Big data analysis in manufacturing

Nowadays, more and more organizations are using big data to initiate the transition to a new digital technological structure in their communications, decision making, situation assessment and business process support.

There’s a certain level of enthusiasm towards artificial intelligence mathematical algorithms allowing to understand how business processes can be improved. At the same time, quite often machine learning is regarded as a sort of a magic wand. Unexperienced executives think: “Come on, I’ll just wave it and easily achieve my business goals”. With that wrong picture in mind, industrial enterprises are eager to implement such methods by actively initiating pilot projects. What’s the problem then? Quite often they turn out to be very limited and carry enormous risks. No wonder that, eventually, they miserably fail. So what does it take to bring such projects to a successful ending?

The modern manufacturing is all about automated business processes behind such important economic indicators as costs per product unit, costs associated with breakdowns, equipment downtime, delivery and storage of both raw materials and products, performance and overall equipment efficiency.

Continuous improvements, based on the analysis of factual data, pass through many well-known management concepts and methodologies: Six Sigma,Kaizen, lean manufacturing, etc. However, despite the depth of development, these methodologies are based on basic statistical analysis for data processing, usually associated with small samples of data. In this regard, machine learning is a powerful tool that complements the classical approaches to optimize production.

Let’s list the most common types of projects based on machine learning technologies, aimed at obtaining additional revenues or reducing costs:

• the increase in the productivity of technological processes due to the selection of the equipment’s optimal operation modes, raw material loads, etc.;
• the improvement in product quality by identifying critical factors in the production process that affects the final result;
• the optimization of technological maintenance and repair (MRO) of expensive production equipment, the prediction of breakdowns and equipment degradation;
• the optimization of product testing costs through a digital product model and virtual sensors;
• the pricing and supply chain management – optimization and forecasting for the procurement, delivery, storage, supply and demand processes;
• the comprehensive improvement of performance indicators by identifying latent factors affecting production processes, and applying situation modelling in digital environments.

Despite the benefits above, there’s a recurring question in the industry: “Is it profitable to invest in the implementation of Big Data now?” Definitely yes. For example, Caterpillar experts saythat the annual losses of its distributors due to delays in the introduction of new information processing technologies amount to $9–18 billion.

Having said this, let’s see in what ways Big Data has an impact on manufacturing units across factories. First of all, it’s all about manufacturing data analysis. Similar to other industries and domains, the modern information systems that support manufacturing intelligence share the responsibility of storing increasingly large data sets (or Big Data). Besides, courtesy of analytic algorithms, those systems are supporting the processing of Big Data in real time.

The use of advanced analytics allows manufacturing professionals to process key performance indicators, take key insights and follow corresponding steps to improve the quality of production. Thanks to the Six Sigma program mentioned above, companies can significantly reduce waste which leads to saving funds. It also influences the variability in production. For instance, factories and plants involved in pharmaceuticals business can profit from extreme ranges of variations accounting for the number of complexities involved in the production.

It’s important to estimate the right quantity of goods in order to keep up with market demands. In this case, the data analysis equals a greater precision, and manufacturers can draw their sale plans in anticipation of demand which leads to the stability of pricing, increased profits and business growth.

The integration of big data in manufacturing has a strategic value, and its use at factories has never been more critical than now. With the right platform to manage data, manufacturers can finally strengthen the relationship with customers, partners and suppliers.

To sum up

Industry 4.0 dictates its own rules. Big Data solutions, as a result of the Internet of Things development, have changed established (but already outdated) business models. Like it or not, it would be meaningless to deny it. Large industrial enterprises are simply obliged to use new technological developments. Otherwise, their economic results will fall sooner or later. Or maybe you know the other way to compete with more advanced “market neighbors”?

Fair enough – the focus on big data technologies in manufacturing is a relatively new area. Among others, Big Data analysis incorporates automation, engineering, information technology and data analytics. Here and now, it’s important to have a clear vision of the manufacturing domain to identify areas to focus their research efforts. After all, with all the quick pace of technologies, it won’t take long before the next-generation infrastructure and technologies will take their place at modern factories once and for all.