case study

Saving an estimated €62,000 yearly and 143 tons of CO2 via easy-to-implement adjustments without changing the facilities

The AIP uses Plant Simulation to streamline crane use and storage allocation, reducing energy consumption by 7.4 percent

The AIP uses Plant Simulation to streamline crane use and storage allocation, reducing energy consumption by 7.4 percent

AIP

The Chair of Production and Logistics at the Institute of Automotive Management and Industrial Production focuses on connecting research with industrial practice. Thus, interdisciplinary teams concentrate on production and operations management, logistics and supply chain management and environmental protection and sustainability with real-world practice and often on-site with industry partners.

https://www.tu-braunschweig.de//en//aip

Headquarters:
Braunschweig, Germany
Products:
Plant Simulation, Tecnomatix
Industry Sector:
Heavy equipment

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With Plant Simulation, we were finally able to evaluate the impact of various warehouse configurations and associated crane operations.
Patrick Oetjegerdes, Research Associate, AIP

Increasing warehouse energy efficiency

The Institute of Automotive Management and Industrial Production (AIP) at the Technische Universität Braunschweig studies internal logistics, and they can tell you moving goods within a company is energy intensive. Led by Patrick Oetjegerdes, a Master of Science (M. Sc.) and research associate under the guidance of Professor Dr. Thomas S. Spengler, his team revealed a surprisingly high energy savings potential using path and space-optimized storage variants. To model it, AIP used Siemens Digital Industry Software’s Plant Simulation in the Tecnomatix® portfolio, which is part of the Siemens Xcelerator business platform of software, hardware and services.

Examining steel coil handling and crane use

Steel coils are the basis for many industrial products, including automotive, mechanical engineering and other sectors. The study examined a steel coil manufacturer’s processes, specifically the transport movements between hot rolling and shipment preparation in their warehouses. As semi-finished products, they are delivered, stored, retrieved and further processed at the customers’ premises, which requires significant energy. Additionally, most warehouse and production environments require overhead cranes of various sizes and types. Using electric motors, they often consume thousands of kilowatt-hours (kWh), making this a worthwhile area for reducing energy consumption. Therefore, these processes expend a significant amount of carbon dioxide (CO2).

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Understanding the warehouse’s structure and energy consumption

For a manufacturer in the steel industry, the one the AIP studied stores up to 2,000 steel coils in each warehouse section, which measures up to 400 by 60 meters. Additionally, these storage areas are operated by up to three overhead cranes, which consist of three elements: the portal or bridge, the trolley and a hook. There are three axes of movement, each characterized by speed and energy consumption for each element. For instance, moving the entire portal is more energy intensive than moving the trolley; however, that can change depending on the weight it is lifting.

The manufacturer produces steel coils to order according to customer specifications, which come in various alloys, sizes and surface properties and are stacked in a triangular pattern. If they need to retrieve a specific coil at the bottom, they must move several of the up to 32-ton coils, which is energy intensive.

Optimizing warehouse configuration

The AIP team used Plant Simulation to understand how warehouse structure, crane use and storage allocation impact energy consumption and how to consider it when making decisions.

“First, we conducted an empirical survey of energy consumption and defined an energy-consumption function for each movement, depending on the weight of the steel coil,” says Oetjegerdes. “There is a function for the portal, the trolley and for upward and downward movements. We then integrated these functions into existing simulation models in Plant Simulation, which include various warehouse sections and cranes. Within Plant Simulation, we used the cranes and more library and the move-to function, which allows us to integrate a custom function that calculates and logs the energy consumption for every movement. Additionally, we used production system models we developed for our partner. Implementing these functions was straightforward.”

Visualizing energy consumption

Simulation was the ability to visualize the current energy consumption of all cranes for the first time. Previously, the manufacturer only knew specific production environments consumed a certain amount of energy, and a specialized crane scheduling was not feasible.

With the Plant Simulation model, the AIP could investigate further scenarios, namely taking a closer look at an energy-oriented warehouse configuration of a storage area with a use rate of approximately 600 to 1,400 coils. Additionally, the AIP altered the storage place assignment to an energy-oriented approach, which considers the amount of energy consumed when assigning a location to a steel coil.

Examining warehouse configurations

In the energy-oriented warehouse configuration, the AIP compared two types of coil stacking. One was a 2.5-level stacking (storing 2,002 coils), where an empty place was left between each coil on the third level stacking, and a 2-level stacking with only two layers of stacked coils (storing 1,642 coils). One assumption was that with more available storage space, the less distance the crane must travel. Another assumption was that fewer stacking levels lead to fewer reshuffles. The goal was to learn which storage configuration consumed less energy while maintaining operational service levels.

“With the 2.5-level variant, in the worstcase scenario the manufacturer needs to move four coils,” says Oetjegerdes. “Alternatively, in the 2-level variant, they only have to move two to reach the bottom coil in the middle. However, using Plant Simulation analysis, the crane covered less distance in the 2.5-level variant, and the 2-level variant had significantly fewer reshuffles. The result was a 7.4 percent lower energy consumption in the 2-level variant, indicating that in terms of energy consumption, the number of reshuffles is more important than the distance the crane covers. This means an optimized warehouse configuration saves nearly €25,000 annually and reduces yearly CO₂ emissions by 59 tons. With Plant Simulation, we were finally able to evaluate the impact of various warehouse configurations.”

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Leveraging energy-oriented storage allocation

The AIP also explored using Plant Simulation for energy-oriented storage place assignment by examining coil placement options. In the warehouse, there are incoming, outgoing and blocking coils that need to be moved out of the way to reach certain outgoing ones. Additionally, during the crane scheduling, workers need to determine a sequence of crane
movement and a place assignment. These decisions are interdependent and significantly influence the operational efficiency of the crane.

To understand which place should be assigned to incoming and blocking coils, the AIP used Plant Simulation to evaluate each location based on various parameters (proximity, level, dimensions, etc.), assign a score for each parameter and choose the location with the highest score. In a second variant, the AIP altered the evaluation to add a score for energy consumption. This way, they could compare the status quo with the new, energy-oriented place assignment.

“We asked ourselves, how much energy can we save with this new concept, and how does it affect crane operations?” says Oetjegerdes. “It turned out the energy-oriented place assignment saved nearly €37,000 and 84 tons of CO₂ annually, increasing crane use by only 0.4 percent. We explained this by observing the energy-oriented approach frequently uses a
slower crane trolley for transport, which consumes less energy than the faster portal movement. Leveraging Plant Simulation brought us to where we could provide clear decision support, not only for tactical warehouse configuration but also for storage place assignment during operational crane scheduling.”

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Planning for the future

Overall, using the Plant Simulation analysis provided valuable insights. “Using Plant Simulation, we demonstrated how energy orientation can be integrated into a simulation model,” says Oetjegerdes. “Furthermore, we showed the practical benefits as a decision support for various planning problems in the steel industry. Together with our participating manufacturer as an industry partner, we can support decisions with validated models, leading to significant savings in crane energy consumption for storing steel coils.

“Thanks to Plant Simulation, we achieved estimated energy cost savings of about €62,000 per year and 143 tons of CO₂ via easy-to-implement adjustments without changing the existing facilities. This is an impressive result, which is why we see an extended energy-oriented approach to simultaneous planning of warehouse configuration, order sequence and storage
allocation as promising for future research. It is also promising to explore transferring the energy-based approach to various applications, such as container terminals and any other industry that uses overhead cranes. Using Plant Simulation made these valuable contributions possible and helped us achieve greater sustainability via optimized planning.”

Using Plant Simulation made these valuable contributions possible and helped us achieve greater sustainability via optimized planning.
Patrick Oetjegerdes, Research Associate, AIP