Today’s manufacturing environment is highly dynamic & complex and consists of various stakeholders like multiple suppliers & partners, multiple distribution channels, multiple fulfillment partners, multiple customer types etc. Decisions have to be taken on business performance and overall supply chain which should be very vital for present and future of the organization. Analytics will help take these decisions for manufacturing organizations.
Analytics applications are of three main types – descriptive, predictive, and prescriptive. Descriptive analytics is the most widely used analytics methodology which has extensive use in manufacturing companies. Descriptive analytics deals with historical transactional data and creates analytics reports for taking decisions, measuring KPIs, and helping extract actionable intelligence. Predictive analytics also uses historical transactions but it predicts the probable future value. For example, the chance of slippage or failure for procuring any part from a specific vendor or chance of slippage or failure in transporting through specific 3PLs can be predicted by predictive analytics. Predictive analytics forecasts future bottlenecks which an organization may face.
The inclusion journey of analytics begins with reactive decision-making (by reports, temporary reports, instant query, and drill drown) that helps get answers to questions like what has happened and how and where it has happened. With the usage of advanced analytics, manufacturing organizations can proactively take decisions and differentiate themselves from competition with predictive analytics and prescriptive analytics. This will answer what should happen next and how to avoid any uncomfortable situation. This also helps in anticipating current and future trends.
Prescriptive analytics can tell the organization how to overcome future challenges. This is kind of a prescription of activities to avoid any untoward incident in the future in terms of nonachievement of targets, shortages of resources, or reduction in cash flow. Simulation, Optimizations and Machine Learning techniques are broadly classified under Prescriptive analytics. These are used in each and every manufacturing value chain component so that organizations can avoid any type of failure in future. Scenario planning is a very common outcome of a prescriptive analytics solution where various possible outcomes are compared with each other.
In procurement functions, prescriptive analytics helps organizations decide the optimum sourcing locations, optimum inbound logistics route, optimum quantity to procure, simulated procurement cost and savings, projected KPI values, and other parameters. In planning functions, prescriptive analytics helps organizations determine the optimized quantity of multi-echelon inventory, optimized master production schedule per proper capacity utilization, scenario planning, and simulated cash flow for various scenarios. In distribution functions, prescriptive analytics helps decide the optimum outbound logistics route, select right distribution center or warehouse, optimize truck loading, simulated cash flows, and others.
Nowadays, almost all COTS products have prescriptive analytics solutions built in. The key is to enable the organization to consider any number of constraints across the value chain depending on the situation. This will result in improving the efficiency of the organization. Prescriptive analytics should allow companies to include external information for better decision-making. In the ongoing business scenario, it is important that enterprises use prescriptive analytics to avoid the uncertain or untoward incidents. Prescriptive analytics can be included in big data analytics or cloud analytics platform or in-memory analytics.
HCL has a strong focus in building capability in prescriptive analytics area.