Significant benefits can be achieved with the right AI tools, both in production control and planning systems and sales planning and forecasting. This blog post was born out of the need to describe what companies need to consider when planning AI development, and where AI can improve and optimize production planning.
The desired target state in an AI project is the starting point for planning
The first step in using AI is to consider and define what business-relevant goals you want to achieve with AI applications, or what problems you want to solve with AI. Once this goal has been clarified, the next steps are chosen accordingly.
When starting AI development work, the foundation must be in place: enough relevant data must be collected from the business area under consideration, and the related production processes must be modelled. Even if there are still clear gaps in these areas, it does not prevent the starting of the project. Process mapping and data collection can be done just as well at the early stages of an AI project – it allows you to look at core processes from a new perspective, and focus data collection in a way that best supports the deployment of business-critical AI applications.
If there are other business development projects going on in the organization at the same time, such as a lean project to improve production efficiency, synergies can be achieved by implementing them simultaneously. At the same time, the objectives and approaches set for the projects can be examined from different perspectives, which can prove very fruitful.
Quality data is valuable
The key to data collection is to determine what data is collected, and where it is stored for use. Although quantity and historical data matters in data collection, the qualitative aspect is also crucial. Data is not collected for the pleasure of collecting it, but with the aim of gathering data that serves the desired purpose. To get the most from the data collected, care must be taken to ensure that the data is stored in a data warehouse with appropriate models for data harmonization and validation.
You can also get started without a data warehouse, but in this case the data set needs to be completed and validated, and this requires the allocation of time and resources. At the very least, the ongoing maintenance and development of machine learning models requires data to be processed in a data warehouse, so the project is less painful to execute if the data warehouse is implemented from the very beginning of the AI project.
The benefits of AI in manufacturing optimization
AI solutions can be used to achieve the following benefits in production planning and optimization:
- Demand forecasting becomes more accurate
- More efficient inventory optimization
- Reduced losses
- Improved security of supply
- Improved capture of tacit knowledge
- Less time is spent on routine tasks.
The application areas where AI is currently most useful for manufacturing optimization are demand forecasting and production optimization. AI can map and take into account all events and trends that affect demand fluctuations, making demand forecasting more accurate. As sales forecasts become more accurate and cover a longer period of time, production batches can be optimized more accurately, while reducing waste. This also improves the efficiency of inventory optimization.
An example of the use of AI in the food sector
Pinja implemented an AI-based project for a food sector client, where more accurate demand forecasting and automated inventory optimization helped reduce waste by around 30% while maintaining delivery reliability.
Automated production planning based on sales forecasts is particularly critical in the food sector, where loss of date-labeled products is realized faster than in other sectors.
With AI, processing large amounts of data is faster, and requires fewer human resources. At the same time, automated production planning eliminates human error. For example, production planners don’t have to create the production plans themselves but can check the plans already compiled by AI. Production quality assurance is also improved when each batch is planned with the same level of accuracy using AI, regardless of product volume.
AI can also be used to bring together all the tacit knowledge of an organization about production planning. The orientation of new employees is faster when AI ensures that all available information is available at the system level to share production decisions. This is particularly important in sectors where the employee turnover rate is high.
A competent partner to map the road
When a company is looking for a partner for AI implementations, it is important to choose a partner that not only has a deep understanding of AI and machine learning technologies, but also of the client’s business, as well as the challenges and opportunities that AI can bring to the client’s system environment.
The partner uses its experience in delivering business-critical systems for the benefit of its clients, as well as its understanding of production processes and the collection of production data in different industries. A successful AI project starts by jointly identifying the processes and activities of the client where AI applications have the greatest potential. The goal is to integrate AI into production management systems in a way that delivers tangible and measurable business benefits to the client.
Read more:
Artificial intelligence and machine learning
The power of smart manufacturing: the potential of AI in the industrial environment
What is production optimization, and what can it achieve?
When is Excel not enough as a production planning tool?
Production Planning System Buyer's Guide
iPES by Pinja Production Planning System
