Making a Match

15 November 2006

Benchmarking with other in-house FM organisations in the same business sector is 'dysfunctional' says Dr Sezgin Kaya. He argues that it is more effective to compare the similarity and dissimilarity of FM organisations, and he has developed FMMatchmaker to help find the right partners.

WHEN CLIENT FM ORGANISATIONS want to benchmark their practices, they tend to look at their immediate domain and seek FM organisations operating in the same market sector. It is common that, for example, FM practice in a financial organisation is benchmarked against another financial organisation. Although the results of such benchmarking might be attractive to business managers because they can compare their organisation to those of their potential competitors, representations of in-house FM organisations with reference to their host organisation’s market sector as the single reference point for benchmarking is misleading and dysfunctional

Until recently, in-house or client FM organisations have often been clustered and grouped together according to host organisation’s market sector. For example inhouse FM organisations operating within retail organisations are called ‘Retail FM Organisations’. Examples can be extended to FM organisations operating in public/private, financial, industrial, and pharmaceutical sectors. As a consequence we have clustered and defined client FM organisations based on the assumption that each client FM organisation (FMO) exhibits the characteristics of its host organisation. This assumption overlooks the distinctive patterns of client FM organisations operating in the same market sector such as procurement options, occupancy profile, change cycles, organisational structures, relative size, facility diversity, service visibility, service interaction and specialisation. In the past, benchmarking studies have been conducted with little consideration of these distinctive patterns.

Interviews with leading academics, client organisations and consultants of benchmarking showed me that the current market sector based benchmarking practice has two major negative side effects:

.....It only helps the weakest practice to become a standard one, and does not add any value to organisations performing better than the average.

.....The current practice has led to wrong business decisions because judgmental decisions have been made in the past without necessarily knowing what the differences are between operations of two FM organisations.

To find the differences between FM organisations I have thoroughly investigated literature and conducted interviews with consultants and clients involved in market sector based benchmarking studies. The results of this exhaustive study (part of my PhD thesis) shows that the differences between two client FM organisations can be classified around

.....Occupancy profile of those whom FM is responsible for delivering services

.....Service interaction duration within the service setting, during when customers of the host organisation use the facilities provided by the FMO

.....The change cycle of the host organisation by which facilities and total occupancy have been influenced

.....Procurement type: single service outsourcing, total facilities outsourcing, managing agent, management of contracts, corporate PFI, partnerships, direct service supervision or bundled service outsourcing. In addition, differences have been highlighted in the same research on the internal structure and format of client FMO; such as:

.....Chain of command (hierarchy): the levels in FM organisational structure

.....Specialisation: how the jobs are broken down into narrow areas of work and responsibility

.....Succession routes: the degree to which jobs within the organisation are designed for the career progression

.....Geographical dispersion: the number of geographical regions FM has responsibilities for

.....Reporting Line: the line manager of FMO

.....Size: the relative size between FMO and the context organisation calculated as number of full time employees (FTE) for FM divided by number of FTE of the context organisation.

To test the accuracy for how the 10 patterns distinguish one FMO to another, 22 client side FM organisations from a variety of sectors were surveyed. They included five public sector and healthcare, seven financial institutions, two media and information services, three ITC, one information service, and four infrastructure organisations including public administration, energy and transportation. The total number of employees in the sample population was over 530,000.

The data has been analysed by a pattern recognition technique, similar to its uses in biometrics. The technique helped researchers identify and recognise the dominant patterns in the sample population. Results emerged from a comprehensive computer simulation of these client FM organisations—anonymous to the investigation. The simulation has identified three distinct classes (w1, w2, w3) as plotted in diagram right. The plotted points represent each of the FMOs, and ideally, the closer the plotted points, the more similar the FMO are.

An example of the prevailing characteristics of each of the first group is Class 1 (w1). It includes a variety of sectors including four financial organisations, one media and information service, two IT, one transportation, one professional law company, and one public administration organisation. This mixed group of companies challenges the current practice that supports market sector based clustering.

The characteristic features of this class are:

.....Reporting to Operations or Property Director

.....Hierarchical FM in hierarchical organisations with a number of succession routes

.....Operating in multi-sites

.....Less specialised in-house team demanding for expertise in suppliers, and hence less in-house FM staff employed in relation to the personnel number of the host organisation.

.....Medium visibility of FM operations to customers and recognition of FM

.....Higher personnel demand for quality.

TFM is trend, but a diverse portfolio is a hurdle

Later in the study, to test the validity of similarities in their peer groups, two organisations, Ss and OH, have been randomly selected out of the 22. Ss is a media and information service company, and OH is an energy related company. Firstly, Ss’s closest match, AGT, a global financial organisation has been introduced to Ss. Surprisingly, it has been stated that Ss had already been sharing information with AGT on an informal basis about the constraints and pressures of managing their facilities. The Head of Ss said that he was meeting regularly with the Head of AGT at a local ‘pub’ in London for sharing each other’s ideas. AGT and Ss are still communicating at strategic, tactical and operational level during the preparation of this document and transferring their experience from one to another. The most recent knowledge transaction between AGT and Ss has involved “strategic assessment of supplier organisations” by which Ss has employed AGT’s way.

Secondly, OH’s closest match, MH, a law company has been introduced to OH. Both were unaware of each other, and obviously have not thought to be peers of each other since they are from totally different market sectors. However, in fact, they are similar to each other in seven dominant characteristics: size of FM organisation, change cycle of the host organisation, procurement type (both have chosen bundled service provision), hierarchy, geographical dispersion, occupancy profile, and reporting line. Following this introduction, OH and MH have found three areas to learn from each other and started the knowledge transaction on: flexible working, business growth and its pressures on the facilities, and the standardisation of FM processes for corporate look. At the end of the initial meeting, OH’s senior facilities manager stated that: “Although we operate in different businesses, 75 per cent of our activities and the way we deliver solutions to business are similar”.

To find the best matching organisations, a new software has been developed, called FMMatchmaker. This software helps client FM organisations find their similar organisations, and identify their peer group’s characteristic features. In addition, if a client FM organisation decides to benchmark against and learn from FM organisations with certain characteristics, the software searches and finds this target cluster gives clients the flexibility to choose a target cluster to benchmark.

.....Dr Sezgin Kaya is Research Fellow in the Centre for Facilities Management, University of Salford. His thesis, which will be published in Facilities shortly, was supervised by Professor Keith Alexander and Dr John Zeisel. A 30 page summary of Dr Kaya’s PhD thesis is available on request.