Introduction to Supply Chain Analytics
The application of high-level insight gained from an organization’s data at multiple stages in its supply chain, procurement and processing, inventory management, distribution, and beyond is known as supply chain analytics. This analysis technique is essential for conveying a complete story concerning operational processes and laying the groundwork for initiatives to automate and optimize logistical operations. It can aid businesses in improving and optimizing overall effectiveness and efficiency in their supply chains in many ways. Data collected throughout all touchpoints of the said supply chain, from sourcing and manufacturing to shipping and customer support, is used in supply chain analytics. This information is then used to make crucial operational choices like purchasing, scheduling, holding, carrying ability, and staffing, among other things. Analytics usually refers to the ability to make data-driven decisions based on a review of relevant, reliable data, which is commonly shown using graphs, charts, and other tools. Massive amounts of data are generated by supply chains regularly. The Supply chain analytics space aids in the deciphering of all this data, revealing trends and providing insights.
Evolution in Supply Chain Analytics Space
The worldwide supply chain analytics market is expected to increase from USD 3.5 billion in the year 2020 to an amount of USD 8.8 billion in 2025, with a projected Compound Annual Growth Rate (CAGR) of 19.8%. Four out of five merchants seek real-time automation for demand planning and forecasting by 2025, with a more significant percentage aiming for the same in inventory processes
. In 2021, the sales and operations analytics segment had more than 29% market share, while the cloud segment had more than 62% market share. The large enterprise sector dominated the market, accounting for more than 60% of total sales in 2021, and the manufacturing segment had the most significant market share, with over 24% for the supply chain analytics space.
Supply chain analytics will expand in tandem with analytics models, data structures, technology, and the ability to combine data across application silos. Improvements in IoT, CEP, and streaming architectures will also allow businesses to gain insight from various data sources. People’s ability to create more detailed and relevant predictive insights that can be implemented into workflows will continue to increase as AI develops. The next frontier in supply chain analytics, according to studies, is cognitive technology or artificial intelligence. Information preservation and process automation are not the only benefits of AI technology. Artificial intelligence software can think, reason, and learn like a human. AI can also process massive volumes of data and information — both structured and unstructured — and deliver quick summaries and analyses of that material. Blockchain, Graph analytics, Hyperautomation, EDI-as-a-Service, Agile supply chains, Cloud computing, and Big data are among the other technologies and trends predicted to play a significant role in supply chain analytics and management.
Types of Supply Chain Analytics
Five forms of supply chain analytics are used to build a capability hierarchy for distribution executives, supply chain managers, and other decision-makers:
Descriptive analytics examines the events of the past. They can spot patterns in past data. This data could come from internal and external supply chain execution software that provides insight across suppliers, distributors, sales channels, and customers. Analytical techniques can examine the same sort of data from various periods to spot patterns and speculate on reasons for change. However, even though descriptive analytics is widely regarded as the foundation of analytics, many firms are only beginning to integrate it into logistics.
Predictive analytics is typically seen as demand projections, often subdivided by product, region, and consumer. These figures provide a heads-up so one may ramp up production, staffing, and raw material purchases to meet demand. They can also point out the impact of changes in operating policies. Predictive Analytics builds on the foundation of descriptive analytics but extends its capabilities. Predictive analytics for inventory management incorporates demand projections into models of inventory policy operation, which then generate estimates of essential performance measures such as service levels, fill rates, and operating costs.
Prescriptive analytics is concerned with what should be done next rather than what is being done now or any plans about the future, i.e., they prescribe decisions geared at maximizing inventory system performance. Prescriptive Analytics could be used to optimize the complete inventory policy. Prescriptive Analytics builds on the foundation of predictive analytics and adds optimization capabilities.
Diagnostic supply chain analytics equips supply chain managers with the knowledge to recognize when data gives them a story they do not understand. When combined with solid visualization technology, it can explain data anomalies and better understand departures from averages, trends, expectations, or norms. It varies from other types of analytics, such as descriptive analytics, in its ability to isolate individual supply chain events and answer crucial concerns managers may have, such as how and why sales in a specific region have been affected.
As the name implies, cognitive analytics is the process of integrating artificial intelligence and machine learning to aid retailers in making quick business choices. Unlike linear data distribution systems, cognitive analytics continuously watches data across all parts of the supply chain to make fast decisions that minimize risk.
Importance of Supply Chain Analytics
The Supply chain analytics space can help a company make better informed, prompt, and efficient decisions.
The complete data is accessed to get a continuously integrated planning strategy and real-time visibility into diverse data that fosters operational efficiency and actionable insights.
By recognizing patterns and trends throughout the supply chain, supply chain analytics may help predict future hazards and find known issues.
The supply chain analytics space can help a company better estimate future demand by studying client data. It helps a company decide which items can be reduced in price when they become less profitable and figure out what the client wants after the first order.
Lean Supply Chain
Companies can use supply chain analytics to watch warehouses, partner reactions, and consumer needs for better-informed decisions.
For supply chain management, companies are now offering advanced analytics. Advanced analytics can analyze both structured and unstructured data, giving businesses an advantage by ensuring alerts arrive on time, allowing them to make the best decisions possible. Advanced analytics can also create correlations and patterns between multiple sources, resulting in alerts that reduce risk with minimal cost and environmental impact.
Companies may see other benefits as technologies like AI become more ubiquitous in supply chain technology. Because of the constraints of evaluating natural language data, information that could not previously be processed can now be studied in real-time. AI can read, understand, and correlate data from various sources, silos, and systems quickly and comprehensively.
It can then perform real-time analysis based on the data interpretation. Companies will have access to far more information about their supply chains. They can improve their efficiency and reduce disruptions while supporting new business models.
Magistral Supply Chain Analytics Services
Magistral’s outcome-oriented services enhance the required supply chain to be more flexible, precise, granular, and efficient. The numerous services provided include:
1. Category Intelligence
2. Commodity Intelligence
3. Procurement Analytics
4. Supplier Engagement
5. Supplier Risk Intelligence
6. Transportation Analytics
The value of category information has grown over time as procurement intelligence has evolved from a cost-cutting function to a strategic business one. Category managers are now expected to generate value throughout the supply chain, and as a result, they can no longer rely on direct procurement categories’ typical cost-cutting optimization strategies.
Monitoring commodity price predictions and trends is an integral part of strategic plans for procurement teams and organizations. It helps make data-driven planning and choices, foresee pricing-related risks, and manage suppliers proactively while avoiding supply chain disruption caused by price fluctuation.
Procurement analysis gathers data and applies analytical tools to gain actionable insights and enhance decision-making to optimize S2P processes. It can help perfect every stage of the process, reduce related risks and operational costs, and gain a competitive edge if adequately studied.
Supplier negotiations are typically done in person, but modern technology that allows for two-way communication can also be used. Supplier risks are identified and negotiated to find any supply chain risks associated with the product or service. Participation in price discussions using the information and insights obtained throughout the negotiation is vital.
Supplier Risk Intelligence
Critical insights on sourcing and supply chains throughout the industry could be better understood by evaluating the advantages and disadvantages of each supplier’s performance. Supplier risk analysis can proactively aid the company by continuously evaluating each supplier’s performance and health and their associated operational value chain to watch and minimize supplier risk.
Transportation management is evolving thanks to supply chain technology fueled by data and analytics. These practical tools aid businesses in being more educated, efficient, and long-lasting. With end-to-end visibility provided along with the supply chain and its moving pieces, a data analytics solution provides better order fulfillment, shipment and delivery tracking, and future planning accuracy.
About Magistral Consulting
Magistral Consulting has helped multiple companies to reduce operations costs through its offerings in Procurement and Supply Chain offerings.
About the Author
The article is Authored by the Marketing Department of Magistral Consulting. For any business inquiries, you could reach out to firstname.lastname@example.org