Each effective business, including manufacturing, tries to minimize waste and simultaneously maximize profitability. Currently, energy price volatility influences total production costs, and therefore, global industrial production. To cope with this, the management of factories searches for opportunities to decrease total costs.
Manufacturers have already begun the transition towards smart factories, and they will likely continue investments going down this path. Industry 4.0 concept, that is popular in European countries, especially Germany, changed existing factories step by step with smart manufacturing tools and solutions to reach production goals more efficiently and effectively.
For example, using a digital twin, manufacturers can simulate an actual production line to ensure that all processes run smoothly before operating it in the physical factory.
In this blog post, you will find out how to minimize different types of waste in manufacturing to reduce costs and increase productivity. You will also look at several successful examples of applying simulation to optimize the production processes.
Energy prices impact on the manufacturing industry
The global manufacturing sector is experiencing turbulence in 2023 due to the rising geopolitical risks, worsened economic outlook, and volatility in the energy and commodities markets. These factors put pressure on manufacturers’ performances.
Growth across industries is predicted to be uneven. Industries with high energy intensity, or highly reliant on investment demand, will be more affected. Globally, production value growth will be slower in 2023 for chemical products and machinery, as well as the rubber and plastic industries, due to weaker B2B demand and rising energy costs.
Deloitte anticipates 2.5% growth in US GDP in 2023, according to Oxford Economics’ Global Economic Model. Despite anticipated challenges connected with energy price volatility, higher labor costs, and inflation worries, new solutions are altering proven business practices in the pursuit of growth and productivity. Technologies, by improving energy efficiency, could make manufacturing operations more sustainable.
According to Deloitte 2023 manufacturing industry outlook, this year manufacturing companies should increase the utilization of digital technology to gain supply chain visibility, productivity, and better connectivity with suppliers, partners, and consumers. This all can be done by using simulation modeling.
Examples of cost reduction in manufacturing
Simulation software is a powerful tool that is used to analyze manufacturing systems, estimate the impact of system changes, and improve manufacturing processes. Strategies like JIT (Just-in-Time) or Lean manufacturing can be modeled and simulated for detailed analysis and effective operations. AnyLogic simulation software enables businesses to experiment without disrupting production and reduce the costs of testing in the real world.
Here are successful manufacturing projects that were implemented with the use of AnyLogic simulation models.
A pet food manufacturer wanted to minimize production waste and maximize plant occupancy. For this purpose, engineers applied manufacturing simulation as a perfect approach for better production scheduling and bottleneck visualization.
Using AnyLogic, the engineers created a model of the shop floor with the production line and linked all production phases. As a result, with the help of the simulation model, the manufacturer increased production rate, eliminated bottlenecks, and reduced waste by 90%.
One of the world’s leading steel producers aimed to optimize and better schedule limestone reclamation to maximize the utilization of the feeding circuit and to reduce running hours. Engineers developed a model for testing different what-if scenarios and understanding the utilization of the limestone feeding circuit.
The simulation with AnyLogic helped maximize the utilization of the feeding circuit. Thanks to modeling, they carried out five experiments and developed favorable plant operation schedules that could reduce machine running and cut costs related to electricity .
One of the same company’s steel manufacturing units showed potential for increasing the overall unit throughput by optimizing the internal logistics systems. The engineers wanted to find a simple rule of thumb to operate cranes and optimize the overall production process by decreasing human dependency in decision making.
The team created what-if scenarios with the aim to reduce waiting time for the vessels and modify the crane operation logic. Statistical analysis results of the experiment showed the advantageous scenario from the standpoint of crane utilization, waiting time of vessels, and throughput in heats. This could save the company several millions of dollars per year and be implemented without disrupting production.
A global leader in capital goods needed to monitor manufacturing processes and make informed maintenance decisions. To do that, the digital twin for an automotive production line was created as a virtual copy of the physical world.
Management could get detailed and demonstrative information about the economic and production consequences for different maintenance policy configurations. Using the digital twin for simulation provided an easy-to-use tool to analyze and compare scenarios to observe how changes could impact maintenance cost.
One of the world's largest makers of semiconductor chips required data to understand how many spare parts the company needed to keep the factories running without overbuying them. The developed simulation model helped the managers determine the number of spare parts to be purchased to avoid equipment downtime, as well as significantly overbuying the spares.
This AnyLogic model was used to support negotiations with the equipment vendors for providing factories with additional spare parts’ consignments at no cost and achieve significant savings with little effort.
A global supplier of technology and equipment for the photovoltaics, semiconductor, and microelectronics industries used AnyLogic simulation software to evaluate the manufacturing optimization possibilities.
The manufacturing optimization model helped engineers investigate the benefits and drawbacks of each manufacturing optimization solution and to find out how they could be further improved. This gave them the opportunity to significantly improve the production line design and choose the best solution in terms of manufacturing optimization: throughput, reliability, and scrap rate at a low cost.
A world energy leader providing equipment, solutions, and services across the energy value chain sought out a tool to analyze the manufacturing system as a whole.
Simulation enabled the visualization of their system over time. A full plant simulation model, built in AnyLogic, was used for capacity planning (identify, evaluate, and prioritize projects), quantitatively analyzing bottlenecks, and evaluating improvement options. AnyLogic software helped observe day-to-day operations, increase production throughput, and decrease manufacturing costs.
Conclusion: using simulation for production growth
Manufacturing executives agree that technologies that improve energy efficiency could make production more sustainable. The examples above show how simulation models help monitor and analyze manufacturing processes and systems as a whole, and evaluate the manufacturing optimization possibilities.
As you can see, simulation modeling is key to minimize production waste, maximize the resource utilization, and optimize the overall production process. Simulation software enables management to achieve the KPIs and decrease manufacturing costs.
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