A supply chain is a network of individuals, businesses, organisations, assets, tasks, and technological developments involved in producing and distributing a product. Supply chain management is a complex process with multiple steps and storage options. The success of a business depends on how successfully it handles its supply chain.
Several supply chain management courses are available for individuals interested in learning more about supply chain management. One can improve supply chain performance and introduce a unique framework for strategically managing and optimising decision phases in supply chains.
This article will look at the importance of BI and data analytics in supply chain management and how they may help firms make better choices.
Importance of Business Intelligence in Supply Chain Management
Business intelligence (BI) is essential in supply chain management. Here are some of the advantages of using business intelligence in supply chain management:
Insights on emerging threats in real-time: Every supply chain faces risks from various sources, ranging from the loss of significant suppliers and consumers to logistical inefficiencies. BI tools help businesses recognise and solve supply chain concerns by providing real-time insights into emerging risks.
Improved decision-making: BI tools enable teams to focus on specific portions of massive volumes of data that would otherwise be impossible to analyse. Several factors hidden within enterprise data impact supply chain goods and processes. When used correctly, BI may be used to improve decision-making.
Optimisation of supply chain management: By providing a comprehensive company view, BI technologies enable businesses to better their supply chain management. It allows one to understand the flow of suppliers better and identify any problems before they become major concerns.
Set attainable objectives for the future: BI takes time to grow. However, once set up, it assists businesses in setting realistic goals for the future.
Self-service BI: It goes a step further by allowing users to run their queries and create their reports, even if they have no training in statistical analysis. It allows team members to get the information they need quickly.
Understanding Data Analytics and its Role in Supply Chain Decision-Making
The practice of analysing data to gain insights and make data-driven decisions in supply chain management is known as supply chain analytics. It enables businesses to predict future demand better, recognise risks, and reduce inventory costs. By analysing consumer data, supply chain analytics may help organisations predict client desires after the first purchase and decide which goods may be deleted when they become less profitable.
Data visualisation, the capacity to slice and dice data from several perspectives, and stream processing to get insight from various data streams offered by IoT applications and weather are all standard supply chain analytics software features.
Key Applications of Data Analytics in Supply Chain Management
Here are some important data analytics applications in supply chain management:
Demand forecasting and planning: Predictive analytics on POS terminal data kept in a demand signal repository may aid a corporation in predicting client demand.
Inventory optimisation: By assessing data on elements such as lead times, demand volatility, and supplier performance, supply chain analytics software may aid in optimising inventory levels.
Logistics and transportation optimisation: By assessing data on characteristics such as delivery times, transportation costs, and route optimisation, data analytics may aid in optimising logistics and transportation.
Supplier management: Data analytics may assist in supplier management by assessing data on characteristics such as supplier performance, quality, and delivery deadlines.
Risk management: By assessing data on elements such as supplier financial stability, geopolitical threats, and natural disasters, data analytics may aid in controlling supply chain risks.
Challenges and Limitations of Implementing Business Intelligence and Data Analytics in Supply Chains
Adopting business intelligence and data analytics in supply chains may encounter several challenges and limitations. Here are a few examples:
Limitations of business intelligence technologies: Business intelligence tools may not be capable of forecasting future events. They may also focus solely on specific business problems, making investigating all factors influencing order timing hard.
Technical and project management issues: It may take much work to deploy business intelligence software and develop a successful BI strategy that meets company goals and achieves the promised return on investment.
Integrating data from several sources: Organisations may need to collect data for analysis from various databases, big data platforms, and business applications, both on-premises and in the cloud. A common strategy is to deploy a data warehouse as a central location for BI data.
Enormous amounts of data: In general, supply chains generate a vast volume of data, making it difficult to assess and correlate data from many sources, silos, and systems.
Scaling analytics capabilities using real-time data: In the future supply chain, comprehensive and quick insights are required, and delays are unacceptable.
Risk of acquiring incorrect or harmful insights from data: Multiple factors buried within company data influence supply chain items and processes, and business intelligence can be used to improve them. However, if used correctly, organisations can avoid deriving incorrect or harmful insights from data.
Difficulty processing large quantities of data: Business intelligence enables teams to focus on specific segments from massive amounts of data that would otherwise be impossible to interpret.Customers are seeking more openness in the supply chain than ever before. Organisations may track the flow of goods through the system and reveal possible issues by mapping and monitoring the supply chain.
Future Trends in Business Intelligence and Data Analytics for Supply Chain Decision-Making
Business intelligence and data analytics are becoming more critical in supply chain decision-making. Real-time data analytics provided by business intelligence aids in supply chain management. Self-service analytics and data product chains are two of the top business intelligence trends for 2023.
Breakthroughs in sales forecasting, predictive analytics, reporting, and customer value analysis are among the future advances. In the fight against the supply chain issue, supply chain management systems based on decision intelligence and real-time prescriptive analytics models are valuable instruments.
Conclusion
The Indian Institute of Technology (IIT) Guwahati has introduced an advanced digital supply chain management certification programme in partnership with Imarticus Learning. The six-month plan consists of live online sessions and campus immersion activities. To provide students with the chance to learn from specialists in the business, subject matter experts prepared the course material.
Seven modules in the curriculum address the foundational elements of digital supply chain management and strategy, including supply chain principles, inventory management technology, supply chain analytics, supply chain risk management, decision phases in supply chains and more.
The supply chain management course provides training in the optimal use of technology to improve operational efficiency and decision-making across the supply chain. It aims to accelerate learners' careers in global Digital Supply Chain Management.
