Manufacturing Data Analytics

The heart of a manufacturing unit is the shop floor. With process automation making inroads into production through various software tools, data analytics can de-silo data generated. Our production analytics solution employs machine learning capabilities of artificial intelligence to uncover insights on patterns and trends to identify/correct process flaws, implement stringent quality control and maintenance schedules to improve productivity. A data-driven approach can drastically reduce lead time from test to prototype to production.

A smart work order management removes the uncertainty from the process by automating all activities from identification to resolution; this includes raising and routing work orders, identification, management of vendors and final inspection and acceptance. Our work order management module incorporates data analytics for the selection of vendors based on vendor credentials and devises a path for easy resolution of the issue.

With the increase in the complexity of the manufacturing process, sensors have become an integral part of the equipment. Maintenance departments can harness insights into the real-time conditions of the process and equipment. Data analytics aids the implementation of condition-based maintenance practices where maintenance personnel can undertake repair and maintenance activities at the precise time it is needed without waiting for scheduled maintenance requirements. Maintenance is as central to manufacturing as production and contributes significantly to expenses. But equipment breakdown and maintenance is the part and parcel of the asset-intensive manufacturing sector. Hence modern manufacturing industries have shifted to proactive maintenance schedules using continuous monitoring through sensors. Analytics solutions use machine learning and robotics in fault detecting, diagnostics, and correction. Analytics can craft algorithms based on both historical and current data to uncover issues before they blow up. Thus predictive maintenance saves money and extends equipment life ensuring efficient operations. Our prognostic maintenance and fault detection analytics extracts insights from data extracted from monitors and sensors on equipment to make accurate diagnostics and predictions.

The supplier is an important cog in the machinery of the supply chain. As a part of supply chain analytics suite supplier performance management analytics aids measuring and analyzing suppliers' performance across the enterprise. Our supplier performance analytics provides a real-time evaluation of suppliers against comprehensive preset metrics to measure all your suppliers and generate reports making the process transparent and collaborative.

The supply chain controls the flow of goods and services that convert raw materials into final products. With manufacturers sourcing raw materials from all over the globe, the modern supply chain has become complex. A data analytics solution can streamline the process by generating data and analyzing them in delivering visibility to key supply chain parameters. Our Supply chain analytics employs artificial intelligence, machine learning, and data modeling to generate algorithms that can analyze historical and current data to predict future trends and events and also to uncover actionable insights and solutions that help organizations achieve process optimization.

For the success of any manufacturing activity, it is obligatory to understand and meet customer needs and expectations. Advanced analytics can help the assimilation of information from surveys and forms identify recurring trends and align this with customer preference to gain a clearer picture of future demand. Data analytics offers insights that can help management in making the right strategic decisions to bolster bottom lines.

Efficient warehouse management is critical for the success of a robust supply chain. With the opening up of digital shopping outlets, business houses have to rethink warehouse management from traditional storing and transporting to cater to not only physical stores but also e-commerce fulfillment centers. Modern warehouses have adopted process automation to improve efficiency and track products. To make sense of the vast data generated data-driven smart warehouse management from the bouquet of our manufacturing analytics solution, we can collect and analyze the data generated offering deeper insights. These can be viewed as dashboards and stored as a customised report.

Demand volatility has been the bane of manufacturing industries, but not anymore with the advent of data analytics using predictive analytics and machine learning technologies. Traditionally demand forecast involves using statistics on historical data from supply chain and market research by specialists. Data analytics for demand forecasting and inventory control uses internal data from sales reports, market research reports and external data from economy and social media trends to make accurate data-driven predictions. Demand forecasting benefits manufacturing units by allowing better management of inventory, plan assets optimization and improved vendor engagement.

Our Warehouse analytics, in addition to predicting demand, plan inventory incorporates:

• Personnel management module that estimates man-hour requirement for tasks and plans deployment
• Space Management module within the warehouse it devises goods storage layout based on size and quantity to optimize warehouse space
• These systems help you maximize space by optimizing warehouse layout and storing cargo in the most appropriately sized location
• Loading/unloading scheduler plans to dock at the warehouse to efficiently manage delivery and pickup based on schedule
• Inbound and outbound analysis
• Personnel management module that estimates man-hour requirement for tasks and plans deployment
• Personnel management module that estimates man-hour requirement for tasks and plans deployment

If you are looking for improvement in productivity, energy efficiency, quality control and procurement you are in the market for production analytics. Production analytics is one of the major wins for any manufacturing analytics solution. Our production analytic module can assimilate data from sensors, consoles and software tools to unearth profound insight s. Inbuilt advanced data analytic capabilities use root cause analysis to support manufacturing optimization.

Some of the areas where production data analytics can be effectively used are

• Productivity improvement
• Product profitability / contribution improvement
• Product quality and defects tracking
• Increasing energy efficiency
• Efficient material requirement planning

Machine utilization contributes significantly to productivity and cost. The traditional report based monitoring process is time-consuming and inaccurate. Industries with distributed manufacturing facilities are leaning towards data-driven monitoring solutions. IoT based analytic tool allows monitoring of machine utilization remotely by collecting data generated by sensors and analyzing it. The results of the analytic tools are displayed in real-time on a dashboard to enable managers to take corrective measures. Implementation of fault detection and diagnostic analytics tool uses information from equipment sensors and process monitors to detect anomaly in product helping early course correction before final production.

Benefits of Data Analytics in Manufacturing Industry

Disparate manufacturing equipment and connected devices are the hallmark of modern manufacturing facilities. With it comes the challenge of implementing data analytics by collecting, storing and analyzing g data but the benefits from data analytics are far-reaching. Invensis data analytics collates data from varying sources analyses using artificial intelligence and the Internet of things to craft advanced algorithms to harness the power of the data.

• Improved availability of machinery hence asset optimization
• Shortened Product development timelines
• Improved quality management
• Enhanced customer experience through reliable product availability in time
• Lower production cost due to improved efficiency and decreased expense
• Improved energy efficiency
• Improved visibility into manufacturing operations offering better control over operations
• Better performance Monitoring of systems and applications