A supply chain is a system of organizations, people, activities, information, and resources involved in moving a product or service from a supplier to a customer. Supply chain activities involve the transformation of natural resources, raw materials, and components into a finished product that is delivered to the end customer. In sophisticated supply chain systems, used products may re-enter the supply chain at any point where residual value is recyclable.
In recent decades, companies are relying on traditional supply chain systems which is becoming increasingly difficult and firms have looked to lean manufacturing, technology and global production to increase efficiency and reduce costs. However, global operating systems, pricing pressures and ever increasing customer demands and expectations have gotten more complex. There are also recent economic impacts such as global recession, escalating fuel costs, supplier bases that have shortened or moved off-shore. At the same time, supply chains have grown more complicated – bridging multiple continents and involving external suppliers. All of these challenges and tactics potentially result in waste in supply chain and diminishing returns. That’s where combination of large and fast-moving data and advanced data analytics comes in.
Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories.
Key Supply Chain Challenges
- Lack of real-time data visibility, with no common view across all businesses and channels
- Lack of synchronization between planning and execution
- Lack of flexibility in the networks and distribution footprint
- Price volatility and difficulty in de-risking
According to an article on EBN online (Betting on Analytics as Supply Chain’s Next Big Thing), “Some industry experts claim that the day for real-time supply chain practices has come — and is on the verge of being more mainstream, thanks to a multitude of cloud data management tools and increased corporate adoption of new supply chain software platforms coming to market. However, there’s also acknowledgement that a necessary foundation for moving efficiently at real-time speed — supply chain analytics — is still very much at the beginning stages of development at many companies, and will take time to build out.”
In the past few years, all corporations with a supply chain and logistics system devote a fair amount of time to making sure it adds value, but combination of large, fast-moving, and varied streams of big data and analytics make it possible to dig into supply chain data in search of clear understanding of the strategic priorities, market context and competitive need of a company. These tactics offer major new opportunities to reduce inventory, lower/dynamic costs, improve agility and enhance customer responsiveness.
The supply chain is a great place to use advanced analytics, near real time analytics and advanced disciplines such as geo-analytics to look for a competitive advantage because of its complexity and the prominent role supply chain plays in a company’s cost structure and profitability. With the help of these powerful data-processing and analysis capabilities businesses can optimize distribution, logistics and production networks. They can also improve the accuracy of their demand forecasts, discover new demand patterns and develop new services by sharing data with partners through the supply chain. Supply chains can appear simple compared to other parts of a business, even though they are not. If we keep an open mind, we can always do better by digging into data as well as by thinking about a predictive measure instead of a reactive view of the data.
