Predictive analytics is increasingly being used in production planning. How can algorithms help in creating production schedules? This can be achieved through task automation or by using algorithms to analyze material consumption and production costs. What is predictive analytics?
Predictive analytics is a type of analysis aimed at predicting the probability of future events. It utilizes neural networks, various deep learning models, and artificial intelligence in a broad sense. It also leverages large amounts of data—both historical and current. Predictive analytics is applied across many industries and processes. It works exceptionally well in finance departments for forecasting current and future
costs and profits. It is also used in HR to minimize employee turnover, as well as in marketing and sales to better predict customer behavior. It is also widely used in production processes and contributes to a significant improvement in their efficiency.
What data should be considered when planning production?
Production planning is the foundation of every manufacturing company. Without it, creating sales plans or business strategies is impossible. Every company must know how many units of a given product it can produce and how much time is needed to complete tasks. Therefore, it must use large amounts of data to minimize the risk of errors. Production planning should always be treated individually. The first step should be to start with the company’s general market position and gather information on profits, costs, customer product ratings, etc. These provide insight into the organization’s operational efficiency and determine the impact of products on the market, which allows for better planning of future actions. Producing any product requires raw materials—e.g., a specific type of material, energy, or water. Intangible products, such as computer applications, can be treated in the same way. Their creation requires, among other things, disk space or qualified specialists. To ensure they can work at a high level, their needs must be met. For this reason, one of the most important types of data needed for production planning is information about available resources—time, space, money, or raw materials needed to produce a specific number of products. The data should also include the time required to complete a specific task. Data on the capabilities of the machines and tools used will also be useful—their efficiency is usually limited, which is good to include in production plans. An important element of every production plan is the underestimation of materials, processes, and time required to complete a task. Collecting and analyzing large amounts of data allows for the maximum refinement of the production process, but it is safe to add a certain margin of error to the adopted plans—this makes it much easier to react to potential difficulties. Ongoing verification of established plans and continuous checking of the validity of conclusions is also important. This is particularly significant in the case of
discrete manufacturing, as the production of a small number of highly complex products can consume more time and materials than process manufacturing.
What impact can the application of predictive analytics have on production?
The primary application of predictive analytics in production processes is predicting possible events. Collecting data, for example, on the performance of a specific process, allows for modeling the probability of a potential drop in efficiency over time. This allows for the implementation of solutions that minimize the risk of errors, which can improve the company’s business potential. Predictive analytics also supports the automation of business and production processes. This often takes place within
ERP systems. Thanks to them, it is possible to integrate data collection, analysis, and task execution within a single platform. This can lead to an increase in knowledge resources and higher company profits. Another key benefit of using predictive analytics is the ability to identify patterns. Algorithms are designed to detect relationships between data that, for example, employees might not have been able to notice. Another advantage is the fact that neural networks can do this in a much shorter time, which contributes to an overall increase in efficiency.
Why is it worth implementing predictive analytics-based solutions in the manufacturing industry?
Predictive analytics and similar technologies not only allow for predicting future events but also work great for improving current processes. These types of analyses can be used, for example, to check the demand for a specific type of product or the energy needed to produce a certain number of products. Tools using predictive analytics also help in inventory management, planning replenishment, and overall optimization of their use. For example, an
ERP system can continuously check the consumption of a specific type of raw material and, if necessary, inform employees about the growing demand for it. It can even—if programmed to do so—order the necessary amount of materials itself. This reduces the overall costs of running the business, and the saved money can be invested by the company in development—purchasing new machines, training for employees, or developing new products.
IT systems supporting the production planning process
The most common tools supporting production planning are ERP systems, which collect data on company operations and allow for the automation of some tasks. They are often combined with neural networks and various machine learning models. Thanks to such solutions, the platform can not only perform programmed processes but also improve them over time. This significantly improves the efficiency of the entire enterprise. Another frequently used solution in production management systems is cloud technology, which is often combined with big data. The more a company produces, the more data can be obtained. However, processing it requires extensive infrastructure, which can be expensive. Expenses can be minimized by using the services of an external cloud provider. It ensures data security and contributes to cost reduction. Regardless of the technology chosen for production management, the application of predictive analytics in planning and ongoing supervision of production processes leads to an overall improvement in enterprise efficiency. Algorithms can quickly identify patterns and relationships between specific data groups, as well as suggest methods to improve their performance. Furthermore, thanks to the knowledge of possible events, the company can react to them faster, which guarantees a market advantage.
