Tag Archives: United Kingdom

Manufacturing is not returning to the West

There’s many claims over the last year or so that “manufacturing is returning to the West” and “China’s days as the world’s factory are numbered”{{1}}. These claims are misguided.

[[1]]Vivek Wadhwa (23 July 2012), The End of Chinese Manufacturing and Rebirth of U.S. Industry, Forbes[[1]]

We’ve just reached a time where manual and skilled labour is no longer a major manufacturing cost, causing final assembly to slowly drifting toward the customer base it serves. This shift reduces the length of the supply chain from assembly to your front door resulting in a reduction in turn-around time which, in turn, reduces working capital requirements and allows manufacturers to push product updates through the supply chain faster.

Manufacturing isn’t leaving China and other low cost manufacturing centres. What has changed is that it now makes good sense to manufacture some high value but low volume and bulky products in other major markets, such as the U.S.

The problem with thinking that manufacturing is returning to the first world is the implicit assumption that this also means that the old manufacturing jobs will return. They won’t. They no longer exist. It also ignores that fact that the huge scale of manufacturing in China will help it to grab the lions share of the world manufacturing market for some time to come.

Manufacturing as a manual process

Consider Henry Ford’s assembly line from 1913: a complex, labour intensive process that created a large number of good, blue collar jobs.

566px-Ford_assembly_line_-_1913Source: Public Domain

When we think of manufacturing this is the image we usually have in head. It’s a bit like those train crossing signs that have a caricature of a steam engine on them. It might not be the current reality, but it’s the image we use to understand what’s going on around us.

As transport costs dropped, work moved to lower cost countries

Back in Henry Ford’s day transportation was expensive. Factories were often located close to the markets they served to minimise transport costs, with management struggling to ensure that enough raw materials arrived at the factory to keep it busy. However, the development of railroads, steam ships, and the shipping container network incrementally cut the cost of transport until it cost roughly the same to move a box across the world as it did to move it across the country.

As Marc Levinson points out in his book, The Box{{2}}:

[[2]]Marc Levinson, The Box: How the Shipping Container Made the World Smaller and the World Economy Bigger. iBooks.[[2]]

As transportation costs decline relative to other costs, manufacturers can relocate first domestically, and then internationally, to reduce other costs, which come to loom larger. Globalization, the diffusion of economic activity without regard for national boundaries, is the logical end point of this process. As transport costs fall to extremely low levels, producers move from high-wage to low-wage countries, eventually causing wage levels in all countries to converge. These geographic shifts can occur quickly and suddenly, leaving long-standing industrial infrastructure underutilized or abandoned as economic activity moves on.

This is the shift we’re thinking of when we consider off-shore manufacturing: China as the source of cheap (and fairly unskilled labour).

Today, manufacturing is not a manual process

Apple released an interesting video the other day{{3}}. It shows the manufacturing process for the new Mac Pro.

[[3]]Greg Koenig (22 October 2013), How Apple makes the Mac ProAtomic Delights[[3]]

SourceApple

What’s interesting about this process is how few people are involved.

Manufacturing has changed a lot in the last few decades. What was once dominated by manual labour is now an automated and highly efficient process. Machines have replaced people. We can see this in many of the factories that are returning to the West: they’re all highly efficient, highly automated, capital intensive operations that require very little manual or skilled labour.

7395855880_053e6daede_cSource: Steve Jurvetson

Machines, however, have yet to replace engineers

While capital has won over manual and skilled labour, that same is not true for engineers: knowledge workers.

As Roger Martin found in his research for a recent HBR article{{4}}:

[[4]]Roger L. Martin (October 2013), Rethinking the Decision Factory, Harvard Business Review[[4]]

I vividly remember working with the CEO of one of North America’s largest bread manufacturers in 1990–1991. He had just replaced a labor-intensive and antiquated plant with the most advanced bread bakery on the continent. He proudly told me that the new computerized ovens and packaging machinery had reduced direct labor costs by 60%. But meanwhile, a throng of new and expensive knowledge workers had been added at both the head office and the plant—engineers, computer technicians, and managers—to take care of the sophisticated computer systems and state-of-the-art equipment. The new plant wasn’t quite the unalloyed good that it appeared at first sight. Variable costs of manual labor fell, but the fixed cost of knowledge workers rose, making it critical to keep capacity utilization high—which was possible in some years but not in others.

While the West has been worried about losing it manufacturing capability, many of the off-shore manufacturing destinations have been investing in education. China, for example, now has a huge engineering workforce that companies can draw own to sort out their manufacturing problems.

It’s this incredible ability to mobilise huge workforces that is keeping many manufactures in China. An article in the New York Times from last year has an Apple anecdote that shows this in action{{5}}.

[[5]]Charles Dugigg & Keith Bradsher (21January2012), How the U.S. Lost Out on iPhone Work, The New York Times[[5]]

Another critical advantage for Apple was that China provided engineers at a scale the United States could not match. Apple’s executives had estimated that about 8,700 industrial engineers were needed to oversee and guide the 200,000 assembly-line workers eventually involved in manufacturing iPhones. The company’s analysts had forecast it would take as long as nine months to find that many qualified engineers in the United States.

Moving closer to the customer

The rapid pace of change in today’s market is driving companies to reduce the time between final assembly and when the product drops into the customer’s waiting hands.

Zara is the poster child for this shift, with a supply chain can create a new product and then have it in the stores in around two weeks. Zara has used this ability to disrupt the traditional annual, seasonal fashion cycle, resulting Zara becoming one of the largest retailers in the world.

Apple’s recent decision to make the Mac Pro in the U.S. is part of a trend to move the manufacturing of high value but low volume and bulky products closer to the customer. Elon Musk’s Tesla is also part of this trend.

Manufacturing automation technology has reached the point that it makes more sense to locate the manufacturing of these products closer to the customer, allowing transport costs and delivery times to be minimised.

We shouldn’t assume, however, that this trend will end with manufacturing returning to the West.

It’s easy to forget the more people live in Asia than in the entire rest of the world combined. If manufacturing is moving to be closer to the customer, then we need to remember that there are more customers in Asia than in the rest of the world. China’s position as a manufacturing powerhouse appears safe for the time being.

CK6aONG

Source: valeriepieris

What we mean by “export” is changing

So just where will this trend take us? (And, by extension, will our old export industries return, bringing their jobs back with them?)

The future of manufacturing and export seems – like to many industries – connected to the knowledge economy.

Those old manufacturing jobs are never coming back. They no longer exist. Similarly, thinking in terms of operating a factory and then exporting to another country is also looking somewhat antiquated.

Today (or perhaps, tomorrow) a manufacturer is a simply company that is run from one country and, from there, manages the sale of products in another.

Kogan{{6}} is a great example of this. The business is run from South Melbourne, Australia, which is where the products are designed. The products themselves are made in China and (in many cases) shipped directly to the United Kingdom where they are sold via the company’s UK web site (which is also managed from Port Melbourne, but hosted somewhere “in the cloud”).

[[6]]Kogan @ PEG[[6]]

An even more interesting example is another local business which sells safety barriers that are placed around robots in factories to ensure that workers aren’t accidentally injured. They recently started exporting to Europe. They did this by setting up a small, automated factory in Germany to service the European market. The barriers are designed in Australia and the designs are beamed directly to the machines in Germany, machines that consume resources from all over the globe.

So manufacturing – as we’ve traditionally understood it – is not returning to the West. The blue collar jobs that went overseas are not coming home to give our rather lacklustre economies a boost.

We can also expect China to remain an manufacturing powerhouse for the foreseeable future. The huge scale of operations over there, and the ability to rapidly redeploy these resources, will allow China to grab more than it’s fair share of the world manufacturing market.

Manufacturing, like so many industries{{7}}, is changing, and changing rapidly. What’s most interesting though, is how a new generation of companies are emerging that are finding ways to exploit this situation to “export”, and create new, knowledge intensive jobs at home in the process.

[[7]]The destruction of traditional retail @ PEG[[7]]

Source: Steve Jurvetson

Winners and losers in retail

There’s a lot of talk in the media at the moment about the soft retail market. Consumer confidence is down1)Australian Consumer ConfidenceTrading Economics and we (as we’re all consumers) are not spending like we used to, or at least we’re not spending like the retailers would like us to, and that when we do spend that we’re running to cheaper online retailers. I’m not sure that this is the whole story though.

With a spare Sunday afternoon on my hands I decided to spend some time trawling through the ABS retail data and take a look beyond the month-on-month trends. Working on an Australian version of the Shift Index2)The Shift Index: Measuring the forces of long term change, Deloitte has nudged me to wonder about the long term trends that are affecting retail.

Continue reading Winners and losers in retail

References   [ + ]

What I like about jet engines

Rolls-Royce{{1}} (the engineering company, not the car manufacturer) is an interesting firm. From near disaster in the 70s, when the company was on the brink of failure, Rolls-Royce has spent the last 40 years reinventing itself. Where it used to sell jet engines, now the company sells hot air out the back of the engines, with clients paying only for the hours an engine is in service. Rolls-Royce is probably the one of the cleanest examples of business-technology{{2}} that I’ve come across; with the company picking out the synergies between business and technology to solve customer problems, rather than focusing on trying to align technology delivery with a previously imagined production process to push products at unsuspecting consumers. I like this for a few reasons. Firstly, because it wasn’t a green fields development (like Craig’s List{{3}} et al), and so provides hope for all companies with more than a few years under their belt. And secondly, as the transformation seems to have be the result of many incremental steps as the company felt its way into the future, rather than as the result of some grand, strategic plan.

[[1]]Rolls Royce[[1]]
[[2]]Business-Technology defined @ Forrester[[2]]
[[3]]Craig’s list[[3]]

A Rolls-Royce jet engine

I’ve been digging around for a while (years, not months), looking for good business-technology case studies. Examples of organisations which leverage the synergies between business and technology to create new business models which weren’t possible before, rather than simply deploying applications to accelerate some pre-imagined human process. What I’m after is a story that I can use in presentations and the like, and which shows not just what business-technology is, but also contrasts business-technology with the old business and technology alignment game while providing some practical insight into how the new model was created.

For a while I’ve been mulling over the obvious companies in this space, such as Craig’s List or Zappos{{4}}. While interesting, their stories don’t have the impact that they could as they were green fields developments. What I wanted was a company with some heritage, a history, to provide the longitudinal view this needs.

[[4]]Zappos[[4]]

The company I keep coming back to is Rolls-Royce. (The engineering firm, not the car manufacturer). I bumped into a story in The Economist{{5}}, Britain’s lone high-flier{{6}}, which talks about the challenge of manufacturing in Britain. (Which is, unfortunately, behind the pay wall now.) As The Economist pointed out:

A resurgent Rolls-Royce has become the most powerful symbol of British manufacturing. Its success may be hard to replicate, especially in difficult times.

[[5]]The Economist[[5]]
[[6]]Britain’s lone high-flier @ The Economist[[6]]

With its high costs and (relatively) inflexible workforce, running an manufacturing business out of Britain can be something of a challenge, especially with China breathing down your neck. Rolls-Royce’s solution was not to sell engines, but to sell engine hours.

This simple thought (which is strikingly similar to the tail of the story in Mesh Collaboration{{7}}) has huge ramifications, pushing the company into new areas of the aviation business. It also created a company heavily dependent on technology, from running realtime telemetry around the globe through to knowledge management. The business model — selling hot air out the back of an engine — doesn’t just use technology to achieve scale, but has technology woven into its very fabric. And, most interestingly, it is the result of tinkering, small incremental changes rather than being driven by some brilliant transformative idea.

[[7]]Mash-Up Corporations[[7]]

As with all these long term case studies, the Rolls-Royce story does suffer from applying new ideas to something that occurred yesterday. I’m sure that no one in Rolls-Royce was thinking “business-technology” when the company started the journey. Nor would they have even thought of the term until recently. However, the story still works for me as, for all it’s faults, I think there’s still a lot we can learn from it.

The burning platform was in the late 60s, early 70s. Rolls-Royce was in trouble. The company had 10% market share, rising labour costs, and was facing fierce competition from companies in the U.S. Even worse, these competitors did not have to worry about patents (a hangover from the second world war), they also had a large domestic market and a pipeline of military contracts which put them in a much stronger financial position. Rolls-Royce had to do something radical, or facing being worn down by aggressive competitors who had more resources behind them.

Interestingly, Roll-Royce chose to try and be smarter than the competition. Rather than focus on incremental development, the company decided to designed a completely new engine. Using carbon composite blades and a radical new engine architecture (three shafts rather than two, for those aeronautical engineers out there) their engine was going to be a lot more complex to design, build and maintain. It would also be a lot more fuel efficient and suffer less wear and tear. And it would be more scalable to different aircraft sizes. This approach allows Rolls-Royce to step out of the race for incremental improvements in existing designs (designing a slightly better fan blade) and create a significant advantage, one which would take the company’s competitors more than the usual development cycle or two to erase.

Most of the margin for jet engines, however, is in maintenance. Some pundits even estimate that engines are sold at a loss (though the manufactures claim to make modest margins on all the engines they sell), while maintenance can enjoy a healthy 35%. It’s another case of give them the razor but sell them the razor blades. But if you give away the razors, there’s always the danger that someone else may make blades to fit your razor. Fat margins and commoditized technology resulted in a thriving service market, with the major engine makers chasing each other’s business, along with a horde of independent servicing firms.

Rolls-Royce’s interesting solution was to integrate the expertise from the two businesses: engine development and servicing. Rather than run them as separate businesses, the company convinced customers to pay a fee for every hour an engine was operational. Rather than selling engines, the company sells hot air out the back of an engine. This provides a better deal for the customers (pay for what you use, rather than face a major capital expense), while providing Rolls-Royce with a stronger hold on its customer base.

Integrating the two business also enabled Rolls-Royce to become better at both. Maintenance data helps the company identify and fix design flaws, driving incremental improvements in fuel efficiency while extending the operating life (and time between major services) tenfold over the last thirty years. It also helps the company predict engine failures, allowing maintenance to be scheduled at the most opportune time for Rolls-Royce, and their customers.

Rolls-Royce leveraged this advantage to become the only one of the three main engine-makers with designs to fit the three newest airliners in the market: the Boeing 787 Dreamliner, the Airbus A380 and the new wide-bodied version of the Airbus A350. Of the world’s 50 leading airlines, 45 use its engines.

Today, an operations centre in Derby assess, in real time, the performance of 3,500 jet engines enabling to Rolls-Royce to spot issues before they become problems and schedule just-in-time maintenance. This means less maintenance and more operating hours, fewer breakdowns (and, I expect, happier customers), and the operational data generated is fed back into the design process to help optimise the next generation of engines.

This photograph is reproduced with the permission of Rolls-Royce plc, copyright © Rolls-Royce plc 2010
Rolls-Royce civil aviation operations in Derby

This service-based model creates a significant barrier to competitors for anyone who wants to steal Rolls-Royce’s business. Even if you could clone Rolls-Royce’s technology infrastructure (hard, but not impossible), you would still need to recreate all the tacit operational knowledge the company has captured over the years. The only real option is to recreate the knowledge yourself, which will take you a similar amount of time as it did Rolls-Royce, while Rolls-Royce continues to forge ahead. Even poaching key personnel from Rolls-Royce would only provide a modest boost to your efforts. As I’ve mentioned before{{8}}, this approach has the potential to create a sustainable competitive advantage.

[[8]]One of the only two sources of sustainable competitive advantage available to us today @ PEG[[8]]

While other companies have adopted some aspects of Rolls-Royce’s model (including the Joint Strike Fighter{{9}}, which is being procured under a similar model), Rolls-Royce continues to lead the pack. More than half of its existing engines in service are covered by such contracts, as are roughly 80% of those it is now selling.

[[9]]The Joint Strike Fighter[[9]]

I think that this makes Rolls-Royce a brilliant example of business-technology in action. Rolls-Royce found, by trial and error, a new model that wove technology and business together in a way that created an “outside in” business model, focused on what customers what to buy, rather than on a more traditional “inside out” model based on pushing products out into the market that the company wants to sell. You could even say that it’s an “in the market” model rather than a “go to market” model. And they did this with a significant legacy, rather than as a green fields effort.

In some industries and companies this type of “outside in” approach was possible before advent of the latest generation of web technology, particularly if it was high value and the company already had a network in place (such as Rolls-Royce success). For most companies it is only now becoming possible with business-technology along with some of the current trends, such as cloud computing, which erase many of the technology barriers.

The challenge is to figure out the “in the market” model you need, and then shift management attitude. Given constant change in the market, this means an evolutionary approach, rather than a revolutionary (transformative) one.

People don’t like change. (Or do they?)

I seem to be having a lot of conversations at the moment around whether people (you, me and everyone else) like and embrace change, or whether they resist it. The same question arises for companies. Like a lot of these questions, I think it depends. As individuals we don’t mind change, given appropriate circumstances. Organisations also want to change (and in today’s business environment it seems to be a question of changing or becoming irrelevant). However, people in organisations are usually strongly incentivised to dislike change, especially if they want to make that next repayment on their new mortgage. Fixing this, and creating a culture that embraces change, means changing the way we think about and structure our organisations and our careers. It means rethinking the rules of enterprise IT.

Every time a conversation comes around to the topic of change, I’m always reminded of a visit I made to a Toyota factory something like a decade ago. It’s so long ago now that I can’t even remember the reason for the visit, but that’s not the point of this missive.

Toyota, like most businesses, loves change. (Many large companies reorganise so often that change seems to be the only constant.) Change, embodied in the development of the Toyota Production System, was what took Toyota from the bottom of the global car industry to the top. Change is also why many of us have moved companies, following jobs as our employers reorganise their operations. For some of us, change is an opportunity. For many though, change is the tool of the man as he tries to disrupt our lives. Change means unwanted relocations, pay cuts, career stalls, or the need to shift jobs when we don’t want to. Change is something to be resisted.

What was interesting about my visit to Toyota though, was the attitude of the workers on the shop floor had to change. They didn’t hate it. They didn’t even resist it. They actually arrived at work each day eager to see how work practices had been changed since the end of their last shift.

A Toyota assembly line circa 2000.
A Toyota assembly line circa 2000

The concrete example I saw of this was the pre-sorting of seatbelt parts into coloured tubs. Apparently only a few weeks earlier the parts had been arranged on a wall. All hooks and dangling parts, like your Dad’s tools in the shed. When a car came down the assembly line a worker would select the parts appropriate for the car model, and then attach them to the car. Each seat belt had roughly four parts, so that meant there was three unnecessary decisions. Unnecessary decisions usually mean mistakes, mistakes waste time and money, and there were a number of mistakes made.

One day a member of the shop floor team had had the bright idea of pre-sorting the seat belts to avoid these mistakes. Some coloured tubs were sourced (some of the shop floor team drove to the local Walmart with a little petty cash), parts were sorted into tubs, and they gave the idea a trial run. Selecting seatbelt parts for a car now only required one decision: which tub?

The idea was a huge success; error rates went down dramatically. I hear that it was even taken global, and implemented in most Toyota factories around the world. (Though being around ten years ago, and with today’s rapid pace of change, I expect that the tubs have been superseded by now.)

What’s interesting about this story is that the change originated on the shop floor, from the assembly line worker who were actively looking to improve operations, rather than from head office as part of a reorganisation. Some of the improvements I heard about even resulted in the elimination of jobs, with the workers redeployed elsewhere in the factory. Workers weren’t just changing how they did something, they were also changing what they did. Change was what made the work interesting and engaging for the workers, rather than being seen as an oppressive tool used by the man.

I think we can safely set aside the idea that works don’t like change, as this story is not an isolated incident. Why then, do so many people resist change? Why, for every Toyota factory, there is a story like the UK newspaper industry, where workers (and unions) resisted change for decades, until Rupert Murdoch came along.

Rupert Murdoch, destroyer of unions, and good Melbourne boy
Rupert Murdoch, destroyer of unions, and good Melbourne boy

The problem is not people or organisations, but people in organisations.

People are funny things; they tend to do what you incentivise them to do. There’s an excellent article over at the NY Times, The no-stars all-star, which talks about measurement and incentives in basketball. We often talk about “what gets measured determines what gets done” from an employee incentive point of view, but this article puts some real meat on the bones of that argument.

Shane Battier, the no-stars all-star
Shane Battier, the no-stars all-star

As the article says (on page two):

There is a tension, peculiar to basketball, between the interests of the team and the interests of the individual. The game continually tempts the people who play it to do things that are not in the interest of the group.

A little later it goes on to mention (on page three):

A point guard might selfishly give up an open shot for an assist. You can see it happen every night, when he’s racing down court for an open layup, and instead of taking it, he passes it back to a trailing teammate. The teammate usually finishes with some sensational dunk, but the likelihood of scoring nevertheless declined. “The marginal assist is worth more money to the point guard than the marginal point,” Morey says.

The point guard’s career is defined by the number of assists he makes (among other metrics), and he’ll try and increase the number of assets even if it’s not in the best interest of the team. After all, teams come and go, while he has a career to maintain.

Once you place a person into a role you have put them on a career path which will determine their attitude to change.

Usually we take an operational approach to defining roles, rewarding people for the volume of work they are responsible for. Career progression then means increasing the amount of work they are responsible for, regardless of what this means for the company.

Measuring a project manager in terms of head count or revenue under management will give them a strong preference for creating ever bigger projects. It doesn’t matter if the right thing to do is create more, smaller projects, rather than run a programme of a few major projects as we have in the past. Your project manager’s career path is to increase their head count and revenue under management. And they do have those private school fees due soon.

Just like the point guard, change that will prevent career progression will be resisted (remember those kids in private school), even if it is counter to the company’s best interests. Which makes the current transformation we’re seeing in IT all the more important, because if we set the wrong incentives in place then we just might be our own worst enemies.

We can’t force a square peg into a round hole; nor can we force our existing employees to take their current roles and careers into a new organisational model. They just don’t fit. Take IT for example. We can’t expect many modern IT departments to spontaneously modernise themselves, transforming into agile business-technology engines under their own volition. It’s not that the departments don’t want to change: they do. Nor are most of the employees, as individuals, opposed (remember the Toyota example). But the combination of people and organisation will repel all but the most destructive boarders.

It’s interesting how other games, games other than basketball that is, have structural solutions to this problem. One solution is the line-up in baseball. From the NY Times article (page two, again):

“There is no way to selfishly get across home plate,” as Morey puts it. “If instead of there being a lineup, I could muscle my way to the plate and hit every single time and damage the efficiency of the team — that would be the analogy.”

Solving this problem in IT means rethinking the rules of IT.

The game of IT has, for the last few decades, been determined by the need to deliver large, enterprise applications into the IT estate. Keep the lights on, don’t lose orders, and automate anything that hasn’t yet been automated. Oh — and I’d like my reports accurate and on time. IT as the game of operational engineering. It was these rules that drove the creation of most of the roles we have in enterprise IT today.

However, this has changed. Decisions are now more important than data, and the global credit crunch is driving us to reconsider the roles we need in IT. We’re trying to reinvent our IT departments for the modern era – I even posted about how this was driving the need the need to manage technology, and not applications – but we haven’t changed the rules to suit.

If we want out people to embrace change, as the people on the shop floor at Toyota did, then we need to provide them with roles and careers that support them in the new normal. And this means changing the rules. Out with the more – more applications, larger projects, more people – and in with the new.

So what are the new rules for IT?

Innovation [2010-02-01]

Another week and another collection of interesting ideas from around the internet.

As always, thoughts and/or comments are greatly appreciated.

Working from the outside in

We’re drowning in a sea of data and ideas, with huge volumes of untapped information available both inside and outside our organization. There is so much information at our disposal that it’s hard to discern Arthur from Martha, let alone optimize the data set we’re using. How can we make sense of the chaos around us? How can we find the useful signals which will drive us to the next level of business performance, from amongst all this noise?

I’ve spent some time recently, thinking about how the decisions our knowledge workers make in planning and managing business exceptions can have a greater impact on our business performance than the logic reified in the applications themselves. And how the quality of information we feed into their decision making processes can have an even bigger impact, as the data’s impact is effectively amplified by the decision making process. Not all data is of equal value and, as is often said, if you put rubbish in then you get rubbish out.

Traditional Business Intelligence (BI) tackles this problem by enabling us to mine for correlations in the data tucked away in our data warehouse. These correlations provide us with signals to help drive better decisions. Managing stock levels based on historical trends (Christmas rush, BBQs in summer …) is good, but connecting these trends to local demographic shifts is better.

Unfortunately this approach is inherently limited. Not matter how powerful your analytical tools, you can only find correlations within and between the data sets you have in the data warehouse, and this is only a small subset of the total data available to us. We can load additional data sets into the warehouse (such as demographic data bought from a research firm), but in a world awash with (potentially useful) data, the real challenge is deciding on which data sets to load, and not in finding the correlations once they are loaded.

What we really need is a tool to help scan across all available data sets and find the data which will provide the best signals to drive the outcome we’re looking for. An outside-in approach, working from the outcome we want to the data we need, rather than an inside-out approach, working from the data we have to the outcomes it might support. This will provide us with a repeatable method, a system, for finding the signals needed to drive us to the next level of performance, rather than the creative, hit-and-miss approach we currently use. Or, in geekier terms, a methodology which enables us to proactively manage our information portfolio and derive the greatest value from it.

I was doodling on the tram the other day, playing with the figure I created for the Inside vs. Outside post, when I had a thought. The figure was created as a heat map showing how the value of information is modulated by time (new vs. old) and distance (inside vs. outside). What if we used it the other way around? (Kind of obvious in hindsight, I know, but these things usually are.) We might use the figure to map from the type of outcome we’re trying to achieve back to the signals required to drive us to that outcome.

Time and distance drive the value of information
Time and distance drive the value of information

This addresses an interesting comment (in email) by a U.K. colleague of mine. (Jon, stand up and be counted.) As Andy Mulholland pointed out, the upper right represents weak confusing signals, while the lower left represents strong, coherent signals. Being a delivery guy, Jon’s first though was how to manage the dangers in excessively focusing on the upper right corner of the figure. Sweeping a plane’s wings forward increases its maneuverability, but at the cost of decreasing it’s stability. Relying too heavily on external, early signals can, in a similar fashion, could push an organization into a danger zone. If we want to use these types of these signals to drive crucial business decisions, then we need to understand the tipping point and balance the risks.

My tram-doodle was a simple thing, converting a heat map to a mud map. For a given business decision, such as planning tomorrow’s stock levels for a FMCG category, we can outline the required performance envelope on the figure. This outline shows us the sort of signals we should be looking for (inside good, outside bad), while the shape of the outlines provides us with an understanding (and way of balancing) the overall maneuverability and stability of the outcome the signals will support. More external predictive scope in the outline (i.e. more area inside the outline in the upper-right quadrant) will provide a more responsive outcome, but at the cost of less stability. Increasing internal scope will provide a more stable outcome, but at the cost of responsiveness. Less stability might translate to more (potentially unnecessary) logistics movements, while more stability would represent missed sales opportunities. (This all creates a little deja vu, with a strong feeling of computing Q values for non-linear control theory back in university, so I’ve started formalizing how to create and measure these outlines, as well as how to determine the relative weights of signals in each area of the map, but that’s another blog post.)

An information performance mud map
An information performance mud map

Given a performance outline we can go spelunking for signals which fit inside the outline.

Luckily the mud map provides us with guidance on where to look. An internal-historical signal is, by definition driven by historical data generated inside the organization. Past till data? An external-reactive signal is, by definition external and reactive. A short term (i.e. tomorrow’s) weather forecast, perhaps? Casting our net as widely as possible, we can gather all the signals which have the potential to drive us toward to the desired outcome.

Next, we balance the information portfolio for this decision, identifying the minimum set of signals required to drive the decision. We can do this by grouping the signals by type (internal-historical, …) and then charting them against cost and value. Cost is the acquisition cost, and might represent a commercial transaction (buying access to another organizations near-term weather forecast), the development and consulting effort required to create the data set (forming your own weather forecasting function), or a combination of the two, heavily influenced by an architectural view of the solution (as Rod outlined). Value is a measure of the potency and quality of the signal, which will be determined by existing BI analytics methodologies.

Plotting value against cost on a new chart creates a handy tool for finding the data sets to use. We want to pick from the lower right – high value but low cost.

An information mud map
An information mud map

It’s interesting to tie this back to the Tesco example. Global warming is making the weather more variable, resulting in unseasonable hot and cold spells. This was, in turn, driving short-term consumer demand in directions not predicted by existing planning models. These changes in demand represented cost, in the from of stock left on the shelves past it’s use-by date, or missed opportunities, by not being able to service the demand when and where it arises.

The solution was to expand the information footprint, pulling in more predictive signals from outside the business: changing the outline on the mud map to improve closed-loop performance. The decision to create an in-house weather bureau represents a straight forward cost-value trade-off in delivering an operational solution.

These two tools provide us with an interesting approach to tackling a number of challenges I’m seeing inside companies today. We’re a lot more externally driven now than we were even just a few years ago. The challenge is to identify customer problems we can solve and tie them back to what our organization does, rather than trying to conceive offerings in isolation and push them out into the market. These tools enable us to sketch the customer challenges (the decisions our customers need to make) and map them back to the portfolio of signals that we can (or might like to) provide to them. It’s outcome-centric, rather than asset-centric, which provides us with more freedom to be creative in how we approach the market, and has the potential to foster a more intimate approach to serving customer demand.

Tesco’s looking outside the building to predict customer needs

Tesco is using external weather data to drive sales
Tesco is using external weather data to drive sales

Tesco, the UK’s largest retailer, has started using weather forecasts to help determine what to stock in its stores across the UK.

Traditional approaches to stock management use historical buying data to drive stock decisions. This has worked well to date, but the increasing unpredictability of today’s weather patterns — driven by global warming — has presented business with both an opportunity and a challenge. An unexpected warm (or cold) spell can create unexpected spikes in demand which go unserviced, while existing stock is left on the shelves.

In Tesco’s own words:

In recent years, the unpredictability of the British summer — not to mention the unreliability of British weather forecasters — has caused a massive headache for those in the retail food business deciding exactly which foods to put out on shelves.

The present summer is a perfect example, with the weather changing almost daily and shoppers wanting barbecue and salad foods one day and winter food the next.

Tesco’s solution was to integrate detailed regional weather reports — valuable, external information — with the sales history at each Tesco store. A rise of 10C, for example, led to a 300% uplift in sales of barbecue meat and a 50% increase in sales of lettuce.

Integrating weather and sales data will enable Tesco to both capture these spikes in demand, while avoiding waste.

(Largely adapted from the article in the Times Online.)