A survey conducted by Deloitte states that 44% of businesses that have invested in supply chain analytics tools expect an 11% to 20% return on investment. It is evident that business intelligence and analytics tools have transformed the manufacturing and marketing sectors, also indicating a huge potential for big data analytics in supply chain management and optimization. In this industry, metrics and performance indicators are no longer useful and new approaches to the analysis and processing of large, unstructured datasets generated by the supply chain must be developed. Critical KPIs are closely correlated with real-time data collection and analysis since business performance is directly related to the speed at which decisions, backed by data, can be made. Supply chain analytics will assist enterprises in addressing tactical, logistical and strategic issues.
Importance of Data in Supply Chain Optimization
To put it simply, supply chain analytics serves as a link between data and making decisions. Comprehensive data analysis is the process of finding trends and insights to help supply chain managers make informed choices. The data can include customer and supplier information, inventory status and transportation expenses for the supply chain.
Regarding the analytical part, the goal is to identify methods for cutting expenses, expediting deliveries, optimizing inventories and upholding quality. The collection of data from several sources, including procurement, stock, order coordination, storage facilities and logistics, is required in order to perform this analysis. There are a lot of moving parts engaged at once, so keeping track of everything is essential.
Benefits of Supply Chain Analytics
Most data generated by the supply chain is unstructured and scattered. This data can be analyzed and processed to identify patterns and trends from unstructured as well as structured data with the use of artificial intelligence. Businesses will be able to leverage all their data and drive decisions to manage the supply chain with higher precision.
1. Better Risk Management –
Because new management systems for supply chains are frequently overpriced, decision-makers need to be well-versed in the advantages and disadvantages of each one. Analytics solutions help businesses fulfill the exceptionally high expectations of their clients, improve planning, anticipate and forecast possible/future dangers and expedite inventory management. An analytics platform can potentially highlight outliers, for example, pointing out that a particular courier has been shipping goods late for the past month on a regular basis. It can both identify this pattern and predict the probability of future delays. The platform can also determine the consequences of a delay, including the quantity of possible delivery problems and the expense of chargebacks and refunds.
2. Enhanced Client Loyalty –
Today, putting the customer first is the prime focus for any organization, and it won’t be changing in the near future. Marketing has an extensive past of using big data analysis, and supply chain data is a component of a more effective strategy when it comes to meeting client demands. Every organization has a lot of data from many parts of its operation, but not all of them have the resources to adequately assess and make use of it. When supply chain data is analyzed using the right technologies, businesses benefit by swiftly anticipating problems that could affect customer satisfaction, minimizing risks and accessing and satisfying needs. .
3. Increased Traceability Across the Supply Chain –
Enhancing coordination and movement across stakeholders is the capability of tracking goods throughout the supply chain, from places of manufacture to points of sale. Combined with data science of the supply chain, data matrix codes, monitors, and other loT devices let businesses detect and prevent product spoiling, fix delivery errors, forecast delays, identify fraud, and improve inventory management. By providing the most comprehensible graphs, statistics, and alarms to authorized staff, data analysis systems consolidate all the data into a single source.
4. Inventory Optimization –
Overstocked merchandise and shortages in products are the outcomes of poor inventory management, and neither is desirable. Running short on inventory results in missed sales, whereas having too much inventory means greater stock holding expenses. Businesses can maintain the ideal stock balance with supply chain analytics to minimize expenses and prevent stock-outs. The platform can send out alerts in advance for SKUs which are about to run out, based on the typical lead time associated with the supplier. The operations staff can also benefit from sales patterns in identifying which items need more storage space and which can be phased out or held in smaller quantities.
5. Improved Precision in Planning –
Predicting customer demand is crucial for a logistics organization because it drives every aspect of supply chain operations and serves as the foundation for scheduling acquisition, production capacity, logistics, sales, and other activities. Supply chain analytics can assist organizations to estimate future demand by examining customer data. For instance, it will help businesses decide which goods to cut back on to mitigate potential losses or which ones will remain in demand.
Conclusion
C-suite executives agree that data is essential for business optimization. It’s obvious that the entire supply chain is a trove of data that can be utilized as a basis for making substantial profits.