The Role of Data in Supply Chain Risk Management
Operating successfully in a volatile global landscape requires proactively monitoring and planning for a range of risks. Complex supplier networks, transportation bottlenecks, and raw materials shortages can create significant challenges. Using supply chain intelligence tools such as predictive analytics programs, businesses can improve supply chain risk management. In this post, we’ll examine a range of supply chain disruptors and how data can help organizations minimize these risks.
Supply Chain Risks
Supply chain risks come in a variety of forms, including known and unknown risks. Regardless of the source and severity of the risk, recognizing potential disruptors early allows decision-makers sufficient time to act before they cause lasting damage. Here are some of the most common and potentially serious supply chain risks.
Price volatility of raw materials or critical components can wreak havoc on financial projections and profitability. Inflation, tariffs, and unexpected changes in demand can all contribute to price fluctuations.
Poor-quality components or raw materials can result in a spike in warranty claims and create dissatisfied customers. The cost of resolving quality issues, especially once a product has reached consumers, can be significant.
Transportation-related issues can prevent critical components and materials from arriving in time, slowing or halting production lines. Logistical issues can also delay the timely delivery of finished products to customers. Port congestion, labor disputes, and weather events can all result in unexpected delays.
Raw materials or critical component shortages
A spike in demand for raw materials or shortages can cause a supplier to be unable to meet agreed-upon delivery timelines. This can result in production delays that negatively impact the business.
Civil unrest, labor strikes, and pandemic-related government restrictions send shockwaves through entire supply chains, creating a ripple effect that can be difficult to contain.
As severe weather events become more frequent, so does their ability to negatively impact supply chains. For example, torrential rain can create flooding that takes a supplier of a critical component offline for weeks. And a destructive hurricane can disrupt port operations and damage transportation infrastructure.
As supply chains have become more global, supplier-related risks have grown. Suppliers with poor labor or environmental records create significant public relations risks for businesses that rely on them. Those in poor financial health may struggle to meet deadlines or adhere to agreed-upon quality standards. And if they shutter permanently, businesses that rely on them for critical components will be left scrambling. Lastly, vendors with poor cybersecurity safeguards can pose a liability if their poorly protected IT systems are integrated with those of their customers.
Using Data for Supply Chain Risk Management
Leveraging data from in-house, supplier, and third-party sources can help organizations proactively manage risks at each stage in the supply chain. Here’s how to get started.
Supply chain risk management usually begins by assessing each node in the supply chain in depth for risks that can be identified and measured. Businesses can analyze data from their factories and warehouses, as well as from suppliers and logistics operations. Public data such as weather and global health data is also useful in identifying risks. These risks are entered into a risk register, noting the parts of the supply chain where no data or insufficient data exists. Each risk should be scored based on the severity of the risk, the likelihood of it materializing, and how prepared the business is to address it. This process will help prioritize which factors and stages in the supply chain are most vulnerable to disruption.
Build robust supply chain contingencies
Successful supply chain managers are skilled at creating contingencies. Data analytics programs can also be used to inform the best means for resolving a disruption when one occurs. Examples of supply chain contingencies include alternate suppliers, possible modes of transportation, and additional production facilities that could be used. Well-developed supply chain contingencies can guide decision-makers when risks are realized, helping them react more quickly when it matters most.
Monitor and manage risks
Data analytics programs can help businesses track many factors, including inventory, demand, supplier performance, transportation bottlenecks, and delivery times for raw materials and finished goods. Predictive analytics can also help businesses anticipate how risks such as severe weather events, political unrest, and pandemic-related shutdowns may impact supply chains.
It’s important to note that incomplete, inaccurate, or stale data can have a significant negative impact on a company’s ability to provide decision-makers with accurate information. For this reason, it’s crucial to focus on data quality in supply chain risk management programs. In addition, when data is siloed across multiple systems, decision-makers struggle to gain a holistic view across the entire supply chain, so centralizing data is a must.
Regularly reevaluate known risks and planned responses
Creating a robust system for continual reevaluation of risks, contingencies, and responses is an essential final step. Keep an eye on developing risks, regularly revisit identified risks, and reevaluate planned responses to help create a more resilient and agile supply chain that can thrive in a range of adverse conditions.
Snowflake Supports Supply Chain Risk Management
The Snowflake Data Cloud provides manufacturers, retailers, and logistics companies with the tools they need to manage supply chain risks more effectively. Gain a global real-time view of your supply chain with Snowflake’s support for structured, semi-structured, and unstructured data from internal and external sources and support for secure data sharing with partners to supplement your existing data sets. The Snowflake Marketplace provides access to live and ready-to-query data sets from a large number of third-party data providers and data service providers, increasing the effectiveness of supply chain intelligence tools for enhanced data-driven decision-making. With near-infinite scalability, Snowflake enables the storage of near-limitless data volumes so you can capture and use all relevant data. Dedicated, task-specific compute resources improve the speed and efficiency of data queries. Snowflake’s robust security features such as advanced user access control, dynamic data masking, and end-to-end encryption for data in transit and at rest ensure sensitive data stays protected.