Ever Given – Supply Chain Disruption in the Suez Canal
Supply Chain Resilience and Risk
Post Brexit Supply Chains UK-EU
Value, Customers and Service
Complexity and Disruption
7 V’s Explained
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Tower Hill to Sloane Square, Ealing to Oxford Circus, Harpenden to Kings Cross, Leeds to Huddersfield, Salford to Manchester (sometimes), Liverpool to Hooton, Berkely to San Francisco, Melrose to Boston, Johannesburg to Pretoria, Reichstag to Berlin Zoo, Westmead to Sydney, Chicago Central to Southside, New York to Brooklyn. Use your journey time wisely. In the time it takes you to commute you could listen to Chain Reaction on your favourite podcast platform. Try it today it’s free, informative and you might learn something you did not know about.
To paraphrase Roald Dahl this is a tale of the unexpected. We should always think about the unexpected as the norm. Not many things in life go exactly to plan so why are we so surprised when things go wrong? If you are a supply chain professional you know with some certainty that something is likely to disrupt the best laid plans. The response is what matters. What can you do to put things back on track quickly? This is why there is much talk about resilience.
For all those students of supply chain along with professionals that spent hours playing the ‘Beer Game’ you know that disruption is normal. You also have learned from the experience and know that to correct disruption and the causes of it often makes it worse given the actions taken to make adjustment to flows within the supply chain. In other words it is not simple. It is complicated. Would a simple algorithm solve the problem?
The Science of Prediction
If you follow professional sports you will know that the bookies favourite does not always win. This is one reason we like it. In our sporting lives unpredictability makes it exciting, exhilarating and a unique experience. We love the fact that you cannot be sure of the prediction. Why then when we return to our working lives do we expect things to happen as planned? Should we just learn to accept it as a matter of fact and not be too concerned? After all isn’t an exciting, exhilarating and unique experience attractive to make our working lives better?
Well I can already hear some groans from the reader. “Excitement is not what I want” you say. “I prefer things to go smoothly”. This is rather like putting your old slippers on and knowing they are always in the place you left them behind the door as you enter your comfortable life at home. As Benjamin Franklin said ‘A place for everything and everything in its place’. We hanker after orderliness. All else is chaos. Is it because we have been taught that order is the norm when in reality it isn’t?
A place for everything and everything in its place.
Benjamin Franklin
If I study all the form of all the horses in a race why am I still not certain of predicting the winner? If I gather all the data about football teams including team selections, positions, individual player performance metrics about pass completions, possession and times of goals scored and tactics and everything else there is why am I still unlikely to predict a winner with a probability not much better, if at all, than flipping a coin? If all this is true why then do I waste so much energy gathering data, examining variables and computing odds? Is it because it gives me comfort even though it is likely wrong?
Similarly, in business we spend a lot of time and energy gathering data, building algorithms to predict some future outcome and gain some comfort from knowing we have a forecast and a plan. Strange isn’t it that so much faith is put into the data and our manipulative abilities to get a forecast. If it were so simple we would all be winners but the reason we are not is because the unexpected happens.
I often think that modern technology has offered technical solutions many of which are smart and most are certainly of benefit. We must remember, however, that humans design and build the technology in which we place so much trust and faith. The hardware and the software that drive the algorithms containing so many assumptions that are contained in a ‘black box’ which is not transparent and we assume the algorithm is infallable. In the past few years the Big Tech companies have peddled the myth that they have the data to predict everything. It is after all in their interest to do so. As they might say in Yorkshire “There’s brass to be made” that is money, from data and predictions. This claim stated as: ‘We have so much data we are able to predict everything’ maybe correct unless you add a sub clause to the previous sentence, ‘which is always accurate’. Yes they can predict everything given the data but whether or not that data has valid assumptions built into the algorithms used by the forecaster is not clear. Rudyard Kipling’s piece of writing about six serving men ‘why what, where, when, how and who’ spring to mind when I think about purpose, reason, selection, time, technique/method, and who is involved in making the choices. Put differently, who is the chef and what ingredients are included to bake what cake for whom.
Fractals occur naturally in our world essentially as recurring patterns. In mathematics fractals are a subset of Euclidian space. Felix Hausdorff introduced the concept in 1918 recognising geometrical recursions of shapes. For example, of you take a form such as a circle, a square or triangle and repeatedly copy it in scale this is a fractional representation of the original form. These are fractals. This is called ‘self similarity’ Thus, you can create a complex pattern from an original form by repeating it at scale to infinity. Fractals occur naturally in dynamic systems. Examples are all around us such as trees, flowers and various plant life where simple processes repeat patterns into the future. The past creates the present and future. We recognise fractals in fossils and are able to identify the species. We cut a tree stump and see fractals in the ageing rings of the tree. In human biology fractals appear in organs, cells and bodily make-up. In space fractals are created and observed in the stars and galaxies. In computer science Benoit Mandelbrot developed an algebraic equation recognising the reproduction of what has come to be known as a Mandelbrot Set. Patterns can be generated using the set to determine the next number. That is the fractal. The Mandelbrot Set is used to generate infinite copies by repeating the process rendering images of self similarity.
z = z2 + C
A fractal is a concept that has been transferred from the natural world to the business world. For example, investors search for patterns in data to predict share prices. The charts produced examine patterns from the past in the hope of spotting future trends. Of course they are not looking for perfect fractals which in lay terms we would see as an exact match but rather they search for partial fractals that retain the original underlying pattern; the Form.
Learning From the Past
If we step back to Ancient Greece it was the philosopher Plato who discussed Form essentially an idea, a concept. In discussions he refers to human form, animal forms such as cats, dogs, pigs, horses and birds. In each species we can identify form as similarity but we also see differences within form. For example, humans come in different size, shapes, gender with faces that resemble but do not replicate each other. Forms have properties for example birds have two legs, two wings, two eyes and feathers, beyond that we may distinguish different types by size, shape, colouring and sounds; their characteristics. Although we can identify fractals they are not perfect. In business we might observe conditions that resemble past patterns in the present analysis. Resemblence is not exact. It may however, give some indications of what may happen next. In other words we might be able to predict future events building on the trends. Although one can argue as another Ancient Greek philosopher did (Democritus) ‘that you cannot enter the same river twice’ in other words there is no perfect copy when it comes to the natural world. The conditions are always somewhat different.
In the seventeen hundreds Scottish philosopher David Hume raised the problem of induction generating knowledge. Put simply, if you rely on past experience to learn everything about the present it assumes continuity. The problem comes from an event occuring that no one has previously experienced – discontinuity. There are no fractals to examine! There are such examples in the contemporary business world that spring to mind as singular events where there is no experience such as the Tsunami’s in Asia in places like Sri Lanka; The Icelandic Volcanic eruptions grounding aircraft because of black cloud dust in the atmosphere for weeks; the Covid-19 Pandemic and global supply chains held up by the Evergiven ship effectively log jamming the Suez Canal. These are occurrences I would indentify as ‘Black Swan’ moments. These are not events that could be predicted with certainty. You could not have forecast these matters using traditional forecasting techniques or mathematical models – there was no data on which to build the forecast and this is exactly one of the problems with forecasting. This is why short term forecasts yield the highest accuracy, medium term less so and long term they are likely to be highly inaccurate. Unless the past resembles the present and can be extended as a continuity to a future state it is near impossible to predict. You could always try tea leaves, tarrot cards, crystal balls and the dark arts of course.
The Moral of the Tale
When faced with unforseen, unpredictable unique events business people need to be agile, adapt to changing circumstances and offer flexible responses to the problem faced. Agility, flexibility, responsiveness and resillience are concepts that supply chain professionals recognise and they are well placed to make a contribution when disruption occurs. They may not be able to predict the unpredictable but they will be able to respond when it happens.
Forecasting is of course useful to predict demand when you have established patterns for products and services that are likely to be similar in future time periods. When systems are stable it is easier to predict trends. When disruptions occur and the system stability changes it is much more difficult to predict trends and develop forecasts with any accuracy. Jay Forrester’s work on system dynamics discusses this topic in detail. As mentioned earlier generations of supply chain students who played the ‘Beer Game’ learned about system stability and the ‘bullwhip’ effect. Assumptions built into the forecast need to be transparent to determine conditions under which they hold good.
You can listen to my podcast on this topic to find out more.
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