2006/08/28 09:30 Paul Lillrank, "Towards a Service Engineering and Management Framework: Process Perspectives on Services", SEM 2006, HUT

Services Engineering and Management Summer School, Helsinki University of Technology, August 28-September 2

This digest was created in real-time during the meeting, based on the speaker's presentation(s) and comments from the audience. The content should not be viewed as an official transcript of the meeting, but only as an interpretation by a single individual. Lapses, grammatical errors, and typing mistakes may not have been corrected. Questions about content should be directed to the originator. The digest has been made available for purposes of scholarship, posted on the Coevolving Innovations web site by David Ing.

Paul Lillrank, Helsinki University of Technology, Professor and Dean, Faculty of Industrial Engineering and Management

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Had started working on process perspective, didn't really get there

• Will start on a production system perspective on services

Agenda

Key idea: service machines

Definitions: what is services?

• Basic two dimensions: Borderline between things we talk about, and don't talk about
  • Professionalism:  services are produced by people who have developed skills, in a community
  • Could create a professionalism scale, where have more/less
  • Not performed by anyone off the street, someone trained.
• Commercialism: what kind of commercial exchange is in place, when something is producted
  • Dividing line, when there is money (unless we include barter)
  • Includes systems to calculate price, and the payment of taxes, accounting systems
• Combining the two, some things look like services, but are not.
  • Favours:  e.g. hitchhiking
  • Barter
  • Could develop into a taxi service
• Look at services when value is created for someone, not enough.
• Need to look at the institutionalism context
• Not-for-profit
• Black market

Literature on services marketing has been focused on some intrinsic features that would make it different from products

• Ontological exercise
• Difference would lead researchers to develop management methods
• Management methods behind manufacturing work well.
• Can we copy management methods from manufacturing to services, or have to develop something new?
• Service marketing, and recently service engineering, as new sections of management
• Reading, doesn't think it leads anywhere

IHIP model:

• Intangible: not something that you can drop on the floor
• Heterogeneous: service production systems are complicated, repetitions are different from one to the next
  • Every customer is a little bit different
  • Standardization and automation can not easily be applied, due to the heterogeneity of the input stream
• Separability: services are identical to the service production function, can't be separated
  • In manufacturing, can put the end result into inventory, separated somewhere else in the factory
  • Service producer and customer have to meet
• Perishability: product won't last
• e.g. airline, empty seat can be solved before the gate closes, and then it's gone forever

On any of these issues, can think of a number of counter-examples

• Customization: this is also increasingly true for products
• Inseparability and perishability:  e.g. a haircut exists as long as it is recognizable
• Inventory and storage: customers can be used as inventory, keep them waiting; can manage capacity

These categorization have limited explanatory functions

• Kotler 2003, from pure tangible good to pure service
• Solutions in the middle
• Could have core products, and add-on services
• Augmentation exists, but it doesn't separate immaterial services from material products
• Another view that everything is services:  service and product distinction isn't helpful in different management techniques
• Reference point:  e.g. word processing is a service for me, but it's a product for Microsoft 
• Mobile telephone:  a product-service

Service product matrix, classifying different types of services

• Labour intensity vs. degree of customization
• Problem:  the same model can be applied to physical products

Warneke, The Fractal Company: production as a system of transformations, from inputs into outputs

From book, Service Operations Management, compared to Operations Management

• Most of the content is the same
• Similarly for Service Marketing versus Marketing
• Then, not smart to create a new program in Service Management, as no new content

Deep integration happened in computer science

• Idea from IBM
• Had a number of problems, created a new content
• Then the old sciences start to combine
• Computer, as a device, has been developing since Babbage, Victorian England, steam powered, huge as a house
  • Same thing as today, but couldn't be done with mechanical machines, friction
  • Had Boolean algebra and portable programming, but no electronics
• After WWII, computer science started emerging
• Computer science is big program, can't be split into component parts
• IBM was the company that made it happen
  • In the 1950s, computers were huge, expensive things
  • Academics didn't see this
  • IBM funded chairs, faculty
• In the 1950s, this wasn't obvious
• There was huge, aggressive debate:  you can't have machine-centric science
• If you insist on a computer science, then I'll have a washing machine science
• Washing machine is made up of some scientific components:  electronics, fluid dynamics, chemistry
• No deep integration
• Computing has a lot more, and wider applications than washing machine science
• Washing machines just replace manual labour
• There might be something inherent in washing machines that don't allow deep integration

First management students in 1964, teacher was physics Ph.D., look at students with disdain; whereas now 2-years do this

• At Northwestern, psychologist and linguist started computer science program
• Similarly, Herbert Simon came from political science
• In computer science today, need cognitive science, etc.
• Electrical engineer doesn't need to know much about chemistry
• Similar, the same thing happening in pharmaceutical

But what's different in service science?

• CEO of IBM is enthusiatic in kicking off service science

An example where deep integration didn't happen:  TQM, Total Quality Management

• Lillrank's speciality, spent a lot of time in Japan
• In the early 1980s, a lot of people thought that it was the most important thing around, e.g. Japanese automobiles and electronics
• Reassembling manufacturing
• Many people believed in this, got a chair in Finland in quality at that time
• As time passed by, recognized that there is no such thing
  • Looks foolhardy
• At heart, theory of variation, and Statistical Process Control not replicated elsewhere, by Walter Shewhart at AT&T in the 1920s
  • This helped people do things, reduce errors
  • Japanese adopted these after WWII, and became a world manufacturing power
  • At the same time, it looked like deep integration, but it was not
  • Will eventually run into management problems:  how to do organization issues, etc.
• In Japan, no business schools, and the economics departments don't talk to business
  • Japanese needed to import consultants, etc., got into quality-oriented views of marketing
  • But if compare TQM way of looking at marketing is a simplification/bastardization, focusing only on customer satisfaction and forget everything else
  • End up with a general collection of bad management ideas, put together in a book, and not deep integration
• Some value, if you have engineers that never would study management, they'll learn something
• If your goal in life is to create science, this won't go
• Quality movement has collapsed
  • National quality awards: no one pays attention any more
  • Still have SPC, which has resulted in six sigma thinking, as SPC in more complex environment
  • Also quality metrics
  • COPQ, cost accounting
  • But nothing at the core, it's operations management at the core
• We don't want to come to the same place with service management

Question: If you have service as the central process, have to integrate?

• Can put a perspective in the middle (e.g. look at human resources from a quality perspective)
• But will it create something new, like computer science, which isn't a perspective.
• There are knowledge perspectives, network perspectives of management
• Change location, see thinks differently
• If deep integration happens, then have a new model that becomes a new department
• Important for university administrators to not run around, and focus on things that matter

Question:  In India, find these things

• Business Process Outsourcing is an application, like a washing machine

Having destroyed IHIP, tell us what to do?

• Do something practical? A violation of the mission of creating new knowledge, through science.
• Need definitions and classifications, as they're fundamental to scientific work
• Need a common understanding, so can have meaningful discussions
• Makes it possible to study alternatives:  wouldn't cut down IHIP model if it weren't define
• Models shape the real world, e.g. accounting

Example from clinical medicine:

• Have a medical condition, e.g. leukemia
• It has certain symptoms, body of knowledge
• Then diagnosis, with a treatment
• Look at results, see all over the place
• Not clear causal connection
• Similarly in management, e.g. motivational programs sometimes work and sometimes don't
• Different levels of scalability
• Psychiatry is the worst, and surgery is the best, because you know what the outcome will be

Recently, have discovered the leukemia isn't a single disease, it's seven diseases with similar symptoms

• When know this, and can classify, then could develop seven different diseases

This is why classifications are important, and need definitions that are clear and well-founded

Another example, from quality management, from Walter Shewhart

• Symptoms, use common sense
• But Shewhart suggested breaking symptoms down into common cause quality problems, and specific cause quality products
• They look the same in a defective product
• They can only be distinguished in time series analyst
• Natural defect level comes out of natural processes:  then should do nothing, because the system is running as it should, although by random processes, defects happen
• If can find time and place where this defect happens, then can take action
• Otherwise, will just mess around, and won't be successful
• Shewhart:  you can't make the distinction between common cause and specific cause by the naked eye
• Need to gather data
• It looks like magic, not obvious, a bonanza for consultants

Lillrank created a classification on Types of Science last spring, created some discussion last year

• Traditionally, have made categories based on what scientists do:  
  • (a) Basic science types who generate theory, not practical
  • (b) Applied science people, mostly concerned with solving problems, e.g. engineering, medicine, apply to some field of study
  • Think that this classification isn't good enough, doesn't explain what we do in this department
• We need to look at the governance of science:  the whole superstructure for financing, strategy, dividing roles and responsibilities, monitoring results
  • Look at bigger system on the financing of science, and recipients of science
  • (a) Things done for instrumental value:  science not for itself, used for something
  • (b) Inherent value:  science for its own sake
  • Funding is different
  • In studying Shakespeare, inherent
• Thus, four different types
• (a) Curiousity driven science
• (b) Engineering, clinical science
• (c) New: explorative science, filling defined knowledge gaps
  • This is where Tuta should be
• (d) Another new:  Experimentation for fun, playful science (liked by Nokia), e.g. tinkering with your motorbike
  • Many inventions this way, e.g. Wright Brothers
  • Can discover new things

Less from systems and their environments:  trying to make a definition, isn't always good to look just at the phenomenon by itself

• Looking at scientists today may not help
• Need to look at other layers of the system
• Then it's not just definitions, but context
• May think beyond material and immaterial systems
• Look at different types of production systems and business systems, that have differences

The institutional environment:

• The ontology of IHIP as foundational differences
• Production systems:
  • Difference between open systems and closed system
  • Closed means all of the defined inputs, certain processing and then output
    • Can't change the scheme by what's going on
    • Many companies operate this way
  • Open systems, e.g. travel and tourism system has terrorists, can't have a full and complete description before the act of production
    • Closed systems have low uncertainty, ex ante negotiated, volume low
• In a business system, have a production system, but contractual
  • Could be favour, barter, or public service
• Receiving system

e.g. consuming the services of an automobile (driving)

• Production system:  manufacturing, sales, service and repair
• Business system: car sales (new and used have different behaviours), leasing and rental
• Business system doesn't change the production system
  • Business system comes from ownership and financing
• Receiving systems: time becomes a criteria for billing, charged by number of days that you have the car
• Then, meaningful to say that car manufacturing and sales is part of the material world, and rental is services?

Comment: difference between pre-owned cars and used cars

• Pre-owned cars have all of the same warranty attributes as a new car

Comment: more and more value is not from the physical product, but from the value in use

• Act of consumption is the same

Comment: cars can be customized.  Value comes when I get in the car, and drive.

• An argument that everything is a service, eliminate the difference between the material and non-material
• It's not a different production system, it's a different business system
• Type of business configuration

Student doing R&D study:  the major difference is not the production process, it's the receiving process

• If the end is articulated well, it's different from a receiving system that is vague

Question: Is receiving system is different from the funding system?

• In the R&D example, they're the same

Based on Dalaunay and Gadrey 1987, Araujo & Srping 2006

• Production and exchange
• Could have a separate service provider
• 3-way, different from IHIP, can be split out
  • Production / exchange / consumption

Process management perspective, look at constraints:

• Time constraints:
  • Perishable?
  • Returnable?
  • Creates constrains on production system:  
    • inventory vs. capacity management; billable, revenue models
    • Objectificaiton, at point of invoicing
• Space / location constraints
• Boundary constraints
• Many of these constraints are changing, particularly with information tehcnology

If agree with the reasoning so far, then what could we practically do research on?

• Service machines:  an analogy, since we're a technical university
• In mechanical engineering, have a contract
  • Design parts, have a frame, connect them: need mechanical engineering skills
• Structure and framework aren't articulated, but these are the sorts of things that need to be studies
• This picture is the outcome of a quick and dirty exercise with colleagues in Delhi:  what sort of machine can handle 40,000 calls?
• If only look at process, the operating process is in service delivery, and is relatively simple
  • e.g. someone calling in from Iowa
• Service delivery may be simple, but the operations planning is complex

What kind of contract is made between the customer and vendor?

• Different generations
• First generation of call centers, was on capacity, e.g. buy 52 desks
  • This system doesn't work well, because the number of incoming calls is variable
  • Risk falls on the customer
• Then want to negotiate a different contract, based on the number of calls
  • Then the risk shifts the other way:  the vendor has to shift capacity, will have to pay even if there's no demand
  • From the customer's perspective, there's a risk of not answering the calls
• Third version: forecast given by a customer, in 30-minute time slots, given 2 months ahead, used as a reference point on which incoming traffic is judged
  • This becomes the basis of the revenue model
  • A sophisticated set of quality metrics
  • Metrics can then be linked to bonuses or penalties
  • Operations planning:  in all people-intensive systems, all of things are related to staffing and scheduling, can't save money unless something happens in staffing
  • Human resources: sophisticated incentive system, so people do a good job

In services engineering, the direction is not to dig more deeply into material and immaterial, but go into production systems and develop some perspectives, that allow looking at classical production systems

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