the supply chain visibility and demand. Hence, appropriate actions can be taken. For example, by analyzing the link between production planning and weather forecasts, bakeries can foresee the demand for a specific product category.

Moreover, external channels and internal networks in the supply chain operations and production department generate a lot of data. Using big data to analyze and integrate their databases can significantly improve distribution efficiency and sales process. Furthermore, the high number of vendors and the variety of their evaluation and selection indicators make choosing the right one difficult.

In this case, cloud technologies applied to the supply chain risk management process can impact it significantly. With the new systems, data access, control risks, and exposure are more intuitive and customer-oriented, thanks to the API power and integration into today’s big data applications and analytics packages.

Mitigating internal and external supply chain risks

For improved risk management with the help of supply chain data analytics, it’s crucial to understand the risks businesses are dealing with and the data availability on supply risks. Internal supply chain risk management usually means mitigating the predictable risks based on the internal supply chain data. The organization has more control over these risks, making them easier to manage. Also, monitoring supply chain internal and external risks can help to detect emerging threats on time and deal with them beforehand.

  • Problems within the organization cause internal supply chain risks, including machine issues, transportation risks, import or export restrictions, delivery or supply chain disruptions, information technology issues, etc. Moreover, internal supply chain risk management steps are data analytics, supply chain monitoring, adopting the emergency plan, etc.
  • The external supply chain risks are more difficult to manage as the data, in this case, is unstructured and expanding rapidly. It comes from public media, professional databases, and social networks. Moreover, supply chain external risk management data analytics requires vast technological and human resources. They create an external risk report that depends on the organization’s geographical location and industry background. Internal and external risks can be political, economic, technological, or geographical.

External factors, including weather conditions, social hazards, infrastructure issues, digital threats, etc, logically cause external supply chain risks. In this case, the supply chain risk management process includes defining and monitoring demand risks in the domain, collection of data, and risk analysis.

The great future

The amount of data produced by the supply chain is expanding every year. The new technologies and devices introduced to the supply chain disrupt the supply chain and attack various stages: production, transportation, and selling, creating many data that can be transformed into valuable insights. A great example that illustrates this tendency is the Internet of Things (IoT). According to the research, the data produced by IoT is set to increase by almost 500% to 80 zettabytes by 2025. The IoT implications in the supply chain are expected to play a significant role and become a dominating technology for supply chain risk management software.

This data can be generated by the RFID tags and sensors in the warehouse to detect the precise location of items and track their movement beyond it. The valuable information is like where the goods are coming from, their expiration date, etc. Moreover, during transportation, the data from GPS and the same RFID sensors can help to increase the transit visibility of the shipped items and to optimize routes for potential delay prevention.

Furthermore, this data helps obtain crucial information like temperature and humidity that can affect items and raw materials’ quality during transportation. Combined with environmental data like weather conditions and traffic data, this information can help significantly in managing supply chain and environmental risks during transport. But this is only one of the examples of how data analytics affects supply chains – the possibilities are endless.

Final thoughts

The companies that apply the power of big data to their supply chain risk management benefit greatly. By using advanced analytics, they make their supply chains more customer-oriented, demand-driven, and, overall, more responsive.

Contact us and tap into the advantages of utilizing big data analytics to mitigate supply chain risks and help organizations to become proactive and predict potential risks well before their escalation. As a result, turning supply chain management into a steady and improved process.

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