Fate of the Vasa, The

Alan D. MacCormack; Richard   Mason

Case | HBS Case Collection | August 2004 (Revised June 2005)

Fate of the Vasa, The

Abstract

In 1628, the royal warship Vasa was launched. It was Sweden's most expensive naval vessel ever built, costing over 5% of GNP. On its maiden voyage, the ship sailed 1,400 yards in its own harbor, heeled over to the side, and then sank. One third of the 150 crew and officers were killed. An inquiry was convened to establish the cause of the disaster, with testimony taken from, among others, the ship's captain, its officers, the ship's designer, and those responsible for its construction. No one was found guilty of negligence. The question is "Why did the Vasa sink?" The answer lies in the state of knowledge about shipbuilding of the time, the continual changes requested by the king, who was fighting in the Baltic, and the resulting experimental nature of the design.

Keywords: History; Risk and Uncertainty; Technological Innovation; Ship Transportation; Product Design; Technology Adoption; Failure; Business and Government Relations; Product Development; Sweden;

Format: Print

8 pages

Educators Purchase

Citation:

MacCormack, Alan D., and Richard Mason. "Fate of the Vasa, The." Harvard Business School Case 605-026, August 2004. (Revised June 2005.)

This teaching note accompanies the case “Innovation at Uber: the Launch of Express POOL” (case no. 619-003). Set in March 2018, the case follows ride-sharing company Uber as it develops and launches a new product called Express POOL. The teaching note offers guidance for instructors who wish to use the case either with or without its supplementary dataset (courseware no. 619-702) and supplementary video short (courseware no. 620-702).

Existing application performance management (APM) solutions lack robust anomaly detection capabilities and root cause analysis techniques that do not require manual efforts and domain knowledge. In this paper, we develop a density-based unsupervised machine learning model to detect anomalies within an enterprise application, based upon data from multiple APM systems. The research was conducted in collaboration with a European automotive company, using two months of live application data. We show that our model detects abnormal system behavior more reliably than a commonly used outlier detection technique and provides information for detecting root causes.

Faculty & Research

Fate of the Vasa, The

Abstract

Citation:

About the Author

More from the Author