The purpose of this paper is to develop a model to harness occupancy sensing in a commercial hot-desking environment. Hot-desking is a method of office resource management designed to reduce the real estate costs of professional practices. However, the shortcoming is often in the suitability and appropriateness of allocated work environments. The Internet of Things could produce new data sets in the office at a resolution, speed and validity of which that they could be factored into desk-allocation, distributing seats based on appropriate noise levels, stay length, equipment requirements, previous presence and proximity to others working on the same project, among many others.
The study utilises primary data from a commercial office environment in Central London (numerical building system data and semi-structured interviews) to feed a discrete events simulator. To test the hypothesis, the authors look at the potential for intelligent hot-desking to use “work type” data to improve the distribution of individuals in the office, increasing productivity through the creation of positive “work type environments” – where those working on specific tasks perform better when grouped with others doing the same task. The simulation runs for a typical work day, and the authors compare the intelligent hot-desking arrangement to a base case.
The study shows that sensor data can be used for desk allocation in a hot-desking environment utilising activity-based working, with results that outweigh the costs of occupancy detection. The authors are not only able to optimise desk utilisation based on quality occupancy data but also demonstrate how overall productivity increases as individuals are allocated desks of their preference as much as possible among other enabling optimisations that can be applied. Moreover, the authors explore how an increase in occupancy data collection in the private sector could have key advantages for the business as an organization and the city as a whole.
The research explores only one possible incarnation of intelligent hot-desking, and the authors presume that all data have already been collected, and while not insurmountable, they do not discuss the technical or cultural difficulties to this end. Furthermore, final examination of the productivity benefit – because of the difficulty in defining and measuring the concept – is exploratory rather than definitive. This research suggests that not only human-centric smart building research should be prioritised over energy or space-based themes but also large-scale private sector collection of occupancy data may be imminent, and its potential should be examined.
Findings strongly suggest that the hot-desking may cost more in lost productivity than it gains in reduced rental costs and as such many commercial offices should revaluate the transition, particularly with a view to facilitate intelligent hot-desking. Companies should begin to think strategically about the wider benefits of collecting occupancy data across their real estate portfolio, rather than reviewing use cases in silos. Finally, cities should consider scenarios of widespread collection of occupancy data in the private sector, examining the value these data have to city systems such as transport, and how the city might procure it for these ends.
This paper raises positive and negative social concerns. The value in occupancy data suggested herein, bringing with it the implication it should be collected en mass, has a noted concern that this brings privacy concerns. As such, policy and regulation should heed that current standards should be reviewed to ensure they are sufficient to protect those in offices from being unfairly discriminated, spied or exploited through occupancy data. However, the improved use of occupancy data improving workplaces could indeed make them more enjoyable places to work, and have the potential to become a staple in company’s corporate social responsibility policies.
This paper fulfils an identified need for better understanding the specific uses of occupancy data in the smart building mantra. Several sources suggest the current research focus on energy and rental costs is misguided when the holistic cost of an office is considered, and concepts related to staff – although less understood – may have an order of magnitude bigger impact. This research supports this hypothesis through the example of intelligent hot-desking. The value of this paper lies in redirecting industry and research towards the considering occupancy data in smart building uses cases including – but not limited to– intelligent hot-desking.
This work was supported by the Systems Centre, the EPSRC funded Industrial Doctorate Centre in Systems and Frazer-Nash consultancy. The contribution of D. Jones and D. Nepomuceno to the background research is also acknowledged.
Cooper, P., Maraslis, K., Tryfonas, T. and Oikonomou, G. (2017), "An intelligent hot-desking model harnessing the power of occupancy sensing data", Facilities, Vol. 35 No. 13/14, pp. 766-786. https://doi.org/10.1108/F-01-2016-0014Download as .RIS
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