Case studies

Engineering Ingegneria Informatica is an international specialist in the field of digital system integration. The company has more than 11,000 employees in more than 50 offices around the world. One of its flagship projects is an ecosystem of platforms that enable other technologies to interact with each other – exchanging value, digitizing processes, developing digital services, and creating value for users, especially through the leverage of 8D.

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.

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.

The Airbus Group joined the European Union ARUM (Adaptive Production Management) project, which is focused on creating an IT solution for risk reduction, decision-making, and planning during new product ramp-ups. The project is aimed mainly at aircraft and shipbuilding industries. Simulation was chosen as a part of the ARUM solution, because it would allow the participants to reproduce the real production facility experience.

One of the largest turbine manufacturers in the world had a very promising five-year portfolio of gas turbines to produce and was planning an optimistic 30% net margin. The company’s portfolio consisted of over 100 programs, made of 1000’s of projects, with each project composed of a number of phases. Relying on its good past performance, the company built their strategic competitive edge around their reliability, enabling them to offer penalties for late delivery and bonuses for early performance.

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.

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.

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.

Manufacturing and economic conditions are so complicated and unstable that it makes traditional scheduling methods ineffective. A major Eastern European steel pipe manufacturing plant decided to improve production planning using simulation software. In addition, the company wanted to minimize gaps and daily fluctuations of the orders handover, identify bottlenecks, and improve order routes.

A biotech startup, providing experimental personalized medical treatment, wanted to conduct data-driven pharmaceutical manufacturing simulation to optimize decision-making processes when delivering new products. These products were undergoing clinical trials, and the manufacturing processes remained unclear while US Food and Drug Administration (FDA) approval was pending. The company was short of resources, but it was crucial to produce its products as fast as possible and without failures.