HAVI is a $5 billion global company and McDonald’s long-time supply chain and packaging partner. They provide services for supply chain management, packaging, logistics, and recycling and waste. When McDonald's wanted to build on the success of All-Day Breakfast and introduce additional menu items at 14,000 restaurants, they chose HAVI to complete the project.
The world’s largest companies use data analytics to increase their revenue and keep up with the changing business world. But how does data science relate to simulation modeling, and what are the cases for the implementation of this interaction, primarily concerning value for the business? The United Services Automobile Association (USAA), a Fortune 500 group of companies, has answered these questions with real-life solutions.
The BNSF Railway Company is one of the largest freight railroad networks in the United States. The company has 32,500 miles of track in 28 states and three Canadian provinces, and more than 8,000 locomotives. Expanding the company’s existing facilities, and developing new facilities in intermodal segments, would require making large capital investments. To help evaluate various options of capacity expansion in major facilities, and make data-informed decisions, BNSF developed container terminal simulation models of the Corwith and Hobart facilities.
GlaxoSmithKline (GSK) was the world’s sixth largest pharmaceutical company in 2014. The company was launching a new vaccine product on a new market that needed a distribution network different from what they had before. Therefore, the company needed to design a new supply chain and align manufacturing processes with it.
Kernel is the world-leading manufacturer and exporter of sunflower oil and provider of agricultural products from the Black Sea basin across world markets. The company owns 550,000 hectares of land and more than 40 grain elevators, with a total storage volume of up to 2.8 million tons. One of the company's regular challenges is the planning, harvesting, and transportation of agro-products.
One of the largest resource companies in the world, with over $80 billion in sales, decided to enter a new market. It was planning to build a new potash mine and export 90% of production. They wanted to design a reliable supply chain, with high-speed replenishment, and the ability to recover, or even benefit, from disasters, both natural and man-made. Amalgama and Goldratt were contracted to design the potash mining operations and a full supply chain for outbound logistics.
Sterling Simulation consulting company was chosen to provide an agent-based marketing model for a pharmaceutical firm. The company owned two competing non-generic drugs on the same market. One drug was well established and tended to be the industry leader, and the other one was recently introduced. There were several concerns about how to obtain a useful market share for the newer drug, while maintaining or increasing the market share for the company’s drugs as a whole.
Indiana University Health Arnett Hospital, consisting of a full-service acute care hospital and a multispecialty clinic, faced poor statistics because the number of no-show patients (those who don’t show up for their scheduled appointments) rose dramatically to 30%. This was primarily connected to the fact that clinic schedules were driven by individual preferences of the medical staff, which led to increased variations in scheduling rules. To eliminate the problem, the client wanted to develop a scheduling methodology that would benefit the clinic, doctors, and patients.
In an effort to find practical operational solutions for a response to an unexpected crisis or natural disaster, Battelle, the world’s largest, non-profit, independent R&D organization, needed to test the effectiveness of a 48-hour shelter-in-place order for an Improvised Nuclear Device scenario. The goal was to reduce radiation dosages received during an uncoordinated mass evacuation, by comparing immediate evacuation and shelter-in-place order.
The Sterling Simulation consulting company was hired by a pharmaceutical giant to create a supply chain model. The client was introducing one of their products to new markets and wanted to visualize the restructuring of the supply chain and how it would react to demand uncertainty. The company’s goal was to reduce lead time while maintaining fill rate and avoiding backorders or lost sales. The client wanted a flexible ‘what if’ tool for risk mitigation analysis.