Supply chain analytics refers to the processes organizations use to gain insight and extract value from the large amounts of data associated with the procurement, processing and distribution of goods. Supply chain analytics is an essential element of supply chain management (SCM).
The discipline of supply chain analytics has existed for over 100 years, but the mathematical models, data infrastructure, and applications underpinning these analytics have evolved significantly. Mathematical models have improved with better statistical techniques, predictive modeling and machine learning. Data infrastructure has changed with cloud infrastructure, complex event processing (CEP) and the internet of things. Applications have grown to provide insight across traditional application silos such as ERP, warehouse management, logistics and enterprise asset management.
Supply Chain is a tricky business. One missing entity or a lack of synchronisation can break the entire chain and mean millions in losses for a company. However, the use of analytics in this domain is resolving several pain points in supply chain management at the strategic, operational, and tactical levels. According to Capgemini Analytics, “Supply Chain Analytics brings data-driven intelligence to your business, reducing the overall cost to serve and improving service levels.” For supply chain professionals, it can only mean one thing to upskill to be able to use advanced analytics to improve operational efficiency and make data-driven decisions.
An important goal of choosing supply chain analytics software is to improve forecasting and efficiency and be more responsive to customer needs. For example, predictive analytics on point-of-sale terminal data stored in a demand signal repository can help a business anticipate consumer demand, which in turn can lead to cost-saving adjustments to inventory and faster delivery.
Achieving end-to-end supply chain analytics requires bringing information together across the procurement of raw materials and extends through production, distribution and aftermarket services. This depends on effective integration between the many SCM and supply chain execution platforms that make up a typical company’s supply chain. The goal of such integration is supply chain visibility: the ability to view data on goods at every step in the supply chain.
Types of Data analytics are:
Descriptive Analytics: Descriptive analytics breaks down the insights gathered from any data sets to help businesses understand them better. In a way it summarises all the information gathered.
Predictive analytics: While predictive analytics doesn’t actually tell you what will happen in the future, it gets pretty close to that. This process unveils all the patterns and motifs from existing data to present a set of trends that are likely to occur in the future.
Prescriptive Analytics: Prescriptive analytics helps businesses take decisions based on analytical findings. Since it is based on data it is scalable and can provide businesses with direction.
Cognitive Analytics: Cognitive analytics tries to mimic the human brain by studying data and understanding patterns and interpreting them to draw conclusions. This knowledge thus gained is further used for future reference.
Challenges of Supply Chain Industry
- Supply chain and logistics process transparency.
- Managing Demand Volatility
- Tackling cost fluctuations in Supply Chain
Advanced Analytics in Supply Chain
Data is crucial for managing all kinds of supply and storage systems. Predictive analysis, in particular, has emerged as a successful way of ensuring an effective management of supply chain units. Predictive analysis uses copious amounts of data to gain insights on any kind of future scenarios to avert plausible disruptions and prepare for the inconvenience.
Predictive analysis can be used for demand forecasting to understand consumer behavior and anticipate product and consumer demand. Historical data can be used to perform time series analysis and come up with insights that can predict seasonal fluctuations, consumer trends, weather-date purchase correlations. It is a proven method of predicting and managing supply chain logistics.
Data-powered decisions can help supply chain businesses set the standard for operational success. Fast digitisation and ready availability of data has paved the way for big data analytics in the supply chain industry. The use of big data analytics has immense potential to curtail costs, manage demand volatility, and make the process more visible for stakeholders.
Better understand risks
Supply chain analytics can identify known risks and help to predict future risks by spotting patterns and trends throughout the supply chain.
Gain a significant return on investment
A recent Gartner survey revealed that 29% of surveyed organizations said they have achieved high levels of ROI by using analytics, compared with only 4% that achieved no ROI.
Increase accuracy in planning
By analyzing customer data, supply chain analytics can help a business better predict future demand. It helps an organization decide what products can be minimized when they become less profitable or understand what customer needs will be after the initial order.
Prepare for the future
Companies are now offering advanced analytics for supply chain management. Advanced analytics can process both structured and unstructured data, to give organizations an edge by making sure alerts arrive on time, so they can make optimal decisions. Advanced analytics can also build correlation and patterns among different sources to provide alerts that minimize risks at little costs and less sustainability impact.
As technologies such as AI become more commonplace in supply chain analytics, companies may see an explosion of further benefits. Information not previously processed because of the limitations of analyzing natural language data can now be analyzed in real time. AI can rapidly and comprehensively read, understand and correlate data from disparate sources, silos and systems.
It can then provide real-time analysis based on interpretation of the data. Companies will have far broader supply chain intelligence. They can become more efficient and avoid disruptions while supporting new business models.
Achieve the Lean Supply chain
Companies can use supply chain analytics to monitor warehouse, partner responses and customer needs for better-informed decisions.