Production Management System. How to use data and AI to optimize production
Manufacturing companies collect vast amounts of data, but they do not always know how to use it effectively. Data can describe how a process unfolded: what happened, when it happened, in what sequence and how long it took. It can also show the status of the objects involved, such as machines, products, orders or customers. The more accurately data reflects reality, the easier it is to assess business performance and understand what is actually happening in the production process.
The problem arises when we record only selected points in the process. We know what happened at the beginning and at the end, but we do not have a clear view of what happened in between. As a result, some of the information that could help optimize production remains unused. How can you monitor production processes, use data from machines and business systems, and turn it into tangible business value? Find out how to approach this in practice.
Production Management System and the problem of selected process points
Let us consider a simple example. In a production process, we record when a customer order is received and when an invoice is issued for the finished product. We therefore know when the process started and when it ended. However, we know very little about what happened between these two points.
Based on such data, it is difficult to determine whether individual tasks were performed in the right sequence, where unnecessary downtime occurred, how long individual stages took, or whether semi-finished products and employees’ time were wasted during production. We are also unable to assess whether the way the process is carried out is actually optimal or whether we have simply been working the same way for years because that is how the process was originally established.
A well-designed production management system should therefore do more than record individual events. It should provide data that shows the entire process, makes it possible to identify areas for improvement and helps translate production data into specific business decisions.
What can a manufacturing company learn from e-commerce?
E-commerce is a good example of how much value can be created by collecting detailed data. Online retailers have long analyzed customer behavior: where a user came from, how much time they spent on a product page, how they used the search function, which elements they clicked, which products they viewed and when they added them to their cart.
This makes it possible to reconstruct the customer journey, identify recurring patterns and see that a particular way of navigating a store often ends with the customer leaving rather than making a purchase. This knowledge provides a concrete starting point for improvements and makes it possible to verify whether the changes introduced actually deliver results.
For many years, manufacturing looked different. It was primarily associated with machinery, physical labor and processes taking place on the shop floor. Today, this picture increasingly fails to reflect reality. Modern manufacturing is also an environment in which enormous amounts of data are generated. The question, therefore, is how this data can be used to better understand processes and make more informed decisions.
Manufacturing software. The production plant as a highly digitalized environment
Modern production plants are highly digitalized environments. Machines are equipped with processors, controllers and advanced software, while ERP, MES and WMS systems support and monitor production processes. Individual devices are connected to networks and continuously exchange data. This is complemented by IIoT (Industrial Internet of Things). It is no longer just a concept featured in presentations about the future of industry, but an everyday reality in many production plants.
Sensors measure temperature, pressure, vibration, energy consumption and the operating parameters of individual components. Data can be collected continuously throughout the operation of a machine. Production halls themselves are also being monitored in increasingly sophisticated ways. Sensors can provide information about environmental conditions, material flows and the use of individual areas.
Cameras are another interesting and still underestimated source of data. They are most commonly associated with security, but computer vision technologies can use them to provide information about what is actually happening in a process. They can support quality control, identify objects, track successive production stages and detect deviations from the intended process.
As a result, a modern production plant can generate enormous amounts of data. Simply collecting this data, however, does not automatically create business value. The key question is whether the data makes it possible to reconstruct the process, understand what happened and identify the causes of a particular outcome.
Only on this foundation can production data analytics, machine learning and AI in manufacturing be used effectively. These technologies do not replace data. Their value depends on whether the data is complete, collected appropriately and placed in the context of the process.
From data to decisions. Production dashboard and production monitoring
Collecting data is not the goal in itself. Data is collected to support better decisions. In manufacturing, there are many such decisions: what to produce, in what sequence, which production line should handle a particular order, how many employees should be assigned to a shift, when to order raw materials or when to carry out a changeover.
It is useful to distinguish several levels of data utilization.
- The first level is understanding what actually happened. Let us assume there is a hypothesis that a shift led by a particular supervisor is less efficient than other shifts. A good production dashboard makes it possible to compare data from recent months: the number of units produced, downtime duration and frequency, defect rates and material losses. This makes it possible to verify whether the initial assumption is supported by the data. This is also important when designing reports themselves. There is little value in creating additional reports simply because the data is available. Every report should answer a specific question and support a decision-making process in which the analysis will actually be used.
- The second level is predicting what is likely to happen in the future. Machine learning models can analyze relationships between multiple factors and use them to estimate when a particular order will be completed, the risk of delay or the probability of a specific problem occurring.
- The third level is using data to recommend specific actions. A system can suggest an order sequence that reduces the number of changeovers, lowers the risk of downtime or increases the likelihood of completing orders on time. This is an example of data-driven production planning automation.
We deliberately refer to algorithms here rather than only to artificial intelligence. Not every problem requires AI. In practice, a well-designed production management system can combine business rules, analytics, optimization algorithms, machine learning models and AI.
Production optimization, prediction and production planning automation as one mechanism
One of the areas with the greatest potential is production optimization and planning. Importantly, real savings rarely result from one spectacular change. They are usually created by many small improvements that may not even be visible without appropriate analytics.
Production planning depends on a large number of factors: customer orders, the availability of components and raw materials, the production capacity of individual lines, required changeovers, technological parameters, machine performance, as well as employee availability and skills.
A human planner can be highly effective, but has limited capacity to analyze hundreds of combinations simultaneously. In practice, planners establish several baseline parameters, apply proven rules and adjust the rest of the plan accordingly. Is such a plan bad? No. Very often, it is good enough.
What is fascinating, however, is that an algorithm can analyze significantly more scenarios and identify a solution that eliminates numerous seemingly insignificant inefficiencies that a person simply does not have the time to detect.
AI in Manufacturing. Three areas where prediction supports planning
Three specific areas where prediction can provide tangible support for planning include:
Predictive maintenance: Based on vibration, temperature, energy consumption, pressure and maintenance history, it is possible to estimate the probability of failure and schedule maintenance in advance instead of reacting only after a machine stops production.
Demand forecasting: Forecasting future orders makes it possible to secure materials and semi-finished products in advance and plan the required number of shifts and staffing levels.
Energy consumption forecasting: By combining the production plan with the energy characteristics of individual processes, companies can estimate future demand and manage energy consumption, tariff utilization and the balance between renewable and conventional energy sources. In Europe, where energy costs represent a significant part of manufacturing costs, this adds another dimension to optimization alongside time, people and machines.
Prediction indicates what is likely to happen. Planning determines what we want to do. Production optimization helps identify the best way to execute the plan using available resources while minimizing unnecessary costs.
Production monitoring and process optimization. Why does scale matter?
Scale matters enormously in manufacturing. Losing 30 seconds in a single production cycle or adding one euro to the unit cost may seem insignificant. Multiplied across thousands or millions of production cycles, however, these differences can translate into substantial amounts of money.
This is not only about the figure recorded in the CFO’s spreadsheet. Every inefficiency affects product cost, order fulfillment times and a company’s ability to compete for future contracts. If a competitor can manufacture a product of the same quality faster, more cost-effectively and with greater predictability, over time this can affect its position not only in individual bids but across the entire supply chain.
This is why manufacturing companies place such importance on continuously looking for even small improvements. A good example is the Japanese concept of Kaizen, based on continuous improvement and the systematic elimination of waste.
Similar approaches to efficiency can also be observed in other highly industrialized Asian economies, where investments in automation, standardization and process analytics are combined with large-scale production.
This approach does not assume that the current way of working is optimal simply because it works. Every process can be measured, analyzed and gradually improved. Sometimes this means reducing changeover time by a few minutes, lowering the defect rate, improving machine utilization or changing the sequence in which orders are processed.
A single improvement may seem insignificant, but when repeated every day and across a large production volume, its cumulative effect can have a direct impact on the performance of the entire plant.
This is precisely why production data is so important. It makes it possible not only to identify major problems, but also to uncover small losses that occur hundreds or thousands of times. And it is precisely the cumulative effect of these seemingly minor differences that can determine the efficiency of an entire process.
AI in production planning. How to select the first use case
There are two ways to select the first AI use case for production planning.
The first is relatively straightforward: start with a problem you already know exists. Excessive downtime, difficulties with production planning, frequent delays, quality issues or high energy consumption. The starting point is clear: we know the area, we can estimate its cost and we can assess whether data and algorithms can actually reduce the scale of the problem. This approach is particularly useful at the beginning because it is easy to define the project objective and measure the outcome later.
The second approach is equally interesting and, in some cases, potentially more valuable. Analytics, process mining, machine learning models and other AI tools can first be used to gain a better understanding of the process itself.
It is not always clear where exactly time and money are being lost. Some inefficiencies can become embedded in everyday work over many years and eventually stop being noticed.
Production data analysis can reveal that certain orders regularly wait between operations, a specific sequence of tasks causes additional changeovers, one production line creates a bottleneck, or some resources remain underutilized for a significant part of the time.
Production Management System. From PoC to a solution used on the production floor
Projects involving data, machine learning and AI in manufacturing are to a large extent exploratory. At the beginning, it is often unclear whether the available data is sufficient, whether a model with adequate performance can be built or whether the solution will generate tangible business value. For this reason, such projects are worth managing similarly to research and development initiatives: formulate hypotheses, define success criteria, establish milestones and assess at each stage whether further investment is justified. The first practical stage should usually be a PoC (Proof of Concept). Its purpose is not to create a finished production management system, but to verify whether a particular concept actually works. A PoC can be used to assess data quality, algorithm performance, the validity of the underlying assumptions and whether the results are sufficiently useful from a business user’s perspective. If the PoC does not produce the expected results, this is not a failure. That is precisely why a PoC is created: to validate a concept at a relatively low cost before investing in a full-scale implementation. Only after successful validation should the project move on to an MVP, pilot and gradual integration of the solution into the actual production process. At each stage, there should be clearly defined milestones and metrics that make it possible to decide whether to continue developing the solution, change its scope or stop the project.
This approach reduces technological and business risk while making it possible to move from an experiment based on production data to a solution that genuinely supports day-to-day work on the production floor.
FAQ
How can production data be used to optimize manufacturing?
Production data can be used to identify downtime, bottlenecks, material waste, quality issues, and inefficiencies in order fulfilment. Data analysis can also be used to compare different process scenarios, predict future events, and support production planning decisions.
What types of data can be collected in a manufacturing plant?
A manufacturing plant can collect data on machine performance, temperature, pressure, vibration, energy consumption, downtime, changeovers, production efficiency, product quality, material consumption, order fulfilment, and the performance of individual production lines. Data can also come from ERP, MES, and WMS systems, IIoT sensors, and cameras using computer vision.
Why does data quality matter when using AI in manufacturing?
Data quality directly affects the ability to use AI and machine learning models in manufacturing. If data is incomplete, inconsistent, or does not capture key stages of the production process, a model may incorrectly identify relationships and generate results with limited practical value. Before implementing AI, it is therefore important to assess the completeness, quality, and context of the available data.
What is predictive maintenance in manufacturing?
Predictive maintenance uses data from machines and equipment to predict the likelihood of a failure occurring. The analysis can include factors such as vibration, temperature, pressure, energy consumption, and service history. This allows maintenance activities to be planned before an unplanned production stoppage occurs.
How can AI support production planning?
AI and machine learning models can analyse multiple factors that affect production planning, including orders, material availability, machine capacity, changeovers, technological parameters, and workforce availability. Based on this data, a system can predict the risk of delays or recommend planning scenarios that make better use of available resources.
What can be monitored in the manufacturing process?
Production monitoring can include machine performance, operation times, downtime, changeovers, line efficiency, product quality, material consumption, energy consumption, and progress against the production plan. The scope of monitoring should be determined by specific business needs and the processes a company wants to analyse.
What are the benefits of analysing production data?
Production data analysis can help identify downtime, bottlenecks, material waste, quality issues, and underutilized resources. It can also support forecasting, production planning, and process optimization. The greatest value is achieved when the results of the analysis lead to a specific business decision or a change in the way operations are performed.
