Manufacturing sector has been growing at a CAGR of 7.32% between FY12 and FY18 being one of the high growth sectors. Moreover the ‘Make in India’ has further given manufacturing a global recognition.

In the event of such prosperity it is important the manufacturing sector streamlines its operations and optimize productivity through efficient management of supply chains and plants. To achieve this it has to make the best use of one its assets: the data. The growing popularity of Big Data and IoT enabled sensors and machines and predictive analytics through advanced analytics and algorithms help gain useful insights about the pattern in data and assist in converting them into actionable insights.

The major areas to which analytics contributes to manufacturing:

Quality Assurance: The definition of quality differs across organizations. In general quality refers to the ability to meet a consumer’s expectations or their intended use of the product. Analytics helps evaluate vital parameters of quality:

• First pass yield, i.e., (units with no rework/scrap counted as coming out of an individual process.
• Check consumer complaints due to quality of services or products.
• Checking whether order has been delivered on time.

Overall Equipment Effectiveness (OEE): This is one of the most important key performance indicators when it comes to determine a true productive manufacturing line. In a manufacturing process not all lines are 100% productive. The parameters dealt with are:

• Tracking individual machine performance
• OEE, availability, quality and performance

Increasing production utilization: The goal of a manufacturing industry is often to optimize production utilization in order to increase profitability. In manufacturing plants and factories with every production stop the opportunity cost of profit increment rises since output that could have been produced is not generated and thereby undermines profitability. The parameters concerned with production utilization:

• Analyzing utilization rate for a period of time or for a product
• Understanding total downtime to analyze trends and problematic areas

Forecasting Demand: Advance analytics and Internet of Things have enabled manufacturers to forecast demand for their products based on past availability of data. It is very essential to keep track on production trends and gaps in order to drive production and operational efficiency.

Managing Supply Chain Risk: The most widely faced risk by a manufacturer is the risk pertaining to delivery of raw materials and the objective is to minimize such risks in order lessen costs. Predictive analytics help estimate the probable delay and uses this information to backup suppliers and implement any contingency plans to avoid any interruption in production.

Analytics helps in consolidating and summarizing data to form metrics that can be used a standard form of component on a dashboard. Algorithms are developed to automatically find anomalies and scoreboards are used to see performance of the machines and plants.

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