Big Data is not a new trend since the accumulation and analysis of data for business process improvement is something that has been around since the late 20th century. The difference is that nowadays data is easier to capture and display in a more presentable way so that everybody can understand it and use it for their benefit. From quality control to predictive maintenance of equipment, big data provides many tools that manufacturers can make use of to improve their businesses and generate value out of what they already have.
What is the benefit of this?
Big Data essentially entails having a table with all the information about your manufacturing company: number of products in production, number of finished products, quality of the product, availability of raw materials, orders placed by clients, procurement, status of the manufacturing equipment, information about your clients, etc.
Since every industry is different, and has their own challenges with implementation, this idea of a fully integrated data engine may sound utopian for many manufacturing firms. However, Big Data analysis tools such as Rapidminer, Qubole, Oracle Data Mining, Statwing, IBM SPSS Modeler, Karmasphere, IBM InfoSphere, or EnterpriseDB have already made their mark in this area in a significant way.
These computational analysis tools can help to find patterns and relationships among process steps and inputs, identify core determinants, perform hypothesis tests, optimize factors that strongly affect yield, and utilize all meaningful information to avoid waste, downtime, or machine breakdowns.
Big Data applied in Manufacturing
For the implementation of a Smart Factory (Industry 4.0), Big Data is a key component. With the use of big-data, you can optimize production schedules based on suppliers, customers, machine availability and cost constraints. All these benefits enable manufacturers to regulate their operations in order to maximize production efficiency and responsiveness.
Here is a list of 8 examples that speak about the ways in which Big Data can improve the manufacturing industry:
1 – Improving manufacturing processes
With big-data, you can gather information about processes and analyze them to understand variability in production, quality issues, production downtime, etc among other problems that will result in savings through early detection.
For example: in a McKinsey case study on the pharmaceutical industry; a company monitored live, genetically engineered cells having 200 variables for tracking the purity of their product (vaccines and blood components). They found that 2 batches of the product had 50 to 100% of the variation in comparison to the others. Using big data analytics, they found 9 parameters that had direct relation with vaccine yield, and by modifying some target processes, the company was able to optimize these parameters and increase vaccine production by 50%; resulting in savings of approximately $7.5 million dollars annually.
2 – Custom product design and production
One of the recent challenges in manufacturing is an increased emphasis on customizing products for the customer, which entails moving to a strongly customer-driven production process. This is difficult because it implies more machine changeovers or tooling changes, that would inadvertently increase production downtime as well. This can be tackled using big data, as companies can use big data analytics to map the behavior of customers, using sophisticated forecasting and predictive modeling techniques; preparing your production line to increase its efficiency in order fulfillment.
3 – Manage supply chain
Big data allows manufacturers to reduce risks in the delivery of materials for production. By considering various external factors influencing congestion of transportation routes, a company could predict the likelihood of on-time delivery beforehand. It would enable a manufacturing company to proactively develop contingency plans to minimize the influence of these factors on production.
4 – Understand performance across multiple metrics
In a manufacturing company, there are various metrics that determine the performance of a company in terms of cost, efficiency, and responsiveness. With big data, you can analyze all variables in a production process and see where problems may originate. This information could be valuable for an effective root cause analysis, as indicated by a survey by LNS research group – wherein 45% of respondents agree that Big Data has contributed towards a better understanding of different plan performances across multiple metrics.
5 – Provide service and support faster to customers
The aforementioned LNS survey also covers this the service aspect of manufacturing companies, since 39% respondents indicated that Big Data could improve a company’s performance in serving customers better. This area is very important because aside from providing a quality product, Big Data enables companies to build a strong feedback loop with customers in order to build trust over the long-term.
6 – Greater visibility of suppliers quality levels
Using big data analytics can also help manufacturers in supplier quality assurance. They can leverage useful performance data that will be needed in sourcing decisions for the future, while maintaining visibility on operational aspects of procurement; in order to have complete control over their supply chain.
7 – Qualify how daily production affects the financial performance of a company
Advanced analytics and Big Data could be used to build a sophisticated Activity Based Costing (ABC) accounting structure, to develop a real-time estimate of the impact of operations on the bigger picture. Although ABC is not always used by many account executives for bookkeeping due to its unorthodox nature, it could still be used for an internal assessment of production activities.
8 – Preventive maintenance
Now it is easier for a manufacturer to avoid breakdowns and downtimes because advanced analytics using Big Data can detect machine performance in real time, allowing companies to put standards at which every machine has to work; so that they can detect anomalies and plug in that information to predict production downtime.