The Nytt way to a more efficient production is
First, you should agree with some basic facts listed below.
That means we set goals and make timetables for when to
achieve these goals. We will create a budget and timeline to
check on expectations, and if not met, we will make required
Our experience is that we start with a limited number of machines/equipment that we monitor. We agree on how the data should be collected and make the suitable installation.
Then we start collecting data from the units, and after about 3-4 weeks, we have enough data to see clear trends/patterns of how efficiently they are used and come up with the first set of recommendations on how to improve.
In parallel, we also do basic training for the operators
involved and give them our OpApp. The operator App provides
additional information that we cannot get directly from the
machines. The OpApp also sends information to the operator to
inform them of the live situation to help act accordingly (shop
When the first data gathering is done, with the help of our Dashboard, show the data obtained and show what conclusions can be derived. We usually find 3-20% improvement potential with minor changes in this first stage.
After this first period of low risk for our customers, we sit down and set a strategy on how to proceed. That includes more data gathered from the machines connected and maybe increasing the data gathering points within them to get an even better picture. But importantly, we decide how to address other machines/equipment in the factory. We always have a step by step approach in mind, to not overload in the first step.
Experience shows that in the beginning to get started, our help is required, so we provide expert advice through experienced production engineers. The goal is always that the customer should gain enough experience to use the Nytt system to make the necessary improvements by themselves.
Nytt is driven by production people. We have backgrounds in
manufacturing, supply of production equipment such as
machine tools, tooling, and theoretical knowledge within
Our drive is to increase productivity and effectiveness in manufacturing.
Our firm conviction is that the way forward is to collect as much data from as many sources as possible, analyze it in a simple and streamlined way, and top it with ML functions to see what is happening now and look into the future.
We have created a data-driven platform for manufacturing companies to have flexibility in decision-making. Using our platform that collects real-time data from the machines, the operators, and other critical sources on the shop floor, we provide relevant information to make data-backed decisions. We also use the historical data to find repeating patterns and provide information regarding any anomalies regarding how a piece of equipment is working, helping manufacturers anticipate and adapt to failures and downtime before they occur.
Using different data-collection strategies through readily available equipment like smartphones, sensors and standard machine protocols, we can get a clear idea about how a factory is running. This information helps companies plan, check, and adapt to the rapid changes and make informed decisions to become more profitable.
ME in Production Engineering from KTH Royal Institute of Technology and MBA from Insead France. Previously CEO at Wikman & Malmkjell AB and now CEO at Precima Production AB.
M.Sc. in Production Engineering from KTH Royal Institute of Technology. Working with Scrum and design thinking.
M.Sc. in Production Engineering from KTH Royal Institute of Technology. Working with R&D and digital solutions in manufacturing.
Experience in machine learning and data science. Working with machine learning strategies in manufacturing.
Former senior research fellow at KTH Royal Institute of Technology, Department of Production Engineering, Stockholm.
Team of 15 programmers in India, working with UI, analytics, web, and Android.