5 Ways Supply Chain Analytics Can Help Mitigate Disruptions

In today’s day and age, companies in sectors across the gamut are continuing to face more frequent and impactful disruptions to their operations. Business leaders and industry innovators are constantly looking for ways to mitigate disruptions with new technology like supply chain analytics. Mitigating operational disruptions largely depends on developing a comprehensive supply chain analytics strategy. For many organizations, driving adoption let alone mitigating disruptions through supply chain analytics poses many challenges. Few have the necessary data building blocks to create a solid data foundation, relevant talent, knowledge, and skills are limited, and adoption is low because ROI is somewhat unclear. 

Despite these challenges, it becomes abundantly clear that constructing real-time data pipeline models that are built by data science teams and backed by data engineering is the future of optimal supply chain performance. Are you prepared to invest in a comprehensive analytics strategy to mitigate business disruptions through supply chain analytics? In this article, we’ll dive into the importance of harvesting accurate and comprehensive data with the proper business and technical skills to maximize its value, as well as diving into the importance of investing in real-time analytics and sustainable digital solutions. 

What are supply chain analytics? 

Supply chain analytics refers to the practice of collecting, analyzing, and interpreting data as it relates to a company’s supply chain operations in order to mitigate disruptions, improve efficiency, reduce costs, and increase profitability. By leveraging supply chain analytics, retailers can gain a competitive advantage by making more informed decisions about their operations. This leads to increased accuracy of demand forecasting, reduced lead times, and optimizing inventory levels. 

What is the goal of supply chain analytics as it relates to mitigating supply chain disruptions? 

The goal of supply chain analytics as it relates to mitigating supply chain disruptions is to identify patterns and insights in data that derive from procurement, manufacturing, transportation, and distribution processes. This information can then be used to identify trends, patterns, and opportunities for improvements in the supply chain. Some of the key areas where supply chain analytics can be used include forecasting demand, optimizing inventory levels, improving transportation and logistics, and reducing supply chain risk. 

How can supply chain analytics help mitigate supply chain disruptions? 

Supply chain analytics can help identify potential disruptions by analyzing data from various sources that can then be used by supply chain professionals to identify potential disruptions before they occur. In addition to identifying potential disruptions, supply chain analytics can also be used to model different scenarios and simulate the impact of disruptions on the supply chain which can be used to develop contingency plans that mitigate the impact of disruptions. 

1. Predictive analytics 

The beauty of supply chain analytics as it relates to mitigating disruptions is having the ability to predict future mishaps. Combining historical data and machine learning algorithms, supply chain analytics can predict potential disruptions, allowing organizations to take a proactive stance. By analyzing data on a variety of factors including inventory levels, supplier performance, and weather patterns, supply chain analytics can predict potential disruption and help organizations take proactive measures to mitigate the risks. 

Take an organization that is sourcing raw materials from a country that is prone to natural disaster as an example. By analyzing historical data as it relates to weather patterns and supplier performance, the organization can predict the likelihood of future occurrences. Armed with this information, the organization can then make more-informed business decisions that help mitigate future risks. 

2. Real-time monitoring 

Prior to recent technological advancement, it was difficult to monitor key aspects of the supply chain in real-time. Presently, leveraging IoT, RFID and other sensors has made it possible to measure various elements of the supply chain in real-time including but not limited inventory levels, and transportation routes. Real-time monitoring can help organizations identify mishaps or disruptions as they happen, empowering them to take immediate action that can ultimately prevent future disruptions. 

If an organization is strictly monitoring just-in-time inventory, it becomes much more difficult to mitigate disruptions. By leveraging IoT and other sensors, supply chain analytics can provide real-time monitoring of inventory levels and transportation routes. If a delay in shipping or a problem with a supplier occurs, the organization can immediately make adjustments to mitigate the impact of the disruption. 

3. Scenario planning 

Agility is the key to long-term success which is why it's important to leverage supply chain analytics to practice scenario planning. Supply chain analytics can be used to create different scenarios and test them to identify areas of vulnerability. These vulnerabilities can then be used to prepare for potential disruptions and develop contingency plans to minimize their impact. 

Take an organization that is sourcing raw materials or products from a country with an unstable political climate as an example. By creating and planning for different outcomes, supply chain analytics can help the organization identify potential disruptions and develop contingency plans accordingly. For example, the organization may plan to shift production to another location or source materials from multiple suppliers to mitigate the impact of the disruption should a change in the political landscape hinder the partnership. 

4. Optimization 

Supply chain analytics provides organizations with the unique opportunity to optimize their efforts in an attempt to become more resilient to disruptions. By analyzing data on factors like transportation costs, inventory levels, and lead time, organizations can leverage this data to optimize their supply chain, thus reducing the impact of disruptions.  

Take an organization that is experiencing disruption due to transportation delays for example. By analyzing data on transportation costs, lead times, and inventory levels, supply chain analytics can help the organization optimize transportation routes and inventory levels to help mitigate the impact of the disruption. For example, the organization may choose to leverage a different transportation route or increase inventory levels at key locations in case a transportation delay impacts supply. 

5. Communication and collaboration 

Supply chain disruptions often occur as a result of poor communication or a lack of collaboration. By providing a single source of truth for all key stakeholders, supply chain analytics can help streamline communication across the supply chain network, enabling all parties to communicate more effectively in response to disruptions. 

For example, an organization may be experiencing a disruption as a result of a delay in shipping. By leveraging supply chain analytics that provide real-time visibility into transportation routes and inventory levels, the organization can quickly notify all key stakeholders. Stakeholders can then work together to mitigate the risk of disruptions by collaborating on solutions as a unified front. 

Leverage supply chain analytic to mitigate disruptions today 

In today’s environment, a large majority of organizations are facing more frequent and impactful disruptions to their operations. To make better supply chain and organizational decisions, it’s critical that you harvest accurate and comprehensive data, secure the technical and business skills necessary to maximize its value, and invest in real-time analytics and digital solutions that maximize potential and sustain growth. How are you going to harness the power of data science to mitigate supply chain disruptions?