Case studies

In 2012, GE opened a new battery manufacturing plant in conjunction with the launch of an innovative energy storage business. GE’s exciting opportunity brought on many new challenges, such as increasing production throughput and yield under evolving processes and uncertainties, and reducing manufacturing costs in order to gain market share. The GE Global Research Center sought out a powerful and flexible tool to analyze, not just the specific process, but the manufacturing system as a whole.

With AnyLogic simulation software as the centerpiece, NASSCO utilizes a custom-built analysis system called the Large Scale Computer Simulation Modeling System for Shipbuilding (LSMSe) to provide highly detailed and accurate capacity analyses for both current production and potential new work.

The managers of one of the most important Italian yacht manufacturers needed a new, intelligent approach that would make the planning process simpler. The objective was to give the real production planner exceptionally rich planning information, which would allow the person to test and refine a plan before its implementation.

Conaprole, the biggest dairy production company in Uruguay, produces more than 150 SKUs in their ice cream plant, using five production lines, and up to five different packaging configurations for each line. The management’s challenge was to be able to reformulate their plans in order to balance supply and demand and make sure they would avoid stock-outs in key products. They also sought ways to optimize the use of their production capacities.

Intel factories used a particular type of equipment that often broke down, which caused capacity constraints. These expensive parts were used in critical factory operations, and the repairs took significant time, so it was necessary to have extra spare parts on hand to avoid downtimes. Broken parts caused constraints at some of the factories while other factories over purchased spares.

The Agent-based Turbine Operations & Maintenance (ATOM) model is a digital twin simulation model developed by decisionLab Ltd and Siemens. The digital twin emulates the global maintenance repair and overhaul (MRO) operations of Siemens’ aero-derivative gas turbine division. Driven by live data already available within the supply chain, the model provides the capability to use sophisticated simulation and data-analytics methodologies to optimize the fleet operations of Siemens, enabling better data-driven decision-making to improve productivity and efficiency in customer operations and asset management.

Today, many steel manufacturers are in need of lean manufacturing tools that will improve their return on investment and service levels. The minimum 80% reliability level most steel companies are struggling to achieve is nowhere near what today’s customers and investors want to deal with. One of the largest and oldest European steel manufacturers came across these problems and was desperately trying to solve them. The company called upon the assistance of Goldratt Research Labs.

CNH Industrial is a global leader in capital goods. It is financially controlled by the Italian investment company Exor and is comprised of 12 brands, including Case, New Holland, and Iveco. Through its brands, CNHi designs, produces, and sells a wide range of agricultural, industrial, and commercial vehicles and powertrains. CNH Industrial identified maintenance processes as a promising area to start applying new Industry 4.0 technologies. In the automotive and related industries, downtime costs can be large. As such, improving maintenance in order to reduce downtime can deliver significant success.

Centrotherm Photovoltaics AG is a global supplier of technology and equipment for the photovoltaics, semiconductor, and microelectronics industries. The company needed to identify the best automated production line and factory configuration to minimize costs and maximize throughput and reliability.

The model simulates the redesigned ground environment and production processes of the shop floor to be renovated. This is one of the significant examples of simulation modeling technology appliance in metallurgical industry.