From equipment log sheets to digital spreadsheets, data capture is an ingrained routine of the manufacturing function. Without data, a factory foreman cannot steer manufacturing efforts in the right direction to meet production budgets. Without data, the operations manager cannot predict how and where resources are being spent and how justified the yield is. Data is an intangible compass that helps manufacturers stay competitive, make logical decisions and increase yield per input, while continuously lowering costs.
Big Data & Manufacturing – A Perfect Combo To Beat The Industrial Odds
Manufacturing 4.0 has turned factory floors into real-time data churning surfaces. Internet of Things, Human-Machine Interfaces, and Connected Manufacturing create several forms of data like temperature, velocity, volume, speed, etc. These datasets meet the basic criteria that define Big Data: variety, volume, and velocity.
Leveraging Big Data Analytics would help manufacturers achieve the economic goals of cost-reduction, yield maximization, and accelerate production.
Here are five ways how the power of Big Data analytics can contribute to improving manufacturing efficiency.
1.Optimizing Manufacturing Process
Dozens of equipment and scores of ingredients come together on the factory floor to form the final product. From the moment the materials go in for production until they are released as finished goods, there are several data points that can tell whether the manufacturing process is working at an optimal state or not.
McKinsey reports how a biopharmaceuticals maker was able to maximize their vaccine production yield by analyzing the production data. Big Data helped the manufacturer to relate data of closely located production processes and the materials used in them. Using Big Data Analytics statistical patterns that indicate material interdependencies were created, which helped devise a new production plan. The revised production plan gave more produce without demanding additional capital expenditure.
2.Predict Customer Behavior
Manufacturing is not a customer-facing business function. However, manufacturers need to have a pulse of what customers want, when they want it and how quickly they want it. In the words of Bryan Goodman, Research Scientist at Ford, “Quite a few customers walk into a dealership and want to leave with a vehicle that day… We have to get the right vehicle with the right engine and right set of features and controls to the right dealerships.”
Ford developed an in-house Big Data analytics based system called the Smart Inventory Management System (SIMS), which helped dealers forecast inventory requirements with accuracy. SIMS connects data between Ford’s manufacturing volumes, sales volumes, regional employment rates, population and even mean income levels to forecast customer demands.
3.Enhancing Quality Assurance
Big Data can help develop unexpected insights about quality assurance. Manufacturers can correlate several dynamic parameters like temperature, valve pressure, carbon dioxide flow, etc. to arrive at process controls that can enhance quality. For instance, previously unknown temperature sensitivities that could be reducing output can be identified with the help of Big Data.
Unlike traditional analytic systems that rely on standalone datasets, Big Data uses neural-network techniques that mimic the human brain in understanding data and arriving at conclusions. This helps in connecting the dots between two seemingly unrelated parameters that could be affecting the overall yield.
4.Recommending Preventive Maintenance
General Electric, the $145 billion manufacturer of heavy-duty equipment, is setting benchmarks for the entire manufacturing fraternity by betting big on Big Data. The conglomerate uses IoT sensors on its jet engines, wind turbines, oil and gas pipelines and many other large-scale manufacturing equipment to create data streams.
These data streams are analyzed with the help of Big Data analytics to predict when a machine might probably break down. Based on such forecasts, preventive maintenance can be carried out, thus preventing time delay and loss of resources that the break down would cause.
5.Optimizing Inventory Management and Supply Chain
Another manufacturing aspect where Big Data can shed a positive influence is inventory procurement and management. Big Data helps analyze historical information as well as real-time supply chain management metrics to devise sound inventory procurement and management plans.
Tesco, one among the Big Three of the UK’s food retail, has been leveraging Big Data since 2006 to improve its supply chain. Big Data helped the retailer understand customer buying habits with accuracy and also evaluate the effectiveness of their loyalty programs.
Big Data Means Big Opportunities for Manufacturers
Big Data helps draw granular level insights out of data mountains. For manufacturers, it is an invisible ally that can say a lot about the business, the health of its operations and the areas where improvement is mandatory. From amplifying manufacturing efficiency to reducing defects, downtimes and costs, there is a long list of benefits that Big Data can provide.
Big Data wins where traditional analytic systems fail short. It brings together data from diverse sources and creates a single data bank from which information can be crystallized for decision making. With the surge of Manufacturing 4.0 and Connected Manufacturing ecosystems, Big Data is certain to be here for long.