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.
Application of warranty analytics software becomes a tremendous competitive advantage.
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. You can rely on robots in work with high temperatures, heavy materials, or toxic elements.
And last but not least is the fact that robots may be programmed to work and complete tasks 24/7. As a result, a significant efficiency and increase and costs reduction may be achieved by the production.
Product development process is essential for the production companies. This process covers all stage up to bringing the product to the market.
New product development is a process of traditional product development modified by modern technologies and innovations. Without data analytics and data science, the product development would become a useless effort with no practical value or result. The application of data analytics techniques to the metrics enables the producers to facilitate the decision making process.
In addition, improvement of the product itself is also within the scope of data analytics. for example, analysis of the customers’ feedback can bring some practical ideas related to product improvement.
Inventory is a key to a successful stock and business management. To guarantee high-level stock management, the system should operate in real-time mode and reflect the updates immediately. An automated solution may bring desirable efficiency and organization to inventory and stock management processes. These solutions provide a wide variety of tools designed to facilitate the inventory.
Proper inventory management can fix all miscalculations and time consumption matters. Open source inventory management systems support the following features:
New inventory and stock management solutions provide control over all the stock levels and spare you from routine tasks. For companies working in the production sphere, such systems are vital for proper functioning of the factory
Stock and inventory management involves the procedures of ordering, purchasing, and controlling of the materials. This process concerns every single element the company uses in the production process.
Optimization of the workforce use belongs to the secrets of successful enterprise efficiency. Workforce management is vital to increase productivity and organize the scheduling of the company. However, this task may be quite challenging.
Workforce optimization covers all the stages of workforce management life-cycle and discloses the workforce efficiency level. This kind of optimization involves:
Advanced solutions are actively applied by the companies representing various spheres of business. Regarding the production, there are three essential matters to apply advanced technological solutions to:
1. Time and attendance management
Superior time and attendance management solutions facilitate tracking and control and mitigate risks of errors and miscalculations.
2. Performance monitoring
Performance analytics helps to find the sore points, to make improvements, and to minimize costs. These tools tack the efficiency metrics and provide insights into workforce effectiveness.
3. Manager-to-employee communications
Technological solutions can considerably facilitate managerial process and understanding as a whole. Creation and scheduling of tasks, live discussions, and time-management features of such solutions are critical when it comes to production.
Workforce optimization allows estimating the whole production process, take an in-depth look to uncover the matters for further improvements.
3D printing, also known as Additive Manufacturing is now widely applied in different business spheres. Application of this technology enables using such materials as plastic, nylon, polyamide and many others belonging to the class of light, flexible and strong materials.
Regarding the production industry, 3D printing can replace some complex processes and facilitate the production of a product reducing the number of involved third parties. Application of the 3D printing gives the benefit of short-run part production. Thus companies can launch new products more frequently. Besides, there are good chances to increase the volumes of the product. The production line that is set up for 3D printing is easier to speed up and tune to the levels and amounts you need.
Nowadays, data science is an integral part of any decision-making process. Big companies primarily rely on data science in their operations and innovations. Properly integrated data science solutions solve numerous problematic issues and bring benefits to businesses.
Big players of production industry apply data science developments to optimize and speed up processes, increase quality and quantity of the produced items. Introduction of innovations is quite a challenging process. However, its benefits can hardly be underestimated.