Discrete manufacturing

Who needs this
  • CEO
  • Head of operations
Questions and pains
  • What is the best production plan with my current work centers, technologies, and resources?
  • How to reduce idle time/downtime/work in progress by rearranging job orders or shifts?
  • What is a constraint now?
  • What is the best way to eliminate this constraint?
  • How it will change my efficiency, timing, etc.
  • What will be the next constraint?
  • What equipment to buy or replace?
  • Should I buy the tool X?

Terrific.Productivity operates a digital twin of an entire manufacturing facility, processes, and equipment and provides you the following

  • Production plan optimization
  • OEE\throughput improvement
  • Smart modernization

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

  • changing replacement of equipment items,
  • working with a new supplier,
  • shift to another type of component,
  • robotization of a process,
  • other
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Case Study

About the customer

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.

  • The plant was equipped with both the most modern tools and machines supplied by leading Japanese and Swiss vendors as well as advanced specialized ERP. But the facility didn’t perform as planned -- the resulting product volume was much lower than the projected capacity, in addition to problems with quality and meeting deadlines. Despite the business systems and equipment functioning properly individually, their integration showed that  the unified business ecosystem wasn’t meeting the projected results.
  • There was a need for a shift scheduling system that would be flexible, adjust to changing conditions, and give transparency to the entire manufacturing process with detailed information about the current stage of each order. 
  • Upper management needed the tool to make forecasts and modeling for non-standard orders in order to make better decisions.
The project

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:

  • project production capacity was not attainable
  • product quality was unsustainable
  • excess WIP
  • overly long production cycle that was preventing signing on large customers with mass orders
  • high cost of manufacturing

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:

  • to shut down the facility for 6 months,
  • to finish production on existing contracts and market new product offers, or
  • to reduce workers’ shift time and shut down some parts of the factory.

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.

  • Production output grew 1.5 times.
  • Excess stock was reduced threefold.
  • Manufacturing cycle time was cut in half.

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.

About the customer

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 manufacturing facilities of concern is focused on producing new types of firearms (pilot production) as well as some specific types of products with the highest standards of fire accuracy, like rifles for biathlon. The plant was struggling with tasks overload and was unable to meet production deadlines. The managers were building a sequence of orders to produce in Excel, which required a lot of time and effort And yet, it still wasn’t clear which orders were more profitable or how to set priorities. The roundout was unpredictable and there was a lack of transparency.
  • The main objective of the project was to implement and deploy software for the plant that would provide full control over the manufacturing system and all its processes, from the development of a technology to transportation to the internal customer.
The project

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.

  • Manufacturing cycle time was reduced threefold.
  • Labor productivity grew by 30%.
  • Manufacturing transparency increased.
  • Planning became more accurate and production is now meeting deadlines.
  • The system provides daily shift schedules and task lists to each team of workers.