Innovation Using TRIZ and Testable Architecture for the Formulation of a Broker Appointment System

Introduction

“Two pints of London Pride please” asked the underwriter.
“Would you like to keep a tab behind the bar?” asked the bartender
The underwriter turned to the broker and enquired
“How many risks shall we be negotiating today Bruce?”
“Around 7 of them” replied the broker
“Then yes, please open a tab for me” said the underwriter to the bartender

The London Insurance Market is a unique marketplace. For over 3 centuries, the relationship between broker and underwriter has transformed the London Insurance Market into one of the most dynamic and successful financial markets in the world. Whilst, it is traditionally a face-to-face business based within the area of the “City of London”, many of the participants have managed to achieve a global presence. Thus, this exclusive marketplace has continued to influence the global markets by being a major player by virtue of its efficiency and productivity.

Brokers play a pivotal role in placing large volumes of business in hands of Insurers on a daily basis. As a result the meeting between broker and underwriter becomes fundamental to the Insurance business. Many technologies have been proposed to enhance the meetings of broker and underwriter. Nevertheless, most the value propositions focussed on the aspect of collaborative tools that enable broker to virtually meet with underwriter over the Internet. Yet brokers and underwriters are always willing to meet each other, face-to-face, following a long and successful tradition, and utterly dislike any interfering technologies. Many of the online collaborative tools have failed to deliver value to both brokers and underwriters.

Unlike these traditional solutions, our business value proposition, which is the Mobile Broker Quest, is an innovative solution which does not attempt to act as a mediator in between the broker and the underwriter, but foster a catalyst to bring broker and underwriter together in a more efficient and prompt manner by focussing on the problem of broker appointment system.

Problem Statement

Traditionally, insurance companies provision a trading floor, with an appointment system, used by the brokers to book meetings with underwriters. One of the observed limitations of the existing appointment system is that the full capability of the service is constrained by the need for the broker to be physically present in the office. The waiting time is only known when the broker logs into system. Very often, the queue tends to be very too long, resulting to brokers leaving to meet other insurers or clients. The broker may or may not come back to the underwriter. This usually leads to an unsatisfactory customer experience and loss of potential business.

There is an apparent problem in the current broker appointment system at a major insurance group. When one profiles the statistics, it reports a drop of 60% in appointment booked since 2005. Brokers are not using the system anymore, since the solution does not offer the value they need. In identifying a process which is no longer supporting the goals of the underwriting process, this paper explains how an innovative solution, Mobile Broker Quest (MBQ), has been articulated and designed by merging two robust methods together, namely, The Theory of Inventive Problem Solving (TRIZ) [Kap96] and Testable Architecture (TA) [Tal04][Yang06] into a Blended Modelling Approach for innovation.

Blended Modelling Approach

The rationale behind the blended modelling approach is primarily due to the nature of a typical innovation life cycle. The variations which exist in an innovation process require more flexibility than the constraints of the classical software development life cycle. According to Garlan and Shaw, in their analysis of advances in software engineering [Gar93] [Shaw01], there is a lack of scientific rigour within software engineering, wherein structural design alone cannot exhaustively define a software problem. Since the yield of a given research cannot be known and guaranteed upfront, the mindset of formulating, inventing and treating requirements has to shift from a deterministic to a probabilistic method to manage the variations.

There are two acceptable attitudes of modelling, namely deductive modelling and inductive modelling [Oud02]. In the problem realm of deductive modelling, a model is an a priori representation of observed phenomena from reality wherein the process is to assume the model to be true upfront and the representation often becomes a structure which can be cloned or reproduced. These structures becomes moulds and knowledge from similar phenomenon observed in one’s problem domain can be “poured into these mould” which will lead to models of the problem definition. In the realm of inductive modelling, a model is an a posteriori representation of observed phenomena from reality and we understand that the reality and/or observation may change. Designers attempt to map the observations to a formal system so that these formalisms can be tested and simulated against the observations. Should the formal system be proven to be true, then a model exists, i.e. it has been induced, otherwise there is no existence of a model at this time.

In order to manage the variations and unknowns of an innovation process model, we are required to use both the inductive and deductive modelling techniques. This potentially adds scientific rigour to remove ambiguity in requirements, resolve design defects, increasing the power of modelling. However to join both discipline requires a robust framework. Our proposed blended modelling approach merges the 2 attitudes of modelling together using a formal and mathematical framework called Testable Architecture [Tal09].

The Mind Model of the AS – IS Process

We formulated a mind model by observing and learning from the customer problem domain. The point of focus was the trading floor at a global insurance group in London. Brokers need to be physically in the trading floor in order to enter the waiting line system by interacting with a touch screen interface, provisioning his/her credentials.

Figure 1 as -is Model of Broker Waiting Line System

The waiting time is known only after the appointment is booked in the queue and when the latter is too long; the broker may leave for other business. The may be a potential loss of business as Figure 1 depicts.

Quality Modelling

As we probed the underwriters and brokers on the issue of quality, there is a clear gap between the SLAs and the capability of the as-is process. The quality model required for the appointment system is, on the one hand, underwriters want more appointments in one day and better time management for their meetings, and on the other hand, brokers, being always on the move, require the flexibility to book appointment anytime and anywhere. We employed the House of Quality [Yoji90] to model the quality attributes and refine them to measurable and controllable attributes, as depicted in Figure 2.

The House of Quality

Figure 2 House of Quality of Broker Quest SLAs

We are left with two fundamental conflicting quality attributes, the ad-hocness of broker appointment requests, and the need for better time management from underwriter. In coupling the flaws of the AS-IS model with the conflicting quality attributes, we now apply a technique called TRIZ to guide the process of inventive problem solving.

The Implementation of the Theory of Inventive Problem Solving (TRIZ)

TRIZ is interdisciplinary and closely related to logic, psychology, history of technology and philosophy of science. The two basic principles in TRIZ 1) “Somebody, someplace, has already solved your problem or one similar to it. Creativity means finding that solution and adapting it to the current problem;” and 2) “Don't accept compromises. Eliminate them”. The main concept applied by Altshuller, the inventor of TRIZ, in developing the 40 principles is that contradictions (or trade-offs) are the constraints that inventions seek to resolve. Inventive solutions do not seek equilibrium along the trade-off, but “dissolve” the contradiction. Inventions are intended to solve problems which are fundamentally “the difference between what we have and what we want” (De Bono). The problems in turn are derived from contradictions. Any invention is therefore intended to “resolve” or “dissolve” these contradiction. From these premises Altshuller developed the 40 principles and the “Matrix of Contradictions”, see Figure 3.

Figure 3 The TRIZ Matrix of Contradictions

Within the problem domain of the Broker appointment system, we started by identifying the two core contradictions in the Broker and Underwriter relationship: 1) there is the need for the ad- hoc style of meeting from the brokers which is natural to their operation and they need the ease of operation to run their daily business while 2) there is the need for a better time management method from the underwriters to reduce the loss of time of an inefficient and uneconomical waiting line. In applying the TRIZ matrix, the following principles to solve these contradictions: Improving <25> Loss of Time without damaging <33> Ease of operation. As we traverse the matrix, Figure 3, we discover four principles which are defined as follows:

<4>Asymmetry means to change the shape of an object from symmetrical to asymmetrical and if an object is asymmetrical, then increase its degree of asymmetry.

<28> Mechanics substitution means to change from a static field to movable fields, i.e. to add another Sense to the solution.

<10> Preliminary action or Prior Action means to pre-arrange objects such that they come into action from the most convenient place and without losing time for their delivery.

<34> Discarding and recovering means to apply solution into the flexibility of transactions.
As observed, TRIZ does not provide the breakthrough idea, but spells out the principles to guide designers / innovators in catalysing the process of idea generation, i.e. the seed idea.

The Seed Idea

In translating the principles as prescribed by TRIZ, designed natively for the manufacturing domain, into the problematic of business process and software enablement, the following principles guides us to produce the seed idea. The latter comes from a mixture of understanding the pain points, potential enhancements of business processes creativity and the dissolution of contradictions. There is no process for creativity but there are indicators that can help to build an environment that fosters and directs creativity. The translation process harvested the following:

<4>Asymmetry means to change the symmetricity of the transactions into an asymmetric model, which indicate that the underwriter side of the appointment process has to be asymmetric to the broker side of the appointment process. This is an essential guiding principle as traditionally in software engineering, one tends to design solution that are structurally similar throughout the solution to improve manageability and reuse of solution components.

<28> Mechanics substitution means to change from static field to movable fields. The principle indicates the addition of another sense or channel to resolve the contradictions. The aspect of movable fields can be linked to the aspect of location and mobility, to the problem, that is the substitution of a static location for a dynamic one. In adding the aspect of mobility to the appointment system, leads us to look at the very common Mobile Technology.

<10> Preliminary action means to gather and prepare all the relevant documents required for underwriting the risk in advance and allowing the system to place them in the order required during the course of the meeting. These documents can also be pre provisioned with all known information such as date, broker details, underwriter details etc. which saves time during the meeting. This given principle reduces the duration of a meeting, hence creating more space in the waiting line to accommodate the ad-hocness of broker’s requests. Incorporating such feature dissolves the contradictions of time management i.e. reduce loss of time and ease of operation.

<34> Discarding and recovering means that the broker appointment system should be thin and flexible to use with fewer click and screens to complete a booking transaction. The principle also indicates that the underwriter should also provide the flexibility to delegate a meeting request amongst his/her peers. Hence, the seed idea can be formulated around the method and technology of 1) Mobile Technology; 2) Pre-provisioned, Positioned and Attach relevant documentation within appointment request, 3) Flexibility in changing appointment variables, e.g. Time and delegation of appointment amongst underwriters and 4)User Friendly Interface.

Requirement Invention

The seed idea is evaluated and rationalized in order to invent the user requirements of the solution. The process of translating the seed idea into requirement consist of fact-finding, identifying constraints as well as expanding information. This involves the analysis of the as-is model (see Figure 1) to understand the problem by delineating and refining constraints. Classically, in the problematic of software engineering, requirements are classified into two classes which are functional and non functional requirement [Boeh76]. However, it has been argued that user requirements have to be classified into their distinct styles which are more profound than the conventional two classes. The process of classification will provide the directives to which type of modelling tools, including inductive modelling tools, should be employed to the different styles of requirement. This is typically to address the approach of blended modelling which is supported by Testable Architecture. Typically, there are four types of the requirement styles which are 1) the data style, 2) the functional and logical style, 3) the communication and behavioural style and 4) the quality styles.

Table 1 Understanding the character of requirement

The TO – BE MODEL of the Mobile Broker Quest

We have formulated a series of high level requirements to how the Mobile Broker Quest will be operated leading to the design of the TO-BE process model (see Figure 4 ) designed to enhance the appointment system in conforming to the quality model or SLAs in Figure 2.

Figure 4 To- Be model of the Mobile Broker Quest

The to-be process model is in its static form, and we can experience some optimization feature in the reduction of the number of clicks required from the broker to provision the system when compared to the as-is model. Yet, in order to profoundly understand if the proposed model conforms to the SLAs and to maximise the probability of containing design defects, the static model has to be translated into a dynamic and formal model and this leads to the application of Testable Architecture.

The Application of Testable Architecture

A dynamic model is based on formal methods, subsequently enabling designer to type check and simulate the proposed model against the refined requirements and the quality model. This part of the modelling discipline is inductive and primarily it allows design and requirement defects to be found and fixed prior to coding. Testable Architecture (TA) is the core engine of the innovation process model and key to the success of the proposed idea, i.e. the Mobile Broker Quest. TA is a methodology that abstracts the complexity of formal methods, pi calculus [Miln80a] [Miln80b] [Miln93], Petri nets [Pet62] and Z Notation to provide a “run time simulation engine” and type check compiler to dynamic models. It fundamentally has the capability of blending structural modelling with inductive modelling, acting as a compiler to design and models. As we journey through the process of building a dynamic representation of the requirement that describe the phenomenon of two participants booking appointments, we are able to exercise the dynamic model to verify and validate against two key question: 1) Is the model representing the right thing?; and 2) Is the model representing the thing right?

In Figure 6, we illustrated the Coloured Petri Net Model of the to-be process highlighting the dynamics of the waiting line for Brokers. We exploited the simulation engine of CPN to assess the model against the quality attributes (see Figure 2) and constraints, to validate if the proposed model conforms to the business values and goals.Coloured Petri Net [Jeff91] is a modelling technique to model parallel behaviour and high-level programming languages to define data, functions, and computation on data. The process model is represented by token exchange between different parts of the Petri Net wherein places are connected to transitions via arcs. Tokens are inserted or removed from places, which carry, as a timestamp, the deterministic or randomly distributed temporal length of the transition they enable.

Figure 5 The Meta Model of the Mechanics of CPN

Formal and Dynamic Modelling

In Figure 6, we illustrate a Petri Net model of a waiting line component of the Broker appointment systems. We employ Petri Net to model the queue wherein simulation processes are performed to understand how the queues work under different condition, e.g. an increase in appointment request and continuously check against conformance. In order to emulate the dynamics of the waiting we use some of the historical data of the existing broker quest system and couple the statistics with some probabilistic model. Based on the empirical research of the queuing theory, we assigned the following distribution behind the CPN model for simulation 1) the arrival rate follows a Poisson distribution, 2) the buffering rate of the queue follows a normal distribution and the processing time of servers follows an Exponential distribution and the waiting line follows a FIFO structure.

Figure 6 CPN Model of the Waiting Line

Observations

Consider Figure 7, where given a burst of 1 appointment request per 10 minutes to 1 appointment request per 3 minutes, the graph shows the gap between the input rate against the output rate. It justifies that the close proximity between the input and output rate shows that the service rate of the Queue System is adequately lower than the input rate. The two graphs differentiate the latency added to the queue system.

Figure 7 Waiting Line Dynamics of Mobile Broker Quest

In Figure 8, the graph reports on the time taken for a large sample of appointment requests to leave the system. The objective is to estimate the number of brokers waiting for more than n minutes (where n is defined by the SLA) that exist given an input burst. The graph shows the period of time a number of brokers take to meet an underwriter, e.g. over hundred appointment requests, a broker take 12 minutes. Hence using such analysis, a threshold can be established to identify those brokers that have a probability of waiting for too long.

Figure 8 Waiting Time of Broker

In order to reduce the number of variables in the experiments, we employed the Taguchi Method of Design of Experiment (DoE), used to determine the relationship between the different factors (Xs) affecting a process and the output of that process (Y). In the defined quality model we are seeking the fundamental SLAs of the MBQ which is to increase the number of appointments in a day to increase revenue in new business. So the function exercised into DoE is as follows:

DoE establishes the most important Xs, of the function to reduce the number of simulations against the SLAs.The iterative process of simulating the dynamic model (see Figure 9) leads to the reinforcement and refinement of the requirements and containment of design defects [Boeh76]. The refined requirements are validated and transformed into formal specifications that are given to the designer of the solution architect.The iterative process of simulating the dynamic model (see Figure 9) leads to the reinforcement and refinement of the requirements and containment of design defects [Boeh76]. The refined requirements are validated and transformed into formal specifications that are given to the designer of the solution architect.

Figure 9 Iterative Refinement of Requirement

The Solution Architecture

The architecture lays the foundation for analytical optimization of function, cost, quality and performance by gaining understanding of: 1) how the system and the system elements function ideally; 2) understanding of the interfaces and their interactions and 3) the understanding of behaviours influenced by the interactions as formalized by Testable Architecture. The process of modelling the latter can only be formally understood by exercising Testable Architecture.

As the solution architecture is formulated, the continuity of the innovation life cycle follows the path of the classical Software Development Life Cycle for coding and testing which ideally fits into the constraints of the Spiral Model.

Conclusion

MBQ yields to an improved time management strategy, increasing the number of brokers an underwriter can meet. It fundamentally addresses the problem of customer intimacy and customer satisfaction as broker may plan their day in advance. The capability of MBQ enables the insurer to maximise the probability of winning of potential business by reducing the number of broker walkouts. In the journey towards the MBQ, we have demonstrated the application of TRIZ to successfully provide the principles required to enhance the process of seed idea generation. Those principles force us to think laterally which ensures that key attributes of a solution are not missed as they are very often during solution envisioning exercise. We also observed that Innovation endeavours carry several facets of the unknowns and variations that require inductive models to test their viability at the early stages of requirements. Hence, we proposed a blended modelling approach, founded on the discipline of Testable Architecture to apply simulation and validation to the proposed model of the broker appointment system.

Reference

[Boeh76] Boehm B W, “Software Engineering”, IEEE Trans. Computers, pp. 1,226 - 1,241, December 1976

[Gar93] Garlan D, Shaw M, “An Introduction to Software Architecture, in Advances in Software Engineering and Knowledge Engineering” Vol 1, ed. Ambriola and Tortora World scientific Publishing Co., 1993

[Jeff91] Jeffrey J M, “Using Petri nets to introduce operating system concepts”, Paper presented at the SIGCSE Technical Symposium on Computer Science Education, San Antonio, USA, 7-8 March 1991

[Kap96] Kaplan S, “An Introduction to TRIZ – The Russian Theory of Invention Problem Solving”, Ideation Intl Inc, 1996

[Miln80a] Milner R, “A Calculus of Communicating Systems”, Lecture Notes in Computer Science, volume 92, Springer-Verlag, 1980

[Miln80b] Milner R, “A Calculus of Communicating Systems”, Lecture Notes in Computer Science, volume 92, Springer-Verlag, 1980

[Miln93] Milner R, “The Polyadic pi-Calculus: A Tutorial”, L. Hamer, W. Brauer and H. Schwichtenberg, editors, Logic and Algebra of Specification, Springer-Verlag, 1993

[Oud02] Oudrhiri R, “Une approche de l’évolution des systèmes,- application aux systèmes d’information”, ed.Vuibert, 2002

[Pet62] Petri C A, "Kommunikation mit Automaten", PhD thesis, Institut f¨ur instrumentelle Mathematik, Bonn, 1962

[Shaw01] Shaw M, “The coming-of-age of software architecture research”, in Proceedings of ICSE, pp. 656–664, Carnegie Mellon University, 2001

[Tal04] Ross–Talbot S, “Web Service Choreography and Process Algebra”, W3C Consortium, 2004

[Tal09] Ross-Talbot S, “Savara - from Art to Engineering: It’s all in the description”, University of Leicester, Computer Science Seminar, 2009

[Yang06] Yang H et al, “Type Checking Choreography Description Language”, Lecture Notes in Computer Science Springer-Berlin / Heidelberg, Peking University, 2006

[Yoji90] Akao Y, “Quality Function Deployment: Integrating Customer Requirements into Product Design” (Translated by Glenn H. Mazur), Productivity Press, 1990