Terrific.Productivity operates a digital twin of an entire manufacturing facility, processes, and equipment and provides you the following
Optimize production plan
Terrific.Productivity automatically calculates the optimal production schedule. All you need is to provide the system information about your working centers, technology, organization structure, inventories, and sales plans. The system can easily receive this information through integrations with your existing systems (ERP, PLM, CRM, MES, etc).
Increase OEE — overall equipment efficiency
Terrific.Productivity discovers constraints in your production process and gives you suggestions on improvements to multiply the speed of the entire manufacturing throughput.
Plan modernization smart
Predict OEE, throughput, profit, and other outcomes for multiple scenarios
The textile-producing holding “BTK Textile” runs 12 factories all over Russia with its headquarters located in Moscow. One of the plants was built just 3 years ago and is equipped with the most modern machines, making it one of the most advanced textile manufacturing facilities in the world.
Our team of data scientists discovered that the key problem was hidden within the business processes. The sequence of operations not being organized correctly resulted in the following implications:
The team conducted many interviews with representatives of each department and deeply analyzed all of the processes in the field. Then, they built an AI model that simulates the entire manufacturing process, integrated this model with the ERP, and created a directory of all the times, equipment items, and operations.
One of the first obstacles in the project was the lack of realistic data. The BFG team found that workers were not using the ERP correctly and were not inputting real data. Each department was using different tools and software to calculate and control their processes.
The planning department, for instance, was using an Excel spreadsheet. The technologists were brought in to verify that data and prepare it in order to train the models.
The second obstacle was that workers were not following manufacturing and technology manuals.
The most significant success factor was that upper management was supportive and kept an eye on the entire project.
The team built a digital model of the entire manufacturing process. Many constraints were detected, one of them even being a collision of two manufacturing processes.
In addition, the BFG team simulated three possible options for how to cut costs:
The third was chosen as the optimal option because it meant maintaining the valuable human resources along with the ability to scale and continue generating revenue.
The resulting system provides shift scheduling and daily task lists.
Senior managers now have remote access to the dashboard, allowing them to simulate different scenarios and calculate the output for non-standard orders based on real-time data on the facility’s current load.
As said by the customer’s management representatives, this digital twin of the factory, built on BFG CMT software, helped to significantly increase the efficiency of the manufacturing processes.
Kalashnikov Concern produces about 95% of all small arms in Russia and supplies to more than 27 countries around the world, making it the largest firearm manufacturer in Russia. Notable products include the Kalashnikov (AK) assault rifle series, the RPK light machine gun series, the Dragunov SVD semi-automatic sniper rifle, the SKS semi-automatic carbine, the Makarov PM pistol, the Saiga-12 shotgun, and the Vityaz-SN and PP-19 Bizon submachine guns.
One of the main problems was uncertainty as to whether a particular order could be done in time.
This was because components management was poor. The plant was receiving orders, but there was no guarantee that other orders were requiring the same type of parts and components at the same time. There were some cooperation constraints, as well.
Our team collected all the necessary information from the PLM system and Excel (about 700 sheets) and then the model was built. In collaboration with the workers and managers, our engineers began testing its efficiency creating Excel tables with shift schedules and tasks for each product route. Also, they decided to divide orders into smaller bunches. It created flexibility and led to growth of productivity.