Mining Company's Global Supply Chain - Random Logistics Data from a Caterpillar

The company which provided the dataset is the world leader in manufacturing of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. The current revenue of the company is estimated to be on the order of tens of billions and they sell products and parts via a worldwide dealer network. The company sells more than 3 million products and 700,000 parts in more than 20 countries around the world every year. They operate with more than 3,000 suppliers and 3,000 dealerships and their logistics operations alone are worth more than 60 million dollars per year. The dataset provided is one example of supply chain problem for one product of the company - a medium size excavator. In the current dataset, the number of dealers, production facilities and shipping ports is the same as in the original problem; it is only the demand figures, the production capacities, the transportation times and costs and the sale prices that have been randomly generated. The figures have been randomly generated in an interval between 0 and an upper limit which is a random increase over the maximum value in the original data, according to a negative exponential distribution.

提供本数据集的企业为全球建筑与采矿设备、柴油及天然气发动机、工业燃气轮机以及柴电机车（diesel-electric locomotives）制造领域的领军企业。该企业当前营收预估达数百亿美元级别，通过全球经销商网络销售产品与零部件。其每年在全球20余个国家售出超300万台产品及70万件零部件，合作供应商与经销商均超3000家，仅物流业务一项年规模便超过6000万美元。本数据集为该企业针对某款中型挖掘机的供应链问题示例案例。当前数据集中，经销商、生产设施及航运港口的数量与原始问题完全一致，仅需求数据、生产产能、运输时长与成本以及销售价格为随机生成。上述随机生成的数据取值区间介于0至预设上限之间，该上限为原始数据最大值的随机增幅，数据生成方式服从负指数分布（negative exponential distribution）。