Big Data for Business Optimization

Through the implementation of data mining and machine learning techniques, companies can improve their efficiency and optimize their production processes.

Friday, July 12, 2019


Analyzing large volumes of data to make decisions that result in better results for a company applies not only to the commercial and sales field, but also to other areas of the same or even more sensitive companies: the production process.

For example, in companies in the industrial sector, the production chain is the heart of the company, and its proper optimization is fundamental to the performance of the entire organization.

Can Big Data be used to minimize costs and improve the performance of a production process? Absolutely.

In its article published on, Vegard Flovik explains that "... Product optimization is a common problem in many industries. In our context, optimization is any act, process or methodology that makes something, such as a design, system or decision, as good, functional or effective as possible. Decision processes to find the minimum cost, the best quality, performance and energy consumption are examples of such optimization."

"... Fully autonomous production facilities will be here in a not-too-distant future. But even today, machine learning can make a great difference to production optimization. Here, I will take a closer look at a concrete example of how to utilize machine learning and analytics to solve a complex problem encountered in a real life setting."

"... To further concretize this, I will focus on a case we have been working on with a global oil and gas company. Currently, the industry focuses primarily on digitalization and analytics. This focus is fueled by the vast amounts of data that are accumulated from up to thousands of sensors every day, even on a single production facility. Until recently, the utilization of these data was limited due to limitations in competence and the lack of necessary technology and data pipelines for collecting data from sensors and systems for further analysis."

Flovik summarizes the production optimization model developed for the oil and gas company in three main components:

"1. Prediction algorithm:
Your first, important step is to ensure you have a machine-learning algorithm that is able to successfully predict the correct production rates given the settings of all operator-controllable variables.

2. Multi-dimensional optimization:
You can use the prediction algorithm as the foundation of an optimization algorithm that explores which control variables to adjust in order to maximize production.

3. Actionable output:
As output from the optimization algorithm, you get recommendations on which control variables to adjust and the potential improvement in production rate from these adjustments.

Read full article: "How to use machine learning for production optimization"

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