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How a digital twin can help decision making


How a digital twin can help decision making

Digital twins are part of Industry 4.0. How are they being used on automobile production lines? Here we investigate how a digital twin is made and used in the automotive industry.

One of the world’s largest capital goods companies, CNHi, wanted to evaluate Industry 4.0 technologies. It chose its IVECO production plant in Suzzara, Italy. Fair Dynamics were contracted and proposed a digital twin. Read on and discover how maintenance costs can be reduced with a digital twin — Production line downtime was shown in a study of over 100 automotive executives to cost an average of $22,000 per minute.

The development of Industry 4.0 in Germany: an interview with Yuri Toluyev


The development of Industry 4.0 in Germany: an interview with Yuri Toluyev

Industry 4.0 was the subject of Yuri Toluyev’s plenary presentation at the IMMOD simulation modeling conference in Saint Petersburg. He described how new approaches to enterprise development, united by the concept of Industry 4.0, are driving technological development.

AnyLogic attended the event and interviewed Toluyev about Industry 4.0, its development, and its implementation. Interesting and informative, read on for Yuri Toluyev’s Industry 4.0 insight...

Ford Motor Company Selects AnyLogic for their Simulation and Modeling Technology


Ford Motor Company Selects AnyLogic for their Simulation and Modeling Technology

We are excited to announce Ford Motor Company's recent selection of AnyLogic for their simulation and modeling needs. Ford Motor Company's substantial analytics team was looking for simulation modeling technology that goes above and beyond discrete-event only tools. The analytics team working with AnyLogic will provide decision-making and problem-solving techniques for multiple industries inside Ford, such as manufacturing, finance, and supply chain. AnyLogic is proud to support Ford Motor Company. Check out the press release announced on a variety media outlets.

anyLogistix Version 2.0, Released!


anyLogistix Version 2.0, Released!

We have released the second version of anyLogistix - a new tool for optimizing supply chains and logistics networks. What is anyLogistix? anyLogistix (ALX) - the only multimethod software for supply chain optimization. ALX combines traditional analytical methods of optimization and innovative simulation technology. The combination of different technologies allows you to model and analyze the supply chain, at any level of detail, therefore, finding more effective ways to improve. Who is anyLogistix made for? Companies with large and complex supply chains (i.e. manufacturers, distributors, retailers and logistics providers) can take advantage of anyLogistix. Implementation of ALX is carried out by our partners,consulting companies.

Automated Production Line Optimization: Case Study


Automated Production Line Optimization: Case Study

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 configuration of the automated production line and factory, to minimize costs and maximize throughput and reliability. Throughput and equipment utilization rate metrics were utilized to compare alternatives. Centrotherm needed to avoid possible bottlenecks in the material flow and optimize in-factory logistics. Also, management required taking into account casualties and stochasticity, for instance, the probability of scrap or how the factory would operate in case of equipment breakdowns.

Quick Win for Negotiating with Supplier Using Simulation Results


Quick Win for Negotiating with Supplier Using Simulation Results

Equipment part breakdown in the Intel factories, as in many factories is inevitable. These failures typically cause capacity constraints and ultimatley cost the corporation time and money. Equipment parts can be repaired locally or may require shipping to the vendor for repair. Since the repair loop takes significant time, it is necessary to have extra spare parts on-hand to keep the equipment running while broken parts are repaired. It is pertinent to avoid overbuying of the spare parts, as they are very expensive. Intel needed a model of the repair loop to increase the visibility of problems such as broken parts accumulating at the vendor repair center and sites over purchasing spare parts. At the AnyLogic Conference 2014, Victor Chang, Software Engineer at Intel presents an AnyLogic simulation that was developed to model the complexities and variability

Analysis of Management Strategies for Aircraft Production Ramp-up


Analysis of Management Strategies for Aircraft Production Ramp-up

Growing competition and a high demand for individual and highly sophisticated products in combination with shorter innovation cycles is leading to a rising number of ramp-ups especially in Small batch production. Daily challenges such as late changes and missing maturity of high Technology products and processes create significant risks. Since 2012 a group of 14 European companies and research institutes have developed novel planning and control solutions in the European public funded project ARUM (Adaptive Production Management; www.arum-project.eu) to overcome those challenges in production ramp-up. The validation of the developed control strategies and their implementation into novel planning and scheduling solutions within a realistic industrial environment is mandatory and several industrial use Cases have been selected, e.g. an Airbus system installation flowline in Hamburg.

Production Plan Optimization at Ice Cream Manufacturing Plant


Production Plan Optimization at Ice Cream Manufacturing Plant

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 company plans ice cream production on a 12-month rolling basis as part of the Sales & Operations Planning process, and the demand plan varies a lot due to seasonality. The factory management needs to prepare the production lines for the peak season during the low season, taking into account product shelf life and the warehouse’s freezing cameras’ capacity and costs. The factory was often unable to meet the high season demand that generated stock-outs. In addition, management found it very difficult to reschedule quickly their detailed plans due to the challenges they faced including bottlenecks, production process constraints, and staff turnover.

GE Manufacturing Plant Uses AnyLogic for Real Time Decision Support


GE Manufacturing Plant Uses AnyLogic for Real Time Decision Support

“This is how GE does ‘startups’ – putting our researchers, manufacturing talent and commercial teams to work to create technology that serves customer needs around the world,” Immelt (CEO) said. “GE Energy Storage was born in New York’s Capital Region from an idea that we turned into an advanced manufacturing plant and a global business that we expect will generate more than $1 billion in revenue annually in just a few years.” (www.ecomagination.com, 2012) In 2012, GE opened a new battery manufacturing plant in conjunction with the launch of an innovative energy storage business. The new Durathon battery products, which are half the size of conventional lead acid batteries, but last ten times longer, are the result of GE’s $100 million initial investment in battery technology developed at GE’s Global Research Center (geenergystorage.com, 2014). Expanding the facility doubled production, added 100 new jobs, and brought the total factory workforce to 450 when at full capacity (geenergystorage.com, 2014).


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