Supply chain analytics is an integral part of the logistics operations that organisations use for extracting value and gaining information from the huge datasets relevant to goods procurement, processing and distribution. As an essential element of SCM, it involves using analytics software to enhance forecasting, operational efficiency and responsiveness to customer demands. For instance, to use point-of-sale terminal data in a demand signal repository, predictive analytics is used. Businesses use it to predict consumer demand to cut down on costs and deliver products faster.
Supply chain analytics holistically requires compiling all information relevant to its use case, from procuring raw materials to production, distribution and post-delivery services. All integrations between supply chain execution and management platforms fall under a company's supply chain umbrella. This type of integration aims to provide complete supply chain visibility (viewing all data on the movement of goods in SCM).
Supply chain analytics: How it works
Supply chain analytics helps in compiling data related to the supply chain. It spans multiple applications, third-party sources, infrastructure and upcoming technologies. Implementing technologies like IoT helps enhance the decision-making process for strategic, tactical and operational efficiency in SCM.
Supply chain analytics is used for synchronising planning and execution in the supply chain by improving visibility in real time. These processes impact the customers and the overall profit of the company. Increased visibility enhances flexibility in the network by helping evaluate tradeoffs between customer service and costs.
Data scientists are primarily involved in the operations of supply chain analytics because they understand the data-driven analytical aspects of the business. They may involve factors related to:-
- Cash flow
- Inventory
- Waste
- Service levels
A successful supply chain management career will require you to look for correlations amongst multiple data elements for developing predictive models to optimise the supply chain output. You will need to test out a number of variations to achieve the ultimate robust business model.
Supply chain analytics features
A supply chain analytics software has to include the following features to achieve operation efficiency:-
- Data visualisation: The ability to assess data from every angle to enhance understanding and achieve insight.
- Social media integration: Utilising sentiment data derived from social feeds for enhancing demand planning.
- Stream processing: Acquiring insight across multiple data streams generated by IoT, weather reports, applications and third-party data.
- Location intelligence: Using locational data for comprehending and optimising distribution.
- Natural language processing: Deriving and organising obscure data found in news sources, documents and data feeds.
- The digital twin of the supply chain: Compiling data and organising it into a systematic model of the supply chain shared across various kinds of users to enhance prescriptive and predictive analytics.
- Graph databases: Restructuring information into relevant elements, making it easier to locate connections, pinpoint patterns and enhance product, facility and supplier traceability.
Supply chain analytics types
The main supply chain analytics types are based on the four capabilities of analytics in Gartner's model. They are described below:-
- Descriptive: This form of supply chain analytics uses reports and dashboards to interpret daily happenings. It involves using a number of statistical methods to search, summarise and structure any data relevant to supply chain operations.
- Diagnostic: This is primarily used to figure out why something occurred or why something is not working as it should.
- Predictive: This form of supply chain analytics uses current data to help foresee anything likely to happen in the future.
- Prescriptive: This form of supply chain analytics helps automate or prescribe the most ideal course of action with the help of optimisation logic or embedded decision logic. It helps enhance the decision-making process regarding product launches, building infrastructure (factory or warehouse) or the best shipping strategy for every location.
Uses of supply chain analytics
Companies depend on supply chain analytics to help match supply with demand by developing plans aligning corporate strategy with everyday operations. Down below, we have elucidated the number of uses relevant to supply chain analytics:-
- Avoid risks: It is used in risk management by pinpointing known risks and forecasting future risks using patterns and trends derived from data relevant to the supply chain.
- Enhance order management: It is used for optimising the order management process by compiling all data sources for assessing inventory levels, predicting demand and identifying fulfilment issues.
- Optimise procurement: It is used for streamlining procurement by analysing and organising net expenditure across multiple departments to enhance contract negotiations and pinpoint discount opportunities or alternative sources.
- Enhance working capital: It is used for optimising the working capital by enhancing models to determine inventory levels required for ensuring service goals with minimal investment of capital.
Conclusion
The supply chain is directly impactful to businesses as it is entirely customer-centric and directly involves delivering products to consumers. Therefore, companies greatly depend on the efficiency of supply chain analytics because it helps protect the reputation of businesses and sustainability. Therefore, a supply chain management career is a highly lucrative one since all companies demand an expert in this field. You can start by applying for courses with certifications for supply chain professionals to kickstart your journey in SCM. The IIT Roorkee supply chain management course offered by Imarticus is a world-class course to begin with.
