Ultimately, the crux of the matter is to optimize plant production in order to mass-produce customized parts without increasing costs and while also boosting productivity, that’s why using Industry 4.0 enabling technologies is vital. We’re talking about both the sensorization of plants with Internet of Things (IoT) technology and advanced software developed using Artificial Intelligence and Machine Learning hosted in Cloud environments.
This is the aim, to turn our plants into Smart Factories, but to do this we have to overcome 5 challenges:
1.- Cutting product life cycle
The increasing demand for more specific and niche application solutions requires greater customization of products, making the life cycle of some shorter than usual. For sheet metal cutting manufacturers, this means that the production of parts has to be streamlined and efficient, which requires flexible machine tools, such as the use of 5-axis machining, and advanced software with CNC programming functions assisted by CAD/CAM virtual simulation software programs. This also allows us to be more precise with our nesting, meaning that we can use the piece more efficiently. Not only in terms of maximizing its use, but also by generating less waste.
2.- Shorter delivery times
The biggest challenge in distribution is to deliver orders in record time. At the moment, to reduce delivery times, we need to increase process automation and start using advanced tools based on data-supported artificial intelligence and machine learning algorithms. This means, for example, that we can anticipate peaks in demand and readjust the workload for machine tools to meet critical deadlines.
Generally speaking, the best way to ensure more competitive delivery times is with intelligent production planning and execution software that can be used to automatically calculate optimized manufacturing times based on the production orders issued and to update work queues. All of this streamlines the planning and execution process and cuts delivery times.
3.- Stock control
Inventories are always a risk as they restrict capital, making efficiency and turnover quantity difficult. Again, advanced algorithms allow us to control stock, keeping it up to date and making it possible to anticipate new orders, to subsequently purchase raw materials sensibly and at the best possible price.
4.- Labor shortage
At the moment, many manufacturers are having to overcome a shortage of skilled labor in the production phase. This primarily affects machine operators and manufacturing engineers. Again, automating repetitive tasks makes up for this issue surrounding labor shortage, but also allows the professional to dedicate themselves to other tasks where machines or automation are unable to help. This makes the operator more efficient and, in turn, increases their level of job satisfaction. And we all know that a motivated worker is a more productive worker.
5.- Data-based manufacturing
We talk a lot about the Internet of Things (IoT) and digitization transformation because a sensorized plant means that we can find out, at any time, what’s happening in the factory and facilitates objective data-based decision-making. These have to translate into responses, into solutions, so we need to take them to Cloud environments where they are processed with AI and ML algorithms to yield different preventive and prospective responses.
One way or another, most of these solutions rely on the use of intelligent production planning and execution software and automation to make daily production processes more streamlined, productive and data-driven. Therefore, to tackle these five challenges, the following processes must be addressed:
- Automation of machines and devices.
- Integration of production data into the system.
- Sophisticated production planning, execution and analysis software
The next step is to find a trusted partner who will accompany you on your journey towards industry 4.0. We’ll never tire of repeating it, at Lantek, we’ve been providing solutions that enable us to grow with our clients for over 30 years and have now taken a further step to join the data analytics universe and offer more competitive ad hoc responses.