The Cost Center Trap

In the 1960’s, IT was largely an in-house back-office function focused on process automation and cost reduction. Today, IT plays a significant strategic and revenue role in most companies, and is deeply integrated with business functions. By 2010, over 50% of firms’ capital spending was going to IT, up from 10-15% in the 1960’s.[1] But one thing hasn't changed since the 1960’s: IT has always been considered a cost center. You are probably thinking "Why does this matter?" Trust me, cost center accounting can be a big trap.

Back in the mid 1980’s Just-in-Time (JIT) was gaining traction in manufacturing companies. JIT always drove inventories down sharply, giving companies a much faster response time when demand changed. However, accounting systems count inventory as an asset, so and any significant reduction in inventory had a negative impact on the balance sheet. Balance sheet metrics made their way into senior management metrics, so successful JIT efforts tended to make senior managers look bad. Often senior management metrics made their way down into the metrics of manufacturing organizations, and when they did, efforts to reduce inventory were half-hearted at best. A generation of accountants had to retire before serious inventory reduction was widely accepted as a good thing.[2] 

Returning to the present, being a cost center means that IT performance is judged – from an accounting perspective – solely on cost management. Frequently these accounting metrics make their way into the performance metrics of senior managers, while contributions to business performance tend to be deemphasized or absent. As the metrics of senior managers make their way down through the organization, a culture of cost control develops, with scant attention paid to improving overall business performance. Help in delivering business results is appreciated, of course, but rarely is it rewarded, and rarer still is the cost center that voluntarily accepts responsibility for business results.

Now let’s add an Agile transformation to this cost center culture. Let’s assume that the transformation is supposed to bring benefits such as faster time to market, more relevant products, better customer experiences. And let’s assume that the cost center metrics do not change, or if they do change, process metrics such as number of agile teams and speed of deployment are added. I’ll wager that very few of those agile teams are likely to focus on improving overall business performance. The incentives send a clear message: business performance is not the responsibility of a cost center.

Being in a cost center can be demoralizing. You aren’t on the A team that brings in revenue, you’re on the B team that consumes resources. No matter how well the business performs, you’ll never get credit. Your budget is unlikely to increase when times are good, but when times are tight, it will be the first to be cut. Should you have a good idea, it had better not cost anything, because you can’t spend money to make money. If you think that a bigger monitor would make you more efficient, good luck making your case. Yet if your colleagues in trading suggest larger monitors will help them generate more revenue, the big screens will show up in a flash.[3]

Let’s face it, unless there are mitigating circumstances, IT departments that started out as cost centers are going to remain cost centers even when the company attempts a digital transformation. What kind of mitigating circumstances might help IT escape the cost center trap?

1. There is serious competition from startups.
   Startups develop their software in profit centers; they haven’t learned about cost centers yet. And in a competitive battle, a profit center will beat a cost center every time.
2. IT is recognized as a strategic business driver.
   You would think that a digital transformation would be undertaken only after a company has come to realize the strategic value of digital technology, but this is not the case. IT has been treated as if it were an outside contractor for so long that it is difficult for company leaders to think of IT as a strategic business driver, integral to the company's success going forward.
   
3. A serious IT failure has had a huge impact on business results.
   When it becomes clear exactly how dependent a profit center is on a so-called cost center, people in the profit center are often motivated to share their pain with IT. Smart IT departments will use this opportunity to share the gain also.

Many people in the Agile movement preach that teams should have responsibility for the outcomes they produce and the impact of those outcomes. But responsibility starts at the top and is passed down to teams. When IT is managed as a cost center with cost objectives passed down through the hierarchy, it is almost impossible for team members from IT to assume responsibility for the business outcomes of their work. When IT metrics focus on cost control, digital transformations tend to stall.

Every ‘full stack team’ working on a digital problem should have ‘full stack responsibility’ for results, and that responsibility should percolate up to the highest managers of every person on the team.  Business results, not cost, should receive the focused attention of every member of the team, and every incentive that matters should be aimed at reinforcing this focus.

The Capitalization Dilemma

Let’s return to the surprising assertion that in 2010, over 50% of firms’ capital spending was going to IT.[1] One has to wonder what was being capitalized. Yes, there were plenty of big data centers that were no doubt capitalized, since the movement to the cloud was just beginning. But in addition to that, a whole lot of spending on software development was also being capitalized. And herein lies the seeds of another undue influence of accounting policies over IT practices.

Software development projects are normally capitalized until they are “done” – that is they reach "final operating capability" and are turned over to production and maintenance.[1] But when an organization adopts continuous delivery practices, the concept of final operating capability – not to mention maintenance – disappears. This creates a big dilemma because it's no longer clear when, or even if, software development should be capitalized. Moving expenditures from capitalized to expensed not only changes whose budget the money comes from, it can have tax consequences as well. And what happens when all that capitalized software (which, by the way, is an asset) vanishes? Just as in the days when JIT was young, continuous delivery has introduced a paradigm shift that messes up the balance sheet.

But the balance sheet problem is not the only issue; depreciation of capitalized software can wreck havoc as well. In manufacturing, the depreciation of a piece of process equipment is charged against the unit cost of products made on that equipment. The more products that are made on the equipment, the less cost each product has to bear. So there is strong incentive to keep machines running, flooding the plant with inventory that is not currently needed. In a similar manner, the depreciation of software makes it almost impossible to ignore its sunk cost, which often drives sub-optimal usage, maintenance and replacement decisions.

Capitalization of development creates a hidden bias toward large projects over incremental delivery, making it difficult to look favorably upon agile practices. Hopefully we don't have to wait for another generation of accountants to retire before delivering software rapidly, in small increments, is considered a good thing.

To summarize, the cost center trap and the capitalization dilemma both create a chain reaction:

1. Accounting drives metrics.
   ⇩
2. Metrics drive culture.
   ⇩
3. Culture eats process for lunch.

The best way to avoid this is to break the chain at the top – in step 1. Stop letting accounting drive metrics. Alternatively, if accounting metrics persist at the senior management level, then break the chain at step 2 – do not pass accounting metrics down the reporting chain; do not let them drive culture. When teams focus on improving the performance of the overall business, accounting metrics should move in the right direction on their own; if they don't then clearly something is wrong with the accounting metrics.

Beware of Proxies

This year Jeff Bezos's annual letter to Amazon shareholders[4] listed four essentials that help big companies preserve the vitality of a startup: customer obsession, a skeptical view of proxies, the eager adoption of external trends, and high-velocity decision making. These seem pretty clear, except maybe the second one: a skeptical view of proxies. Just what are proxies? Bezos explains:

“A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp.”

“Another example: market research and customer surveys can become proxies for customers – something that’s especially dangerous when you’re inventing and designing products.”

Here are some common proxies we find in software development:

Accounting metrics are proxies, and not very good ones at that, because they encourage local sub-optimization. 

Project metrics – cost, schedule, and scope – are proxies. Worse, these proxies are rarely validated against actual outcomes.  

“The Business” is a proxy for customers. Generally speaking, so is the product owner.

Proxies should be resisted, Bezos argues, if you want a vibrant startup culture in your company. But without proxies, how do you manage the dynamic and increasingly important IT organization? You make a habit of measuring what really matters - skip the proxies and focus on outcomes and impact.

In his excellent book, “a Seat at the Table,”[5]  Mark Schwartz proposes that IT governance and oversight should begin with strategic business objectives and produce investment themes that accomplish these objectives. IT leaders fund teams to produce desirable outcomes that will have impact on the strategic objectives. Note that these outcomes are not proxies, they are real, measurable progress toward the strategic objective. Regular reviews of teams’ progress -- quantified by these measurable outcomes -- provides leaders with insight, flexibility and an appropriate level of control. At the same time, detailed decisions are made by the people closest to customers after careful investigation, experimentation and learning.

Schwartz concludes: "this approach can focus IT planning, reduce risk, eliminate waste, and provide a supportive environment for teams engaged in creating value."[5] What's not to like?

______________________
Footnotes:

[1] From “What is Digital Intelligence” by Sunil Mithas and F. Warren McFarlan, IEEE Computing Edge, November 2017. Pg.9.

[2] The 1962 book “The Structure of Scientific Revolutions” by Thomas Kuhn discussed how significant paradigm shifts in science do not take hold until a generation of scientists brought up with the old paradigm finally retire.

[3] Thanks to Nick Larsen. Does Your Employer See Software Development as a Cost Center or a Profit Center?

[4] Jeff Bezos - Letter to Shareholders - April 12, 2017

[5] "A Seat at the Table" by Mark Schwartz

How Does Toyota Do it? (Book Review)

When Matthew May was asked to help translate the Toyota Production System for the knowledge worker, he thought: “Huh? It doesn’t make any sense. Everyone knows factory work isn’t creative, right?”

How wrong he was. He found that factory workers were more engaged and creative than their corporate counterparts. Their jobs weren’t creative, their job were to be creative. He found that the Toyota organization implements a million new ideas a year – three thousand ideas a day. May notes that these new ideas are the real reason Toyota makes over twice as much money as any other carmaker, with under 15% of the market. These thousands upon thousands of implemented ideas are the engine of Toyota’s innovation.

Who needs another book on innovation? May asks in the introduction to his book, The Elegant Solution: Toyota’s Formula for Mastering Innovation. You do. This book is a great read – short lessons, lots and lots of case studies and examples both in the text and in the sidebars liberally sprinkled throughout the book. This is a book unlike any other on innovation; it is the result of May’s deep thinking and experience in trying to identify for Toyota University what innovation in Toyota actually means.

The book outlines three principles and ten practices for driving innovation in knowledge work, and concludes with a couple of brief chapters on how to get started down this path.

Principles:

1. The Art of Ingenuity. The pressure to innovate falls on the individual – every single individual in the company. First, ingenuity means connecting with your work, whatever it is, understanding why it is important, and making sure that it is a good fit with your skills and interests. If it isn’t create a job that is. Second, ingenuity means constantly experimenting to figure out how to do that job better. Third and most important, everybody is expected to use their ingenuity – everybody – all of the time.
2. The Pursuit of Perfection.  There is no such thing as perfection – but aspiring to achieve perfection is nevertheless the goal. A good example of this principle can be found in the Fast Company article No Satisfaction at Toyota, an article about constant innovation at the Georgetown Kentucky factory. Read the article, it does a fantastic job at explaining the pursuit of perfection.
3. The Rhythm of Fit. Innovation is always obvious – after the fact. But discovering the obvious is not such an easy task. It requires that you are grounded in today, yet have a clear vision of tomorrow. It requires systems thinking, rather than program thinking. And it requires the right social context – one that inspires rather than suppresses creativity.

Practices:

1. Let Learning Lead. Real learning is not about books or lectures or workshops. It is about constantly trying things and finding out what works. Learning involves asking the right questions much more than finding the right answers. Learning means moving the Scientific Method from PhD programs in Universities to the shop floor and the knowledge worker. Learning comes first.
2. Learn to See. Walk in the shoes of the front line worker. Live the life of the customer. Data is important, but it’s interpretation that converts data into information. Learn to walk around and observe what is really happening. Watch your customer, become your customer, involve your customer.
3. Design for Today. As good as Toyota is at anticipating the future, their innovations are always grounded in clear and present needs – demographic shifts, energy shortages, safety concerns. The idea is, as hockey great Wayne Gretzky once said: “Skate to where the puck is going to be, not where it has been.” Market leaders don’t so much create markets as they understand where the market is going to go, and skate there.
4. Think in Pictures.

5. Capture the Intangible. The most compelling solutions are often perceptual and emotional.” May says. For example, when the Lexus team was designing the car, the entire team spend three months living in luxury in southern California, just to feel what their customers felt. They learned that luxury cars were not transportation, they were a safe sanctuary and quiet escape. Provide an ‘experience’ instead of a ‘product’.
6. Leverage the Limits. This chapter centers on a wonderful story about Toyota’s North American Parts Operations (NAPO) stretch goals. In the year 2000, Jane Beseda, newly appointed vice president and general manager, challenged NAPO with ten audacious and mutually competing goals, including inventory reduction, response time reduction, and waste reduction. The goals were such that they could only be achieved by departments working together across what had been organizational boundaries, and the results were nothing short of amazing.
7. Master the Tension. A McKinsey study on global productivity in the late 1990’s found that the companies which improved productivity the most were invariably companies in highly competitive industries. These companies had to find a better way of doing things – it was a matter of survival – but could not afford to throw money at the problem. We agree with May – constraints coupled with challenge provide the breeding ground for innovation.
8. Run the Numbers.There is a place for instinct, but temper it with facts. Think hard about what the important numbers are – and they are often not the obvious ones. With insight into what numbers are important, capturing and analyzing data will confirm instinct (or not) and uncover patterns that are otherwise invisible. 
9. Make Kaizen Mandatory. Standards exist to be challenged and changed. They are the current best-known way of doing things, and they are documented and followed by everyone. But they objective is to change the standard, to keep on improving the way things are done. Taiichi Ohno once said “Something is wrong if workers do not look around each day, find things that are tedious or boring, and then rewrite the procedures. Even last month’s manual should be out of date.”
10. Keep it Lean. Scale it back, keep it simple, make it flawless, let it flow. May notes “We are hardwired to hunt, gather, and horde. To think more. So lean runs counter to human nature. Getting lean requires fighting the basic instinct to add, accumulate, store. Lean requires a precise understanding of value: the who, what, when, where, how, and why of the customer’s need. It means getting that value to them without complexity creeping in.

Get this book. Read it. It will change the way you think about innovation.

Product Development for the Lean Enterprise (Book Review)

“How could a business book keep me up until 2:30 in the morning?” I wondered as I collapsed into bed. True, it was a business novel, so it had engaging characters, a hint of a plot, and actual villains. But that wasn’t what kept me reading into the wee hours of the morning. All through the book I kept applauding Michael Kennedy for doing such an excellent job of showing how to apply lean thinking to product development. “He gets it!” I kept saying to myself. “He understands that product development is a whole different ballgame than manufacturing.”

The book’s narrator is three weeks from retirement and coasting. His boss has just been asked to take over a flagging engineering department, and our narrator gets a week to assemble a swat team and figure out how to revamp the product development process. Buried deep in the organization is an engineer who has studied how Toyota does product development. We follow her as she first convinces the swat team and then the executives that they need to change the way they think about product development.

We learn that product development is a knowledge-creating process, and learn what that means: Entrepreneurial leadership, responsibility-based planning, expert workforce, and set-based concurrent engineering. Before you fall asleep, note that the novel format helps make these concepts come alive. The swat team has to digest a new paradigm for product development, and then sell it to an executive who has the mistaken notion that lean product development should be pretty much like lean manufacturing.

After the book convinces you that you can’t treat development like production, it goes on to describe in detail what does work for development in an understandable and practical way. I learned a lot about set-based design, and I really liked the description of responsibility-based planning and control.

The book ends with a ‘where do we go from here’ section, offering large group interventions as a way to trigger participative change. The book didn’t offer a lot of guidance on exactly what to do, but it does draw an interesting parallel between the desired new product development process and the change process itself.

Business novels, by their nature, provide ideas and examples, rather than specifics of how proceed in situations that differ from the ideal presented in the novel. But a well-written business novels such as this one provide good way to clarify important ideas in a quick-to-read, entertaining style. It’s easier to attack sacred cows in fiction, and easier help the reader visualize what might happen if a real paradigm shift takes place. If you liked Goldratt’s novel “The Goal” then you might stay up most of the night reading this book, just like I did.

Reference
Product Development for the Lean Enterprise by Michael N. Kennedy, Oakela Press, 2003.

Incremental Funding (Book Review)

The customer wanted an on-line currency exchange capability added to their on-line financial service offerings. They figured it would take several months to implement. But the development team suggested a different approach: start by hiring a dozen telephone operators and implement the necessary software for these folks to execute currency trades. The company gave it a try, and in six weeks the first iteration was ready. With no more than an 800 number on their web site and a rudimentary interface to the currency market, new business was being transacted and profits being made.

Over the course of the next several months, the on-line trading capability was implemented around the core module originally used by the telephone operators. Not only did the company see early revenue, but the risk of failure disappeared once trading started. In addition, system requirements were defined by observing real trades.

In the book Software by Numbers – Low Risk, High Return Development (Prentice Hall, 2004) Mark Denne and Jane Cleland-Huang make the case for incremental delivery of software. Mark Denne developed the Incremental Funding Methodology (IFM) in the 1990’s to help land a large software development contract. In an attempt to distinguish his bid from the pack, he reorganized the deliveries into units of value, and adjusted the development sequence so that the customer would realize revenue faster and in the end, receive a greater return on their investment. The customer discovered that this approach dramatically reduced their need to borrow money and gave them earlier product release with lowered risk. In what seemed like hotly competitive bidding process, Mark’s company won the bid by emphasizing “time to value” instead of development efficiency.

Software by Numbers recommends dividing a project into Minimum Marketable Features (MMF’s). These are small feature sets which deliver some identifiable value to the customer. Each MMF should have its own return on investment (ROI). By laying out the potential ROI’s of various feature sets, an optimal development sequence for the MMF’s can be determined. Early deployment of key MMF’s reduces risk while generating revenue to help fund the remainder of the project.

When business people and software developers focus on identifying and valuing marketable features, their conversation is changed. Developers are exposed to ROI and stakeholders are confronted with the realities of software development. The entire team is focused on achieving business ROI early in the development cycle. Management sees continuously measurable progress, and the team benefits from the early and compelling feedback generated by real software being used in production.

With so many benefits, why wouldn’t IFM be the preferred approach for developing software? Denne and Cleland-Huang note that many practitioners view software architecture as a monolithic whole, requiring early definition because of the extensive impact that architectural changes can have on a system. On the other hand, they argue, it is not until the details of an architecture are implemented that one can tell if the architecture is viable. Thus architecture presents us with a chicken and egg problem.

The book recommends that architectural elements which support each set of MMF’s should be developed with their respective MMF’s. In other words, architectural development should be sequenced using the same financially driven priorities as feature development. While there may be times where architectural coherency may dictate early development of features not immediately related to the current feature set, in general it is not only possible but also preferable to defer implementation of architectural elements until the features requiring these elements is being developed.

As we discover that architecture not only can evolve, but in fact, in any deployed system, the architecture will evolve over time, the view that architecture must be fixed early in the development process becomes a liability. This is a view that creates architectures that are not tolerant of the change they inevitably must undergo. Once we accept that software architectures must be designed to be change-tolerant, the barrier to early deployment of high value features is lowered.

Returning to the currency trading example, we see that early deployment of marketable features is a compelling strategy for increasing return and reducing risk. At the same time the stage is set for a better understanding of the real system requirements and improved collaboration between developers and their customers.

Reference
Software by Numbers: Low-Risk, High-Return Development by Mark Denne, Jane Cleland-Huang, Prentice Hall, 2004

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