Open Agile Architecture™

In 1921, Frank and Lillian Gilbreth made a presentation at the annual meeting of the American Society of Mechanical Engineers. This was entitled Process Charts: First Steps in Finding the One Best Way to Do Work. The Gilbreths introduced their notation to visualize processes in order to improve them.

The main objective is to help people in the organization discover the one best way to do work. Noting that every detail of the process can be affected by other details, they argue that it is necessary to visually represent the process as a whole before changing parts of it.

They point out that a local change that does not take into consideration all upstream and downstream operations could pose operational problems during implementation. It is interesting to note that three concepts still relevant today were formalized so early:

The Gilbreths formalized a three-step process improvement method [Gilbreth 1921]:

To justify their method, the Gilbreths refer to the calamitous losses that result from wastage due to useless movements and gestures. They observe that work-related fatigue is one of the greatest wastes.

Deming, Juran, and Ishikawa [Ishikawa 1985], three key thinkers in total quality, argue that knowing what customers want is essential to providing quality products or services. Explicitly identifying and measuring customer requirements is the starting point of a quality approach. Ishikawa points out that collecting and analyzing customer requirements is a lever in developing cross-functional cooperation.

Customer requirements help focus on those parts of the process that have the greatest impact on customer satisfaction. The quality of products and services depends mainly on the quality of the processes used to design and produce them. Management must therefore train and coach employees in the evaluation, analysis, and improvement of processes.

In a book published in 1991 [Harrington 1991], James Harrington defines the efficiency of the process as its ability to meet customer requirements. For the author, there is no product or service without process and, reciprocally, there is no process without product or service. Customer requirements are a necessary input, but are not sufficient. James Harrington’s definition of value has three shortcomings:

This document promotes in Section 4.2.3 a value-driven approach that borrows from several disciplines, including Value Engineering. It is aligned with Lean Enterprise, which defines value as [Murman 2002]:

Value stream mapping is driven by a definition of value that includes all stakeholders.

Toyota uses a visual representation of production processes; Material and Information Flow Diagrams (MIFDs), the “ancestor” of value stream mapping.

In 1995, James Martin defined a value stream as “the end-to-end set of activities that delivers particular results for a given customer (external or internal)” [Martin 1995].

In their book, Lean Thinking [Womack 1996], Jim Womack and Dan Jones use the term value stream. Value stream mapping as a term became popular when Mike Rother and John Shook published Learning to See [Rother 2003]. The term value stream mapping has since been borrowed by other disciplines, including Agile, DevOps, and Enterprise Architecture. The focus on applying value stream mapping as a tool without understanding the whole Lean system at best does not create value; at worst, it creates waste.

"Value stream mapping is not a solution to a problem, but a tool to help us see." (Jeffrey K. Liker, 2017, The Toyota Way to Service Excellence)

To illustrate value stream mapping, lets consider the example of an individual in need of banking services, and map an end-to-end value stream, for client on boarding; see Figure 36.

One of the first value stream architecture decisions is to determine at which level of granularity client on-boarding should be mapped.

Mapping a generic value stream that works for all on-boarding cases would impose the most complex design onto simple cases, resulting in:

Who has not been frustrated when a service representative argues that the system or procedure itself prevented them from solving your problem?

On the other hand, mapping a specialized value stream for each client segment, product, or channel is likely to result in many almost identical value stream maps.

An example of classical granularity analysis is illustrated in Figure 37.

Products which have comparable production steps and use similar equipment are regrouped into product families:

A value stream map is created for each product family.

In a digital world, it is important to include additional factors to determine the appropriate granularity level of value stream maps. Table 3 illustrates the granularity analysis that helped define the client on-boarding value streams.

The scope of the first value stream covers simple client on-boarding. The second value stream addresses medium complex on-boarding situations. The third value stream addresses cases that require more expertise; for example, to set up an account for a protected adult or a foreign diplomat.

Understanding current conditions is about collecting facts. It can be done through research techniques such as client and employee interviews, direct observation, “mystery customers”, or workshops, to name a few.

The cross-functional improvement team has learned:

Value stream measures are displayed at the bottom of Figure 40:

The diagram shows that the total P/T is 105 minutes. P/T during the customer meeting is 90 minutes, the sum of time from “Discover Prospect” to “Activate Contract”. Lead Time (L/T) is the sum of P/T plus D/T from the client intent to the delivery of the means of payment. L/T can reach nine days. This is mostly due to:

Other measures can be collected; for example:

The equation VA < P/T < L/T is usually verified.

Capable adults represent 69% of all new client on-boarding. 60% of these are capable and resident adults who need a checking account and a credit card. 26% of them are looking for a mortgage loan. The ideal “clean-slate” vision focuses first on creating a streamlined and enjoyable on-boarding experience for the simple on-boarding value stream.

The ambition is to:

The ideal scenario is described below:

Waste is anything other than the minimum amount of resources that add value to a product, including the information customers need. Waste is also referred to as “Muda”. Waste can be classified as Type 1 or Type 2 Muda:

VA time can be measured. It is P/T that produces value. The efficiency of a value stream or process is calculated by dividing the VA time by the L/T.

Taiichi Ohno and Norman Bodek [Ohno 1988] have identified seven types of waste:

Other waste typologies have been developed; for example, Allen Ward and Durward Sobek identify three primary categories of knowledge waste [Ward 2014]:

A word of caution – do not go out and look for waste guided only by some waste taxonomy; doing so would be wasteful. Instead, be guided by value as defined by the client and keep in mind the purpose of the system. Identify what specifically prevents the value stream from reaching its ideal state. Waste typologies are just an aid to identify symptoms that are likely to reveal deeper problems.

Muda points to underlying problems at the value stream level or at the process level. Analyzing what causes waste helps identify countermeasures that have the potential to improve the overall system performance. Value stream mapping is misused when it becomes a cost-cutting tool (attacking symptoms), rather than solving the underlying problems that cause waste.

This is illustrated with the “collect and control documents” process. The main problems are:

Discovering why so many client files are not meeting compliance requirements is a prerequisite to improving the value stream. Figure 41 lists a set of first-level causes.

The “five whys” technique can be used to analyze the root causes of problems; for example:

Two usual generic causes of waste (Muda) are unevenness or variability (“Mura”) and overburden (“Muri”).

Unevenness causes people or machines to be either overburdened at times of peak activity or underutilized in times of low activity. This is illustrated using on-boarding appointments.

The ideal “clean-slate” vision states: “Open an account and get a credit card without an appointment”. This is possible if an advisor has enough time to meet the prospect soon after they arrive.

The available work time per day in a branch depends on the number of advisors available that day and the percentage of their time that can be allocated to on-boarding appointments.

Using the hypothesis that a branch has three advisors who can allocate five hours of their time to on-boarding appointments, the total available work time is 15 hours or 900 minutes a day.

If 10 clients show up that day, the target rate for “producing” on-boarding meetings is 900 divided by 10, which is equal to 90 minutes. This is the takt time, which measures how often the branch should produce on-boarding appointments.

The takt time measures how often you should produce one part or product, based on the rate of demand, to meet customer requirements. In our example, it measures how often you should have customer on-boarding meetings. Takt time is a reference number that gives you a sense of the rate at which a process should be producing.

Using the hypothesis that the new P/T for handling an appointment is reduced to 60 minutes, everything being equal, the branch is in overcapacity to meet customer demand: the branch can conduct on-boarding meetings every 60 minutes while customer demand only requires it to conduct meetings every 90 minutes.

Making a new hypothesis, that 20 clients a day request to meet an advisor, the new takt time to meet customer demand would be:

In this hypothesis, the branch does not have enough capacity to meet customer demand. In order to meet demand, advisors would have to reduce the on-boarding duration from 60 minutes to 45 minutes. This would obviously over-burden advisors who would make more mistakes and deliver a poor customer experience.

In a service context, customer demand often varies intraday. Making the hypothesis that 10 clients show up during lunch time, the three advisors could not satisfy demand. Therefore, in a service context, computing takt time may not be sufficient to manage demand variation (Mura) and avoid overburden (Muri).

The first challenge is to sort prospects so that only capable resident adults who own or rent their housing go through the simple on-boarding value stream. The intention is to rapidly streamline the simple value stream because it is a “low hanging fruit” that can improve the experience of the biggest customer segment. From a marketing perspective, the more prospects that the branch can on-board on the spot, the better. When an appointment is handled later, the bank may lose a customer to competition.

The second challenge is to have enough capacity to meet demand. Because there is not much flexibility in adding new advisors to a branch on the spot, the team is considering two countermeasures:

Section 16.3.4 develops an approach that could help the bank to find new ways of solving the problem. This is illustrated with the second improvement increment.

The on-boarding interview starts with 15 minutes of “quality time” that provides an opportunity to listen to the prospect and understand their most pressing needs. This value stream caters to clients who are likely to have straightforward needs. The advisor asks simple questions to discover which credit card best meets the prospect’s needs. This simplification reduces the “offer and choose products” P/T from 30 minutes to 10 minutes.

Because the market segment served by this value stream is composed of capable adults who are residents and own or rent their housing, the list of required documents is simple enough to be remembered by advisors. Most scan quality issues are due to improper manipulation. Therefore, an information campaign can dramatically reduce scan defects.

The bank can also use Software as a Service (SaaS) services to verify in real time the authenticity of identification and supporting documents. The combination of these countermeasures can help reduce the P/T from 35 minutes to 15 minutes. Because the right documents are requested and their authenticity verified on the spot, rework is almost eliminated.

In this improvement increment, advisors will not get tablets because it would take too much time to implement and the up-front investment would be difficult to justify.

Capacity management matches capacity and demand. The first step is to determine demand patterns and assess the causes of demand variations, as Figure 42 illustrates. It represents the call distribution of the bank’s call center. Call distribution often needs to be analyzed at a lower level of granularity, such as by type of call. For example, credit card calls are likely to have a different distribution pattern than retail brokerage calls.

The call distribution pattern may vary during the week; for example, people who got their credit card declined during the weekend are likely to call the bank on Monday morning. The call distribution pattern may be impacted by market events; for example, a severe decline of indices is likely to increase the number of brokerage calls.

The second step is to identify, experiment, and implement countermeasures that influence demand and/or capacity.

Influencing Demand

Examples of countermeasures that lower demand:

Examples of countermeasures that increase demand:

Managing Capacity

The first countermeasure is to organize the employee work schedule to match demand. However, there are limits due to labor laws and employee well-being.

Examples of countermeasures the enterprise can take are:

When capacity matches demand, it improves the quality of customer experience. It removes any defects caused by overburden and improves employee experience. It prevents paying for underutilized resources and people.

This document will now illustrate how additional countermeasures could further improve the value stream.

Unified communication tools such as Slack™, Skype, or Microsoft Teams^® allow remote working. With this technology, when all advisors in a branch are busy, an advisor located in another branch can take care of a “walk in” prospect remotely. The mutualization of resources across several branches helps absorb activity peaks.

Implementing this target condition requires the bank to:

The feasibility study of this new target condition is conducted by cross-functional teams composed of representatives of the sales, marketing, HR, IT, or real estate functions. Implementation is incremental. It starts with a few pilot studies conducted in different regions. The rollout is progressive and leverages a central change management team.

This new way of working can support new use-cases; for example, leveraging the scarce resources that provide investment advice or instruct complex mortgage loans. The next step is to support a true omnichannel model with clients and employees interacting over the Internet. This creates new cybersecurity challenges that the bank will have to address.

Giving new clients their credit card at the end of the on-boarding appointment would be a differentiating capability. Two scenarios are explored:

The credit card printer solution requires a major investment in hardware and system integration work. The micro process shown in Figure 43 models the tasks to be implemented. If the required APIs exist, or are easy to implement, integration work is simpler. It will also make running an experiment easier. The bank will conduct a few pilot studies before deciding whether or not to commit to funding the investment.

The bank must also factor-in the logistics implication of putting a printer in each branch. Printer maintenance and consumable renewals have a cost and must be organized.

The alternative “low tech” solution is to provide a set of preprinted credit cards to branches. These cards would not have the client name printed on them. This would give clients access to a simple and standard means of payment. They would receive their final credit card later.

This next target condition reduces the “deliver means of payment” P/T from 10 minutes to 5 minutes. The big gain is the dramatic reduction of D/T from 5 days to 0 minutes.

Different organizational models can support effective value stream management. Organizational models need to balance product focus with functional expertise. When the functional dimension weakens, knowledge management and career development suffer.

The value stream manager role, which is comparable to the classical process owner role, can be implemented with minimum impact on the existing organization.

At the other end of the spectrum, the Toyota chief engineer model defines a role that combines entrepreneurship with good system architecture skills. A chief engineer should be capable of:

In the middle of the spectrum, Agile teams are mostly stream-aligned and cross-functional. They regroup the roles and skills needed to develop products. They are led by product owners.