Measuring and Improving Service Productivity

There's a generally held view in the research literature that improving service productivity is a challenging if not elusive goal, in the main due to the difficulty in measuring it. Productivity is the relationship between the inputs to a process and the process outputs. Where the the outputs of the process increase while the process inputs remain constant or are reduced, then productivity is increased. We get more for the same of less.

The concern raised about service productivity is the difficulty in measuring the perceived quality of the service encounter, before and after the efforts to increase productivity. Service quality it is said, is in the eyes of the beholder, and given the need to include the beholder ( the service customer) in the service production and delivery process (the quality of the customers inputs directly effects the ability to serve them), then every service delivery encounter is a unique and varied experience and therefore not comparable for the purpose of measuring productivity. Services therefore are "Open Systems" , open to the variability of each customers inputs and to their varying perceptions of the service as they encounter it. Service and service quality therefore is intangible and difficult, if not impossible to measure.

The manufacturing or "product" sectors have traditionally solved this problem by creating a "Closed System" that limits or excludes the actual buying customers inputs. Customer inputs are captured at the design stage via market research ( a sampling process) and product quality is defined as adherence to the design specification. In other words "we made and delivered what's in the product specification",  a specification derived from satisfying the expectations of the target market sample. Quality therefore is standardized and the productivity of the production process  is measured by the number of products produced that meet the product specification given the relative process inputs. This concept is known as "constant quality".

However the product sectors and the service sectors are using different quality measures, what the legendry quality guru Joseph Duran described as "small q" and "Big Q". "Small q" measures output quality  and adherence to the product and process speciation, while "Big Q" measures the customers "experiential outcomes". Were manufacturers to measure "outcomes" and not "outputs", they would have the same challenges in measuring productivity.  By separating outcomes and outputs they have also separated "productivity" from "customer experience" whereas service organisations have  (unconsciously and mistakenly I would argue)  combined or possibly confused them. 

This confusion has contributed to the complexity burden that plagues service and knowledge work organisations. Separating and measuring "small q" and "Big Q" in service and knowledge-work organisations is amongst the first  steps necessary to facilitate the definition and measurement of service productivity and therefore, dramatically improve it.

"Big Q" (the process outcomes and Customer Experience) should be a balancing metric for "small q" ( the process outputs) as improving outputs (delivering what we said we would deliver) at the expense of outcomes ( meeting customer expectations) is a fools errand in the medium and long term for both manufacturing and service organisations, assuming a competitive market. In the absence of a competitive market, as in the public sector, the gap between outputs and outcomes needs to be monitored by third parties, usually regulators.