Supply chain analytics refers to the methods and technologies used to compile and analyse information from multiple sources in order to gain an understanding of the many steps of the supply chain process, such as procurement, manufacturing, and distribution. By integrating data from your supply chain management (SCM) and enterprise resource planning (ERP) applications, you can gain a more complete view of the logistics network and broader insights that can help you predict and enhance performance.
Why Supply Chain Analytics Are Profitable
As global supply chains become more decentralised and susceptible to disruptions, it becomes increasingly important for all parties involved to maintain constant communication to ensure that products and services can be delivered quickly, with minimal waste, and with minimal setbacks. Using data analytics designed for the modern supply chain, you can access your entire logistics network in real time. The entire Supply chain analytics, not just the individual steps, is visible.
Here is a comprehensive list of all the benefits your company will enjoy:
- In order to increase order fulfilment and revenue, you must stock up on essential raw resources, components, and/or finished products.
- Effective risk management and proactive preparation for disruption necessitate anticipating market trends and supplier base shifts and responding to them as soon as they occur.
- Reduce your cost of goods sold (COGS) and increase your profit margins by reorganising your business processes and promoting a culture of efficiency across your supply chain.
- Resolving problems with shipping and receiving orders is a quick and simple way to boost customer satisfaction and retention.
- Accurate and actionable insights into the company’s real-time planning, sourcing, logistics and warehousing, and aftermarket operations will provide a comprehensive view of the organisation.
By consolidating data from various business divisions, you can make better-informed, more ethical decisions regarding your ESG-related business partners (environmental, social, and governance).
Let’s examine this diagram as a group.
The data originates from the operational systems that oversee the various processes along the supply chain, such as procurement, stocking, order processing, distribution, and transportation. It is also possible to integrate data from merchants, shipping companies, and manufacturers, among other sources.
After the information has been extracted, processed, and combined, it is stored in an online repository, such as a data warehouse or data lake. At the conclusion, you will have a thorough comprehension of your supply chain.
The supply chain data analytics tool you employ will facilitate the use of these data for a variety of analyses. Using supply chain predictive analytics to forecast returns, you can, for instance, determine how much inventory is still available.
With the aid of your tool’s interactive visualisations and dashboards, you can analyse key performance indicators (KPIs) such as fulfilment performance, current versus ideal inventory distribution, and transit times.
The most sophisticated tools allow you to utilise augmented analytics features such as automated machine learning (AutoML), predictive analytics, and prescriptive analytics, as well as embed your analytics within other applications.
This generates knowledge upon which you can act and/or which can trigger alerts and actions in other systems.
Analyzing the supply chain enables you to learn more about your company’s past, current, and projected future performance. This analysis employs one of the previously discussed sorts of analytics. For instance, a supply chain predictive analytics association model could be used to conduct a predictive inventory analysis. You can perform numerous types of supply chain analysis, including analyses of order correctness, on-time shipment, average fulfilment cost, and warehouse getting turnaround time.