Mar 8, 2021 | Analytics, Governance, Operations
One of the significant problems in making any change is the articulation of the need to change, and the outcomes that are expected as a result.
Overcome those two, and change is suddenly easier, albeit still really hard.
The first hurdle is the articulation.
In order to communicate and have complex ideas generally understood, you do not use technical, academic jargon backed by data, you use stories and metaphors in a way that connects with the audience.
Communicating industry 4.0 is such a complex challenge.
What is it, how will it affect me, why should I be interested?
Answering these questions is a core foundation of gaining acceptance, followed by action that becomes automatic as it gets buried in the auto-response system.
Remember the last time you put your hand onto a hot stove.
Before you felt anything, you had reacted by pulling your hand away, a totally unconscious, instantaneous, action then, it started to hurt like hell.
Think about the processes involved in this.
First: the ‘data’ that indicated the stove was hot was collected by the nerves in your fingers and hand.
Second: the ‘data’ is sent for processing to your brain, the CPU between your ears. This processing concludes your hand is in danger of being burnt.
Third: That conclusion is sent to the muscles that control where your hand is, with firm instructions to remove it immediately.
Fourth: Your hand is pulled back out of danger.
Fifth: It starts to hurt like hell, and the memory of that hurt is stored deep in your personal CPU for future reference should your hand stray again.
The astonishing thing is that the first four happen without thought, instantaneously, and the fifth is a long term ‘frame’ through which you unconsciously ‘feel’ the hurt and approach the stove warily. It is a neural network that collaborates, communicates, drives action, and learns.
Industry, or more specifically, Factory 4.0 is, similarly, a set of tools that collects, analyses and acts on data without direction, and learns from the experience, adding to the auto-response ‘memory bank’ and adjusted based on the ‘learning’ that occurs as data on outcomes is collected. The system becomes more Automatic than Artificial.
Header cartoon credit: Tom Gauld in ‘New Scientist’ magazine.
Jan 4, 2021 | Analytics, Social Media, Strategy
Which StrategyAudit posts gathered attention during 2020?
The StrategyAudit blog, and supporting research is both a personal archive of ideas, that vary from complete to really half-baked, a recitation of the things I see and learn from those I work and interact with, and the lessons that come from those interactions.
Over the course of the year, there were 120 posts published on the StrategyAudit site. It seems like a small return for the effort. Luckily, I did not keep a count of the hours spent thinking, researching, writing, and editing these 120, or I would probably have to counsel myself to do something more useful to my retirement fund with the time.
The 3 most popular posts published this year.
A bit of an unfair advantage accrues to those published earlier in the year. However, the pattern across the decade of this blog has consistently demonstrated that most views of most posts happen over the week or so after publishing. It is the minority that then pick up later and continue to deliver multiple views weekly over a long period.
Reflecting on the new management challenges created by Covid, the April attempt at predicting the impact of Covid came in first. Perhaps understandable, and with the benefit of now 8 months hindsight, I am very pleased with the accuracy of the predictions. That will be the subject, as promised at the time, of a separate post. Second place goes to a June post that looked at what I saw as the 6 critical challenges of remote work, a topic we were all thinking about, and at that time just coming to grips with. Third was a bit of a personal rant, which obviously struck a chord at the time, after that idiot MP Craig Kelly jumped on British morning television in January, telling all and sundry that the fires, then ravaging the east coast of NSW, bore no relationship to the hoax that is climate change.
I am going to stretch the friendship a bit and give a dead cat bounce award to a post that I think had some considerable value as I review it, but that got no traction at all. I set out to describe the benefits that may flow from the Covid crisis, reflecting on the adage that there is a silver lining in every cloud. As with the winner in the category, I feel vindicated that the predictions made have been pretty accurate. Perhaps it was just a lousy headline?
The 3 most popular posts of the year.
This is a hands down to a post published over 6 years ago that describes the business model of supermarkets. It has continued to be the most viewed post every year since it was first published. Coming in second is the perennial runner up published in 2016, describing the 4 dimensions of project planning. Third was a welcome surprise, from early 2018, a marketers explanation of the accounting term Net Present Value. Many of those who run small businesses have a disturbing lack of understanding of even the most basic financial management tools, of which NPV is a common and very useful one. There are several other similar posts on accounting related ‘accounting type’ topics that also contributed significantly to the numbers.
Ideas that got no traction, but that seem valuable.
Some of the ideas I post may be a bit whacky, at least to some. However, it is not my job to reflect the consensus, it is my job to stimulate thought, and create some disturbance to the status quo by throwing in stuff from left field.
This one from 2019 combines two ideas. The first that the demarcation between marketing and sales is artificial nonsense created for the convenience of corporate management, and the second, that the accepted sales funnel is as redundant as a knife in a gunfight. Checklists on just about everything abound on the net. This post from late 2017 summarises a checklist I use when assessing the health of a business, was prompted by a similar idea published in the AICD magazine written by Phil Ruthven, for whom I have a very high regard. In 2013 I stumbled across an article in Fast Company magazine that started to explain the OODA loop, an idea that evolved from the fertile and obsessive mind of US air Force colonel John Boyd. I have since read a biography of Boyd, and spent considerable time reflecting on the competitive implications of the OODA loop, which I think is a seminal idea, highlighted this year by the speed and destructive spread of the Corona virus. Those who have successfully re-oriented themselves to get inside the ‘turning circle’ of their competitors, and the spread of the ‘Bug’, and been able to pivot their businesses in the face of the unexpected, have followed, mostly without knowing, the wisdom of the loop to their collective benefit. This follow up published in October 2020 was, sadly, a very strong contender for the dead cat award for 2020.
Finally, one of my personal favourite posts, viewed only once this year, published in 2016 after the death of Leonard Cohen. It has nothing to do with running a business, but is a deeply personal post, reflecting on a couple of the pivotal events in my life.
I look forward to interacting with you throughout 2021, which is getting harder, and harder. The combined impact of the continuing increasingly intense battle for your attention, and the squeezing of organic access to those who may be interested by the ‘social’ platforms makes life a challenge. (with the number of links in this post, LinkedIn is likely to stick me in solitary and throw away the key) If you find value in my thoughts, subscribe to them directly from the website, and spread the word amongst your networks. I promise not to follow you around when you do.
Have a better 2021 than 2020, perhaps an easy goal for most, and the basis of continuous improvement, finding a way to do a better job every day.
Header cartoon: once again, my thanks to Hugh McLeod at gapingvoid.com for the header
Nov 9, 2020 | Analytics, Sales
Getting people to change their minds is tough.
Just look at the divisions in the US over the last months in the lead up to the presidential (have you ever seen anything less presidential?) election.
Most set out to change somebody’s mind by telling them they are wrong, here is the right answer.
This rarely works well, as the natural reaction is pushback as people defend their existing position.
Instead, you have to find the things that they want that are consistent with the position you hold, and deliver those to them.
A former client had been successful for a long time selling the manufacture of large capital items to its natural Australian customer base. Over time, their market share had diminished as lower priced overseas competitors ate away at their base. Their focus was on price, and they cut corners in all sorts of little ways in an attempt to remain competitive, and the erosion continued. The turnaround came when they moved their focus from the procurement functions to engineering, giving the engineering staff of their customers the ammunition to argue the case that in fact, the more expensive invoice cost of their gear was better value than the ‘cheaper’ items sourced offshore. They created what they called ‘the TLC index’: Total Lifetime Cost. This was a calculation based on data and case studies over a considerable period that included scheduled maintenance and replacement parts, reliability data, the costs of downtime caused by offshore supply chain delays, ease of access to those who did the design and fabrication of the component parts, and several other items.
The invoice price on the purchase order was then shown in an entirely different light.
They did not tell their customers that the cheaper offshore item was an inferior product, they focussed on the things the engineering staff were concerned about, and gave them the ammunition to carry the argument based on the value of efficiency and reliability over the life of the items, rather than just focussing on the invoice price of the initial procurement.
It is easier in most cases to focus on price. However, demonstrating that price is only one part of the value equation delivers better results over time.
Dilbert again demonstrates insight: thanks to Scott Adams.
Oct 19, 2020 | Analytics, Marketing
How do you identify those who might emerge as competitors?
Might as well ask what the weather will be like next week, or next year. Just looking out the window will not help much, as things tend to change pretty quickly.
So it is with identifying potential competitors and the new value propositions they offer. However, like forecasting the weather, there are indicators, models that can be applied that will at least throw some light on the question.
It seems to me that there are three perspectives to this question, best described by the usual ‘think outside the box’ metaphor.
- Competitors from inside the current box
- Competitors from outside the current box
- Competitors from outside the postcode.
Leaving aside the great benefit of hindsight, it is generally pretty easy to see potential competitors from inside your current box. You are already rubbing up against them in some way, technically, geographically; you might have common customers for products with slightly different characteristics or value propositions. There is always the possibility for someone in an adjacent market expanding their control of the supply chain vertically, which will bring them into competition with you. Now, there are many digital tools that will assist the process from google key word searches, to explicitly looking at where similar offerings are emerging via social media. In addition, these often obvious emerging competitors are usually the ones your sales force are always on about, as they are the dog that got their homework, often by making cut price offerings of stripped down products to your customers.
Identifying potential competitors from outside the box is a bit harder. it is also increasingly important, as the barriers to entry to many industries have been blown away.
To do this well requires focussed critical thinking, and understanding of the drivers of purchase by your customers, the evolution of those purchase drivers, and the current sources of purchase friction encountered by customers. This takes some commitment to longer term strategic thinking.
Identifying those potential competitors from outside the postcode in a disciplined manner is extraordinarily hard, and beyond the resources of any SME I have ever seen. It not only requires the sort of time for deep thinking and scenario analysis available only to large organisations, it requires a very ‘absorbent’ innovation culture, one that accepts the inevitability of major disruption, and explicitly goes about discovering what those sources of disruption might be, and then disrupting themselves.
This sort of out of the postcode thinking opens up your mind to potential competitors. It also opens your mind to potential sources of future growth for yourself. As a result, having as a part of your strategic thinking and review process an explicit and disciplined ideation process should be a part of every strategic exercise and review.
These three perspectives all have in common the requirement that any enterprise, to be successful competitively over the long term, needs to ‘understand itself’ and be very sensitive to changes in their operating and competitive environment. Then, they have to be able to respond, which is often the hardest bit because it demands change. In the case of out of the postcode opportunities, radical change, and a high level of risk tolerance are required. As Sun Tzu is recorded as having said ‘know others and know thyself, and you will not be endangered by innumerable battles”.
The header illustration comes form the extensive StrategyAudit toolbox.
Oct 16, 2020 | Analytics, Management, Operations
Certainty in forecasting is the holy grail, being certain of the future means success. However, as we know the only thing we know for certain about the future, is that it will not be the same as the past, or present.
Quantifying uncertainty appears to be an oxymoron, but reducing the degree of uncertainty would be a really useful competitive outcome.
When you explicitly set about quantifying the degree of uncertainty, risk, in a decision, you create a culture where people look for numbers not just supporting their position, but those that may lead to an alternative conclusion. This transparency of forecasts that underpin resource allocation decisions is enormously valuable.
How do you go about this?
- Start at the top. Like everything, behaviour in an enterprise is modelled on behaviour at the top. If you want those in an enterprise to take data seriously, those at the top need to not just take it seriously, but be seen to be doing just that.
- Make data widely available, and subject to detailed examination and analysis. In other words, ‘Democratise’ it, and ensure that all voices with a view based on the numbers are heard.
- Ensure data is used to show all sides of a question. In the absence of data showing every side of a proposition, the presence of data that emphasises one part of a debate at the expense of another will lead to bias. Data is not biased, but people usually are. In the absence of an explicit determination to find data and opinion that runs counter to an existing position, bias will intrude.
- Educate stakeholders in their understanding of the sources and relative value of data.
- Build models with care, and ensure they are tested against outcomes forecast, and continuously improved.
- Choose performance measures with care, make sure there are no vanity or one sided measures included, and that they reflect outcomes rather than activities.
- Explicit review of the causes of variances between a forecast and the actual outcomes is essential. This review process, and the understanding that will evolve will lead to improvement in the accuracy of forecasts over time.
Data is agnostic, the process of turning it into knowledge is not. Ensure that the knowledge that you use to inform the forecasts of the future are based on agnostic analysis, uninfluenced by biases of any sort. This is a really tough cultural objective, as human beings are inherently biased; it is a cognitive tool that enables us to function by freeing up ‘head space’ reducing the risk of being overwhelmed.
Consistent forecast accuracy is virtually impossible, but being consistently more accurate than your competition, while very tough, is not. Forecast accuracy is therefore a source of significant competitive advantage.
Header cartoon courtesy Scott Adams and his side-kick, Dilbert.
Forecast in cartoons
Oct 12, 2020 | Analytics, Marketing
It is easy to define the value of a piece of machinery. It is the revenue generated by the machine, divided by the costs to generate that revenue.
Accounting with the benefit of hindsight is easy. It is not so easy when forecasting what the future value may be. Forecasting when the impact of the many relevant variables can only be estimated is an exercise in fortune telling. Quantifying these relative unknowns to allocate a numerical ‘value’ becomes a task with several parts.
- Defining the factors that may impact the calculation
- Allocating a relative weight to all the identified factors
- Determining the ‘base’ figure from which to build the numbers that enable a calculation.
- Repeating the above process for all the costs involved.
The calculation is then easier:
Value = weighted benefit 1 x weighted benefit 2 x weighted benefit 3: divided by:
weighted cost 1 x weighted cost 2 x weighted cost 3.
It becomes way harder when setting out to value an intangible asset, such as the value of a brand. For example, a pair of sunglasses purchased in a general retailer for a fraction of the price of an almost identical pair, apart from a brand, sold through a specialist optical retailer. Too many, the more expensive branded glasses represent value for a range of emotional reasons, to others, they would be a rip-off.
At some point early on, and subjected to continuing evolution based on experience and research, you need to be able to identify the factors that add value to a target customer, and their relative contribution to the end result.
Always, the complicating factor is context.
I need a new computer, this one is getting a bit old, and while it still does the job well, at some point, something will reach the end of its life, and ‘poof’, gone. At that point the context changes, as does the value equation.
What was something needed but not urgent, that had a calculable value, suddenly becomes a whole new game, as I need the new computer: Now!
A whole different value equation!
The variables may be the same, but the relative weights have changed dramatically, determined by context.
