Operational Analytics

Absolutely! Supply Chain Analytics can help an organization make smarter, quicker, and more efficient decisions. Companies can reduce costs and improve margins by accessing comprehensive data to gain a continuous integrated planning approach and real-time visibility into the disparate data. Operational Analytics for Supply Chain (or rather Supply Chain Analytics) can identify known risks and help to predict future risks by identifying patterns and trends throughout the supply chain.

On the upside, by analyzing customer data, Supply Chain Analytics can help a business better predict future demand. It helps an organization decide what products can be minimized when they become less profitable or understand what customer needs will be after the initial order.

Manufacturing companies can use Operational Analytics to monitor heavy machine operations. They can deploy an Analytics-powered Predictive Maintenance framework to collect, manage, and leverage intelligent data usage. This framework allows the companies to focus on asset health and perform services and repairs based on the asset’s proclivity to failure to meet prescribed performance objectives. The ultimate purpose of Predictive Maintenance is to predict when the equipment might break down, followed by preventing the machine failure through regularly scheduled and corrective and preventive maintenance activities.

Using Route Optimization powered by Operational Analytics, a logistic company can determine the most cost-efficient and shortest path between different points. Shipping Route Optimization is more complex than finding the shortest delivery path between two points. It needs to include all relevant factors, such as the number and location of all the required stops on the route and then time windows for delivery.

Inventory Optimization is the process of maintaining the right amount of inventory required to meet demand, keep logistics costs low, and avoid common inventory issues such as stock-outs, overstocking, and backorders. Operational Analytics enables Inventory Optimization, allows to reduce working capital by reducing unnecessary inventory buffers, improves service level, and ensures that their customers/retailers get what they want and when they want it, without holding too much inventory at the manufacturing site. Operational Analytics is also an important aspect of Finance and Operations Analytics that empowers manufacturing businesses with Just-in-Time manufacturing and reduces operational costs.

Inventory Optimization can be achieved by blending a range of aspects and looking at data through various lenses. Some of the most critical aspects are - 
1) Demand Forecasting

2) Tracking changes in buyer behavior 

3) Stock Replenishment and Supplier Reliability

Each of these aspects can be predicted to a high degree of accuracy by deploying Operational Analytics with the relevant Data Science models and then bringing all of them together by virtue of highly agile, data and scenario simulators. 

A supplier scorecard is a document that allows a business to measure the performance and effectiveness of a vendor over time using relevant metrics. This scorecard is powered by Operational Analytics. It breaks down supplier performance into categories and factors that can be quantified. It helps identify gaps where your suppliers can improve performance. It also helps determine RoI for the supplier spend and eliminate chinks in the supply chain.

Operational Analytics helps create an optimal production plan and schedule. Most manufacturing facilities often have multiple scheduling constraints that must be accounted for during the creation of the production schedule. Operational Analytics allows you to compare the schedule plan to the actual production output and determine whether the schedule created is an accurate and feasible representation of what can actually be produced. Having a realistic and feasible production schedule allows you to optimize resource capacity even amidst demand-supply fluctuations.