Department of Mathematics

Proposed Project for the MSU Industrial Math Students

Steelcase, Inc.

Multi-Lens Study: Making Sense of Mixed Data to Understand Dynamics of the Office

Background:

Steelcase has developed a proprietary sensing capability that goes beyond traditional approaches. In addition to placing passive infrared sensors throughout office spaces to measure usage of the space, we provide customers with a mixed methodology including surveys as well as multiple types of sensors. The capability also includes mapping patterns of movement through the space and identifying key metrics from floor plans of the spaces that are studied.

Impact:

Steelcase customers are leading customers and vary by size, industry, region, and culture. These customers engage Steelcase in order to measure their space and employee perceptions. The data they receive from Steelcase is a critical element in their decision making on real estate and facility decisions worth millions of dollars. The outcomes of the study will 1) add to the data that customers use to make these million dollar decisions 2) help Steelcase continue to develop the offering to customers and expand and extend the data that we make available to them 3) help Steelcase frame and market the capability to customers, and 4) contribute to Steelcase’s next evolution of the capability.

Types of Data:

We are particularly interested in finding ways to connect and correlate differing sets of data from multiple sources and multiple companies.

  • Sensing data from sensors installed in office space – related to usage and occupancy
  • Sensing data from sensors installed in the office space – related to the preferred traits associated with the space
  • Survey data from participant responses to questionnaires
  • Data from floor plans that can be coded in order to connect it with the sensing data (ex. Types of space, adjacencies, square footage, etc.)
  • Space syntax data – patterns of movement through space that can be coded in order to study in relationship to survey and sensing data

Questions:

The key questions that we’d like to answer through the project include the following:

  • What are the trends in the sensing data across all studies?
  • Based on company performance (from publicly available data), what might we predict about space usage? In other words, when companies perform better, what is the nature of their space usage? What can we predict, recommend, or suggest as best practices from the data?
  • In what ways is space usage driven by traits in the space that people say they prefer? What are the connections between the sensing data (how people behave) and the survey data (what they say)?
  • In what ways is space usage connected to floor plan data and the space syntax data? What are the trends? Can we make any predictions?