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For any industry, adopting new technology can be costly both in time, and money. As most of you know, adopting simulation modeling is the way around making costly unnecessary changes and depleting human resources. New to the simulation and modeling concept as a decision-making and problem-solving tool, is the oil & gas industry. Alvaro Gil from Steam Systems Ltd. guides us through the implementation of simulation modeling, the data behind the models, bumps in the road, and ultimately the return on investment. Utilizing simulation and modeling to address oil & gas industry challenges, Stream Systems helps clients realize billions of dollars in cost-savings.

Calling all Simulation Professionals - WSC 15 is now accepting Case Study Track submissions! This is an opportunity for professionals across all industries to share their innovative solutions using AnyLogic simulation and modeling software. Are you a thought-leader involved in the simulation profession or who use simulation to solve complex business problems in your organization? Submit your case, represent the AnyLogic community, and join over 800 international attendees at the WinterSim Conference in Huntington Beach, California on Dec 6-9th, 2015.

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

“Cesarean delivery” is a method of childbirth in which a surgeon cuts through the pregnant woman’s abdomen and uterus to deliver the baby. The more natural method of childbirth is called “vaginal delivery”, in which the baby leaves the mother’s uterus through her vaginal canal. Ideally, cesarean delivery would only be used when vaginal delivery would endanger the life or health of the child or mother, because cesarean delivery involves major abdominal surgery that is accompanied by much greater risks for both mother and child than vaginal delivery. Cesarean delivery also costs about 50 percent more. Over the last 40 years, the U.S. rate of cesarean delivery has increased dramatically.

There are many cases of simulation modeling in healthcare. Application areas can vary, from process optimization in hospitals to macrolevel agent-based epidemiology models. Due to its multimethod nature, AnyLogic allows models to be built at various abstraction levels. A good illustration of how researchers and consultants can apply the same tool to different problems is the three models built by the Stockholm County Health Administration in Sweden. The models included macro, meso, and micro abstraction level applications in healthcare simulation. The microlevel model simulated the maternity ward in a hospital that was currently under construction. The purpose of the model was to support discussions related to which resources, capacity, and work methods were required in the new ward. One relevant discussion was whether to keep mother and child in the same room during their entire stay or to have dedicated rooms for antenatal care, delivery, and postnatal care.

While the extensive rail library was a key reason that CSX chose AnyLogic as its general purpose simulation tool for the Network Modeling, Operations Research, and Process Excellence groups, the other libraries and methods have added significant value as well. In fact, the first major project where AnyLogic was used did not utilize the rail library. After reviewing the problem in more detail, a discrete-event simulation model was built to help managers studying train throughput. The model simulated the demand of empty trains from five coal mines, as well as the fulfillment of the demand. A supply-chain-like network model was created, which implemented logic to depict the demand, supply and staging of empty trains. The trains were modeled as moving entities across the network. By varying values of relevant parameters, users can infer the impacts of different factors to the train throughput (i.e. siding staging capacity and loading speeds at the coal mines). The model provides a way for decision makers to gain insight into the system to help identify the maximum possible throughput. The objective was to identify the best operational/capital strategy to handle the increased business.

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.

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. 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.

Inland waterway logistics have their own special aspects that include high seasonality due to weather conditions, long delivery times, restricted door-to-door delivery capabilities, and more. On the other hand, river transport is cheap, environmentally friendly, and allows large amounts of goods to be carried. Barges are widely used to carry both bulk cargo and containers. Simulation can help companies justify switching to river logistics by comparing water transportation to other means in terms of costs, throughput, or shipping time. Shipping operators can use simulation to optimize routing and fleet management policies, taking into account uncertainties, breakdowns, bottlenecks, weather conditions, and water level. The following is a success story of river transport simulation-based optimization in South America.

The sphere of public transportation services in Australia is undergoing a transformation in response to a number of drivers, such as a need for inter-modal integration and widespread introduction of consumer information technologies. However, in order to better address new challenges associated with these changes one must first develop an understanding of the dynamic relationships between the way public transportation system operates and the many ways in which people use it to achieve desired mobility. The public transportation company employed PwC to develop a solution for better decision-making and problem-solving. A model was built using AnyLogic 7 as a platform to unify and animate various static data into a dynamic system of interactions. The model is based on three sources of information: network structure provided in GIS format, service timetable in tabular form and ticket sales information gathered from a variety of systems.