FedEx Information Management: Data Warehousing Strategy
This paper examines how FedEx leverages information management across its operations and sales functions to drive efficiency and customer service. It traces the flow of package-tracking data from pickup to delivery, explains how performance metrics and route data inform resource scheduling, and discusses how customer information supports sales strategy. The paper then evaluates data storage solutions—specifically data warehousing and data marts—recommending both as complementary tools suited to FedEx's scale and operational complexity. Data warehouses store all enterprise-level information, while data marts provide targeted, lightweight data sets accessible to field devices and department-specific applications.
- Information Management at FedEx: How FedEx collects and applies operational and sales data
- Fundamental Impact on Efficiency: Data's role in maximizing capacity and reducing costs
- Data Storage Solutions: Data warehousing and data marts explained for FedEx
- Recommendation: Why both warehouse and mart solutions are advised
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What makes this paper effective
- Concrete operational examples—such as the step-by-step package scanning process—ground abstract information management concepts in real-world logistics practice.
- The paper moves logically from how data is collected, to why it matters strategically, to what storage architecture best supports it, creating a coherent argument structure.
- The distinction between data warehouses and data marts is clearly explained with a practical example (mobile courier devices needing limited data sets), making a technical topic accessible.
Key academic technique demonstrated
The paper uses applied analysis: it identifies a business context, explains the relevant technical concepts using cited sources, and then produces a justified recommendation. This moves beyond description into evaluation, which is the hallmark of undergraduate-level business writing.
Structure breakdown
The paper is organized into four sections. The first two sections establish FedEx's data needs—operationally and strategically. The third section introduces and explains the technical storage options (database, data warehouse, data mart). The final section synthesizes the analysis into a clear dual recommendation, with justification grounded in the operational constraints described earlier.
Information Management at FedEx
Information management is essential for FedEx. The company uses information in a number of ways. On the operations side, data drives decisions regarding the scheduling of assets. Every piece has its own delivery time guarantee, for example, and it is necessary to use information to ensure that the right package gets on the right plane and the right truck to reach its destination.
The process works as follows. The customer goes online and schedules a pickup, and the automated system sends that information directly to the courier who works that route. The package is scanned at pickup, and the tracking number becomes the basis for all information associated with that package — the person at the destination can see within minutes that the package has been picked up. From there, the package is scanned at the local depot, when it goes on the plane, when it lands at the hub, when it is sorted for the next plane, and then when it lands at its destination airport. It is scanned again when it is loaded onto the truck for delivery, and once more upon final delivery. Embedded in the tracking number is a wealth of information, such as the weight of the package, the package type, and other data used in scheduling resources.
FedEx knows the mean volume for each of its scheduled flights, and routes are designed around a particular mean volume that an individual courier can handle. Each decision regarding capacity is therefore made on the basis of the information gathered by the company. Routes are designed according to the load they are expected to carry.
Furthermore, performance reviews are conducted on the basis of this data. Each station and each route has a set of specifications derived from historical data. Data is collected regarding how many stops can be completed within a given time frame on average. There are data points for every business day — more than 200 per route per year. With that many data points, FedEx is able to understand the mean and standard deviation for performance on any given route, and use that information in employee performance reviews. The same principle applies to other roles: sorters who scan packages at stations, for example, have specifications for how many packages are normally processed per hour.
FedEx also uses data in sales. The company identifies its best customers based on shipping volumes, and deploys sales staff to work with those clients and ensure their needs are met. New business opportunities can emerge from this information — sales staff can speak with high-volume clients to identify unmet needs, and FedEx can respond accordingly. This is why the company has expanded into services such as customs clearance: these offerings are closely related to the overnight shipping business and reflect needs that customers expressed.
Fundamental Impact on Efficiency
FedEx has always used data to drive its business, with the objective of maximizing efficiency. The company recognizes that moving the volume of goods it handles could be an incredibly inefficient process. Flying planes is expensive. The company's core efficiency objective is to optimize the capacity of critical resources, starting with its aircraft. If each plane flies as full as possible — and each truck as well — the company operates at optimal efficiency. In this way, FedEx can keep its fixed costs as low as possible by leasing the fewest planes necessary while still meeting all customer needs. The same logic applies to trucks: the company aims to run as few routes as possible while fully meeting customer demand, keeping costs low in the process.
On the marketing side, FedEx must know who its customers are, particularly its best customers. The more it knows about its customers, the better it can serve them. This is important because the company depends on regular, high-volume businesses to ensure that capacity is maximized. Some customers are unpredictable in their shipping behavior, but those who are more predictable are highly valuable because of the contribution they make to the company's overall efficiency.
References
Cardon, D. (2016). Database vs. data warehouse. Health Catalyst. Retrieved April 30, 2016 from
Standen, J. (2012). Data warehouse vs. data mart. Datamartist. Retrieved April 30, 2016 from http://www.datamartist.com/data-warehouse-vs.-data-mart
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