Top 8 Data Science Use Cases in Production
Production sphere embraces a wide range of processes related to all branches and stages of creating material goods. In addition, these material goods may be of different values and even have rather contrasting goals.
Under conditions of the third industrial revolution production sphere has to use automation and digitalization processes. Furthermore, now we are moving to the phase of industry 4.0 which is to bring even more efficiency, robotization and advanced technologies to production. This article aims to highlight data science use cases in production which seem to be the most successful and fruitful.
Trying to meet all expectations and satisfying all customer’s needs at a time is rather a bad idea. A business strategy having this aim in mind is destined to fail.
Therefore, it is much better to concentrate on a particular group of products or services and then continue development. This differentiation process is widely known as segmentation. Customer segmentation, in its turn, helps to concentrate on the specific customers’ subgroups that are the closest to the ideal buying persona. In the case of B2B companies, these companies are often segmented according to the following criteria: location, size, number of employees, previous purchase, etc. B2C companies segment their customers according to demographic factors: age, gender, marital status, etc.
Customers’ segmentation importance for the production sphere is enormous. First, of all, if you have a clear idea of what your customer wants this allows improving your product on various production phases.
Application of predictive analytics gives a superpower of foreseeing possible problematic issues and complications. The players of the production industry are especially interested in gaining this power.
The managers and participants of the production process pay considerable attention to quality control and efficient functioning of the production. Predictive analytics largely contributes to this process.
For instance, predictive maintenance analyzes historical data on machines performance and helps to define the likelihood and time of their failure. The companies working within the production sphere can maximize the operating time of the machinery and minimize financial loss.
Demand forecasting is one more important opportunity to get the benefit of. Analysis of the previous data about the product allows finding patterns and trends in the rises and falls of the customers’ demand.
Technological innovations win new stages in the development of the production industry. Robotics, nanotechnologies, and 3D printing, cloud computing are no longer strange in the production sphere. High-speed output, efficiency, and flexibility of the production are among the results of their application.
Warranty claims take a considerable amount of costs for big production companies. Therefore, technological innovations are called upon to bring relief in this aspect as well. Warranty analytics systems come to the rescue. These systems are designed to facilitate communication between the materials suppliers and the employees at various production stages to provide high quality. Customers satisfaction remains in the center of attention.
Active detection of possible defects contributes to the general minimization of costs spent on support of the warranty claims.
Enormous amounts of the products produced, high speed and demand make companies search for new ways to optimize working process and make it as efficient as possible — one of the ways to transfer some process to robots. Simple repetitive tasks or complex operations, dangerous processes or challenging construction works make a massive pile of work that may be easily performed by the robots and AI-powered machines.
Adoption of industrial robots has been considerably increased in recent years. One of the most critical factors here is that robots make production safer for employees.
