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Global Advisors’ Thoughts: Should you be restructuring (again)?
By Marc Wilson
You don’t take a hospital visit for surgery lightly. In fact, neither do good surgeons. Most recommend conservative treatment first due to risks and trauma involved in surgical procedures. Restructuring is the orthopaedic surgery of corporate change. Yet it is often the go-to option for leaders as they seek to address a problem or spark an improvement.
Restructuring offers quick impact
It is easy to see why restructuring can be so alluring. It has the promise of a quick impact. It will certainly give you that. Yet it should be last option you take in most scenarios.
Most active people have had some nagging injury at some point. Remember that debilitating foot or knee injury? How each movement brought about pain and when things seemed better a return to action brought the injury right back to the fore? When you visited your doctor, he gave two options: a program of physiotherapy over an extended period with a good chance of success or corrective surgery that may or may not fix the problem more quickly. Which did you choose? If you’re like me, the promise of the quick pain with quick solution merited serious consideration. But at the same time, the concern over undergoing surgery with its attendant risks for potential relief without guarantee is hugely concerning.
No amount of physiotherapy will cure a crookedly-healed bone. A good orthopaedic surgeon might perform a procedure that addresses the issues even if painful and with long term recovery consequences.
That’s restructuring. It is the only option for a “crooked bone” equivalent. It may well be the right procedure to address dysfunction, but it has risks. Orthopaedic surgery would not be prescribed to address a muscular dysfunction. Neither should restructuring be executed to deal with a problem person. Surgery would not be undertaken to address a suboptimal athletic action. Neither should restructuring be undertaken to address broken processes. And no amount of surgery will turn an unfit average athlete into a race winner. Neither will restructuring address problems with strategic positioning and corporate fitness. All of that said, a broken structure that results in lack of appropriate focus and political roadblocks can be akin to a compound fracture – no amount of physiotherapy will heal it and poor treatment might well threaten the life of the patient.
What are you dealing with: a poorly performing person, broken processes or a structure that results in poor market focus and impedes optimum function?
Perennial restructuring
Many organisations I have worked with adopt a restructuring exercise every few years. This often coincides with a change in leadership or a poor financial result. It typically occurs after a consulting intervention. When I consult with leadership teams, my warning is a rule of thumb – any major restructure will take one-and-a-half years to deliver results. This is equivalent to full remuneration cycle and some implementation time. The risk of failure is high: the surgery will be painful and the side-effects might be dramatic. Why?
Restructuring involves changes in reporting lines and the relationships between people. This is political change. New ways of working will be tried in an effort to build successful working relationships and please a new boss. Teams will be reformed and require time to form, storm, norm and perform. People will take time to agree, understand and embed their new roles and responsibilities. The effect of incentives will be felt somewhere down the line.
Restructuring is often attempted to avoid the medium-to-long-term delivery of change through process change and mobilisation. As can be seen, this under-appreciates that these and other facets of change are usually required to deliver on the promise of a new structure anyway.
Restructuring creates uncertainty in anticipation
Restructuring also impacts through anticipation. Think of the athlete waiting for surgery. Exercise might stop, mental excuses for current performance might start, dread of the impending pain and recovery might set in. Similarly, personnel waiting for a structural change typically fret over the change in their roles, their reporting relationships and begin to see excuses for poor performance in the status quo. The longer the uncertainty over potential restructuring lasts, the more debilitating the effect.
Leaders feel empowered through restructuring
The role of the leader should also be considered. Leaders often feel powerless or lack capacity and time to implement fundamental change in processes and team performance. They can restructure definitively and feel empowered by doing so. This is equivalent to the athlete overruling the doctors advice and undergoing surgery, knowing that action is taking place – rather than relying on corrective therapeutic action. A great deal of introspection should be undertaken by the leader. “Am I calling for a restructure because I can, knowing that change will result?” Such action can be self-satisfying rather than remedial.
Is structure the source of the problem?
Restructuring and surgery are about people. While both may be necessary, the effects can be severe and may not fix the underlying problem. Leaders should consider the true source of underperformance and practice introspection – “Am I seeking the allure of a quick fix for a problem that require more conservative longer-term treatment?”
Photo by John Chew
Strategy Tools
Strategy Tools: Opportunity/vulnerability matrix – “The Bananagram”
Logic suggests that high relative market share (RMS) should translate into higher profitability (unless the firm was not using its potential advantages or pricing to penetrate the market further). This suggests that a “normative curve / band” exists to describe this phenomenon i.e. the expected profitability of the average business segment in a particular industry according to normal expectations conditional on the segment’s relative market share. This normative band is shown in the figure below as the area between the two curves.
The Opportunity / Vulnerability Matrix
The curve is best explained using data / businesses that have been correctly segmented. In practice such data can only be obtained after analysing the organisation and having a good understanding of any relationships. The band used to be shown coloured yellow, hence the chart became known as a “bananagram”.
The implication of the curve is that high relative market share positions, correctly segmented, are valuable segments / businesses. Managers should therefore strive to achieve / participate in these segments / businesses.
Another implication, in some ways obscured focusing primarily on the growth share matrix (especially where “dogs” are concerned), is that it is useful to improve relative market share in a business segment whatever the starting position. The bananagram enables one to calculate a rough estimate of the equilibrium profitability to be expected from any particular position (relative market share). Therefore it is possible to estimate the potential benefit of moving any particular segment position against the cost of doing so – extra marketing spend, product development or lower prices. This allows one to quantitatively assess whether it is worth trying to raise RMS and which segment / business investments give the best return to shareholders.
Empirical evidence suggests that the majority of observations would fall between the two curved lines and it would be unusual for businesses to fall outside this band. There are two possible positions where a business segment can find itself outside of the two curved lines – this is depicted in the figure below.
The Opportunity / Vulnerability Matrix – Example
Business A is earning (for example) 45 per cent return on net capital employed, a good return, but is in a weak relative market share position (say 0,5x, or only half the size of the segment leader). The theory and empirical data from the matrix suggests that the combination of these two positions is at best anomalous, and probably unsustainable. Business A is therefore in the “vulnerability” part of the matrix. The expectation must be that in the medium term, either the business must improve its relative market share position to sustain its profitability (the dotted arrow moving left), or that it will decline in profitability (to about break-even). Why should this happen? Well, the banana indicates that the market leader in this business may well be earning 40 percent or even more ROCE in the segment. What may be happening is that the leader is holding a price umbrella over the market, that is, is pricing unsustainably high, so that even the competitors with weak market share are protected from normal competition (especially where pricing is concerned). What happens if the market leader suddenly cuts prices by 20 percent? They will still earn a good return, but the weaker competitors will not. The leader may opt to provide extra product benefits or services, instead of lowering prices, but the effect would still be a margin cut. It is as well to know that business A is vulnerable. If relative market share cannot be improved, it is sensible to sell it before the profitability declines.
Now let’s look at business B. This is a business in a strong relative market share position – the leader in its segment, five times larger than its nearest rival. It is earning 2 percent ROCE. This is a wonderful business to find. The theory and practical data suggest that such a business should be making 40 percent ROCE, not 2 percent. Nine times out of ten when such businesses are found, it is possible to make them very much more profitable, usually by radical cost reduction (often involving restructure), but sometimes through radical improvement of service and product offering to the customer at a low extra cost to the supplier, but enabling a large price hike to be made. Managements of particular businesses very often become complacent with historical returns and think it is impossible to raise profits in a step function to three, four or five times their current level. The bananagram challenges that thinking for leadership segment positions, and usually the bananagram is proved right. After all, high relative market share implies huge potential advantages; but these must be earned and exploited, as they do not automatically disgorge huge profits.
Source: Koch, R – “Financial Times Guides Strategy” – Fourth edition – Prentice Hall – page 313-316
Fast Facts
South African retailers have maintained flat margins on lamb and seen declining margins on beef
- Beef producers’ share of retail prices has increased from 43% to 45% from 2000 to 2013 while lamb producers’ share has decreased from 55% to 53%
- Lamb prices have escalated above other meat prices as producers have passed on supplier increases
- Retailers have been unwilling to cushion these increases
- Retailers have cushioned an increase in beef producer prices and taken smaller margins
- Retail prices of beef have risen at a slower rate than producer prices
- Beef consumption is growing with the rise of the middle class while lamb consumption is declining
- Demand for beef is higher than lamb due to affordability
- Retailers are willing to take less margin on beef in order to maintain foot traffic through their stores
Selected News
Term: Model weights
“Model weights are the crucial numerical parameters learned during training that define a model’s internal knowledge, dictating how input data is transformed into outputs and enabling it to recognise patterns and make predictions.” – Model weights
Model weights represent the learnable numerical parameters within a neural network that determine how input data is processed to generate predictions, functioning similarly to synaptic strengths in a biological brain.1,2,4 These values control the influence of specific features on the output, such as edges in images or tokens in language models, through operations like matrix multiplications, convolutions, or weighted sums across layers.1,2,3 Initially randomised, weights are optimised during training via algorithms like gradient descent, which iteratively adjust them to minimise a loss function measuring the difference between predictions and actual targets.1,2,5
In practice, for a simple linear regression model expressed as y = wx + b, the weight w scales the input x to predict y, while b is the bias term.2 In complex architectures like convolutional neural networks (CNNs) or large language models (LLMs), weights include filters detecting textures and fully connected layers combining features, often numbering in billions.1,2,5 This enables tasks from image classification to real-time translation, with pre-trained weights facilitating transfer learning on custom datasets.1
Weights are distinct from biases, which add normalisation and extra characteristics to the weighted sum before activation functions, aiding forward and backward propagation.3,6 Protecting these parameters is vital, as they encode the model’s performance, robustness, and decision logic; unauthorised changes can lead to malfunction.5 In LLMs, weights boost emphasis on words or associations, shaping generative outputs.3
Key Theorist: Geoffrey Hinton
The preeminent theorist linked to model weights is **Geoffrey Hinton**, often called the ‘Godfather of Deep Learning’ for pioneering backpropagation and neural network training techniques that optimise these parameters.1,2 Hinton’s seminal 1986 paper with David Rumelhart and Ronald Williams popularised backpropagation, the cornerstone algorithm for adjusting weights layer-by-layer based on error gradients, revolutionising machine learning.2,4
Born in 1947 in Wimbledon, London, Hinton descends from a lineage of scientists: his great-great-grandfather George Boole invented Boolean logic, his grandfather Charles Howard Hinton coined ‘hyperspace’, and his great-uncle was logician Bertrand Russell. Initially studying experimental psychology at Cambridge (BA 1970), Hinton earned a PhD in AI from Edinburgh in 1978, focusing on Boltzmann machines-early stochastic neural networks with learnable weights. Disillusioned with symbolic AI, he championed connectionism, simulating brain-like learning via weights.
In the 1980s, amid the first AI winter, Hinton persisted at Carnegie Mellon and Toronto, developing restricted Boltzmann machines for unsupervised pre-training of weights, addressing vanishing gradients. His 2006 breakthrough with Alex Krizhevsky and Ilya Sutskever-training deep belief networks on ImageNet-proved deep nets with billions of weights could excel, sparking the deep learning revolution.1 At Google Brain (2013-2023), he advanced capsule networks and transformers indirectly influencing LLMs. Hinton quit Google in 2023, warning of AI risks, and won the 2018 Turing Award with Yann LeCun and Yoshua Bengio. His work directly underpins how modern models, including LLMs, learn weights to recognise patterns and predict outcomes.3,5
References
1. https://www.ultralytics.com/glossary/model-weights
2. https://www.tencentcloud.com/techpedia/132448
3. https://blog.metaphysic.ai/weights-in-machine-learning/
4. https://tedai-sanfrancisco.ted.com/glossary/weights/
5. https://alliancefortrustinai.org/how-model-weights-can-be-used-to-fine-tune-ai-models/
6. https://h2o.ai/wiki/weights-and-biases/
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