Data is an important success factor for companies. Yet it is not uncommon for valuable insights and business intelligence to be sought elsewhere than within a company. Even if there is an awareness of the far-reaching potential of data, a lack of knowledge or experience prevents many companies from exploiting the associated benefits. Yet in industry in particular, improving processes is a constant imperative. However, this is often countered by the fear of having to invest considerable resources in order to build a consistently evidence-driven organization.

Of course, they have a point. It takes experienced data scientists, the most advanced technologies, and the right infrastructure to get ahead. On the other hand, what are the costs of underestimating or missing out on opportunities for growth and innovation? So what's the point of industrial companies investing in Data Science? Can data be the driving force to stay competitive on a global scale? In this article, we get to the bottom of it.

What is Data Science?

Data Science is about collecting, analyzing, and interpreting large and complex amounts of data using advanced, statistical, and computational methods to gain meaningful insights and knowledge. A combination of skills and techniques from computer science, statistics, mathematics, and domain knowledge is required to uncover patterns, trends, and correlations in data. These serve as the basis for decision making and to drive tangible innovation.

Sounds complicated at first, doesn't it? To put it more simply: You could compare Data Science with solving a gigantic puzzle. First, you collect and arrange the pieces. Then you try to put them together step by step until a bigger picture emerges. Often, there is so much data that it takes select software-based tools to actually make use of it. Data scientists can help companies identify patterns and trends that lead to better business decisions and more accurate solutions for customers.

Why is Data Science important for industrial companies?

 Industrial companies are often confronted with huge amounts of data. These can be used beyond their primary purpose to identify typical patterns, for example indicators of error-prone processes. Based on these insights, Data Science is therefore used to optimize production processes, reduce downtime and increase quality. It can also help industrial companies improve the efficiency of their supply chain, better predict demand and increase overall customer satisfaction. Through smart data analytics, companies can develop new products and services that better meet the needs of their target audiences, or even identify upcoming business trends earlier than others. Consider Data Science as a powerful engine that accelerates business development and enables industrial companies to make faster and more accurate decisions based on real-time data and multivariate analysis.

Data Science is already being used successfully by thousands of industrial companies worldwide. But, and this one is really important, it's not just about who uses it, but above all how Data Science is used. In the highly competitive markets in which industrial companies operate, efficiency plays an important role. Even small improvements can lead to significant improvements in profitability. According to a 2022 McKinsey study , companies that use data analytics can increase productivity by 10% to 30%, reduce machine downtime by 30% to 50%, and make forecasts up to 85% more accurate. Improving efficiency means reducing waste, optimizing resources and maximizing productivity in every way possible.

Examples of efficiency optimization in industrial companies with the help of Data Science

In the manufacturing industry, unexpected downtime due to equipment failures can be costly and cause delays in production. Through the use of data science techniques such as machine learning, it is already possible today to accurately predict when equipment failure is likely. This enables proactive steps to be taken to prevent spontaneous and unplanned downtime. 

Numerous industrial operations collect data from sensors integrated into machines to measure temperature, pressure, vibration, and more. By wisely analyzing these time series, faulty patterns and anomalies can indicate impending machine failures. With this information, companies schedule preventive maintenance windows to repair or replace machines. This is known as predictive maintenance.

Another example of the use of data science to improve efficiency comes from supply chain management. Industrial companies that rely on a global network of suppliers can use data science to optimize their supply chains and reduce costs. By analyzing data on supplier performance, lead times, and inventory levels, companies identify approaches to shorten lead times, streamline processes, and improve product quality.

In manufacturing, it is crucial that the products in question meet certain quality standards and specifications. Costly rework and warranty claims want to be avoided. Data science can help industrial companies improve their quality control processes by analyzing data from production lines and test equipment to detect defects and quality issues in real time. For example, a company in the electronics sector can use machine learning algorithms to analyze data from sensors that measure various quality parameters during production. Again, by identifying patterns in the time series, errors can be detected early and corrective action taken before scrap or rework occurs.

These examples highlight the importance of efficiency to the success of industrial companies, as it directly impacts productivity, profitability, and customer satisfaction. From predictive maintenance to quality control, Data Science provides industrial companies with powerful tools to identify areas for improvement. As technology evolves, Data Science is becoming increasingly valuable to industrial companies, making it an indispensable tool for those who want to succeed in today's competitive global marketplace.

The advent of robotics in the manufacturing industry

Robotization, or the application of robotic technology in industrial operations, is changing the manufacturing industry at a rapid pace and continuing to drive innovation. From assembly lines in the automotive industry to food packaging plants, robots are being used to perform a variety of tasks, some of them very tedious, that previously could only be done by humans. The integration of data science and robotics is a powerful combination and represents a revolution in manufacturing. Data science helps manufacturers gain insights from their vast amounts of data generated during the manufacturing process. In parallel, robotics delivers the precision, speed and accuracy needed to perform complex tasks with minimal human intervention.

A good example is the Ford company. The automotive company introduced a system called Ford Production System (FPS) in 2013. It collects real-time data from sensors and other sources to provide deeper insights into manufacturing operations. Ford also uses robots to automate tasks such as welding and painting, allowing for greater consistency and precision in these critical operations. Combined with artificial intelligence (AI), you have a powerful technology in your hands that can fundamentally change the entire sector.

Some challenges around Data Science and AI need special attention. These include cybersecurity, which ensures the safety of plants and systems. Standard precautions are usually not enough for effective protection. A differentiated expert view can significantly reduce the attack surface for criminal interference. Machine learning techniques can sometimes deliver unpredictable results. Extensive empirical knowledge is required to avoid undesirable developments. Finally, it is not always easy to integrate new technologies into existing workflows. We therefore recommend consulting experience providers before embarking on a Data Science project. If you have any questions about Data Science and Machine Learning, please feel free to contact us.

Increasing efficiency through the use of data science in the energy sector

The energy sector is one of the most important sectors in the world, as it provides the necessary energy for industry and commerce as well as all areas of life. It is therefore crucial for these companies to operate as efficiently and effectively as possible. There is therefore expertise built up over decades on how to maximize output and reduce waste. By collecting and analyzing data from a variety of sources, such as sensors, IoT devices, and historical data, energy companies gain further valuable insights and encounter new starting points to unlock improvements.

Data science can help optimize the use of renewable energy sources such as wind and solar power by analyzing weather data and production volumes. Accordingly, energy companies can ensure that they make optimal use of these renewable resources and reduce their dependence on non-renewable sources such as fossil fuels.

Not only that, but data science can be used to identify areas where industrial consumers can significantly reduce their energy consumption and emissions. Or those areas where improvements can be made, for example by upgrading equipment to more energy-efficient models or introducing energy management systems. Finally, data science can help improve the transparency and sustainability of energy supply by aggregating data on supply chain partners, transportation methods and emissions. Last but not least, the environment also benefits.

In an industrial company, not using data science can be dangerous

As we now already know, data-driven insights can help companies identify new markets, new products and improved processes. This leads to higher revenues and profits. In contrast, not using Data Science can have a number of negative consequences.

Here are some of the potential dangers:

• Lower efficiency and productivity

• Increased costs

• Reduced competitiveness

• Suboptimal decision making

• Difficulties in meeting regulatory requirements and changing consumer expectations

• Legal and reputational risks

• Missed opportunities for innovation and growth

• Inability to keep pace with rapidly evolving markets and technologies

• Inability to respond to changing customer needs and preferences

• Inability to use data-driven insights for strategic planning and decision making.

With the rapid developments in Data Science, it is becoming increasingly clear that companies that harness the power of data will be more successful in the future. By investing in data science, companies can stay ahead of the curve and better adapt to ever-changing market realities.

How to start building a data science team in your organization

Now that you're aware of the potential benefits of implementing data science in your organization, are you eager to get started? Getting started may seem daunting, but don't worry, we've listed the appropriate steps needed to build your own data science team.

The first step in building a Data Science team is to define the team's goals. Determine the specific business problems or opportunities you want to address with Data Science. Identify the skills and resources needed to solve these challenges.

Once goals are defined, the right talent is needed. Data Science is a multidisciplinary field that requires a range of skills, including statistics, programming, machine learning, and expertise. Look for candidates with appropriate academic backgrounds and several years of experience.

A powerful data processing infrastructure is a prerequisite for the successful work of the data science team. Processes and tools for collecting, storing, cleansing, and analyzing data must be put in place. Choose the appropriate tools and technologies that fit your business goals and data requirements. Our experts will be happy to help you with this.

Data Science is an iterative process that involves constant trial and error and continuous improvement. Encourage the Data Science team to experiment with different data sources, algorithms, and models to find the best solutions to your problems.

Data Science is a rapidly evolving field. Your team needs to keep up with the latest trends and technologies. Provide ongoing training and experience-sharing opportunities to keep your team's skills and knowledge up to date.

You are unsure whether you want to face this task alone? Trustbit has in-depth knowledge and years of experience in the data science field. We offer substantial consulting and hands-on support in setting up and building your in-house data science team. Contact us today and meet your trusted partner when it comes to enterprise transformation.