Knowledge-based theories that deal with corporate performance include:
- scientific management;
- brainstorming and mind maps;
- systems thinking;
- evidence-based management.
In my earlier days I was fascinated by scientific approaches to management such as 'management science' and 'operational research'. These approaches go back to the Second World War when the Allies sought to optimise military and logistical activities using statistics and modelling.
Linear programming was applied to manufacturing constraints and offered an elegant mathematical model that optimised two variables - for example, price and cost.
Queuing offered a statistical way of simulating queues to improve flows in manufacturing services and retail; the use of multichannel service points fed by a single queue in banks and post offices is an example of it in action.
Much of this work was focused on identifying, testing and getting around or minimising constraints. The other big focus was on optimising the physical environment - for example, in delivering low/no stock control, popularised under the title of 'just-in-time'.
These theories appear to provide logical solutions to problems that deliver better performance, and they can work well in many operational situations as long as the definition of the problem and the variables is clear-cut and simple.
But in many business situations the problem involves many variables and is much more ambiguous and fuzzy. This is where softer modelling of the problem can work better: for example, by using multi-criteria decision tools such as the strategic option grid described in my first series of articles.
Building more complex business and financial models through scientific management has been with us a long time. When I was a consultant at KPMG some 27 years ago, we were building Lotus 123 spreadsheets for business plans and to raise capital.
Back then we were doing P&Ls and cashflow forecasts driven by market and operating assumptions that we could flex under different scenarios and for different strategies in order to create new business models, test different combinations of resources, and play with market size, growth rate and relative market share.
Many of these models are still relatively simplistic in modelling the economic source of the assumptions. For instance, relative market share (which, times the market size, gives 'sales') may be a quantified percentage, but this variable is derived in turn from:
- relative competitive advantage;
- relative investment in marketing, advertising and specifically relative price;
- the effectiveness of that investment and of others.
It also means building up a complicated and quite detailed picture of competitors and the relative fit of each to different segments of the market.
All the strategic modelling software that I have come across faces a tough challenge in integrating the financial, operating, marketing and strategic worlds.
A softer way of dealing with these kinds of challenges is brainstorming, which is about generating ideas by association.
Brainstorming is a great way of generating lots of ideas in a short time. It stimulates team energy and is generally found enjoyable and fun. One of the key rules is not to criticise ideas as they emerge, since they might lead on to other ideas. It's important not to interrupt the creative process and its flow.
The brainstorming term was popularised by Alex Osborn in his 1953 book Applied Imagination. It was succeeded by Edward de Bono's work on 'lateral thinking', which involves looking at problems from different perspectives.
The downsides of the brainstorming/lateral thinking approach are:
- most of the ideas are disconnected and need quite a lot of sorting out (this is mitigated if there are some categories that the ideas can be put into as they are generated);
- many of the ideas are not of very high quality but it can be difficult to discard them as people may be precious about 'that was my idea';
- it is very easy to lose sight of the original objective and it can become an end in itself.
I remember a Father Ted episode where someone puts a bomb in a milk float that will go off if the vehicle stops. Father Ted responds to the crisis by brainstorming the options with a flipchart. Hours pass as the brainstormers become engrossed in their ideas and eventually they hit on the idea of putting a brick on the accelerator because it's less effort than driving the float around in circles!
To provide a little more structure, brainstorming can extend into mind mapping. Here you draw a picture of the components of an issue or problem and then draw lines to represent interdependent relationships. It's a useful technique with the proviso that as it is a causal map from the perspective of a particular person or group it may not make as much sense to others. A fair bit of work may be needed to draw out the tacit reasoning behind a mind map.
My own optopus, which I described in my first series of articles, is a semi-structured form of brainstorming/mind mapping.
Problems the accountant could use this for include:
- understanding any decline in performance;
- the drivers of economic profit for a new area of strategy development;
- post-acquisition integration issues.
Systems thinking is the visualisation of cause and effect relationships using mind mapping. It is a process of mapping complex and interdependent causal chains to visualise the future behaviour of a system.
Peter Senge formalised the theory in The Fifth Discipline. He highlights the interdependent nature of organisations and their issues. Whereas scientific management works to a very engineering and physical type of model, Senge takes a far more organic approach.
Systems thinking is also useful in conjunction with scenario story-telling - covered in the first article of this series. For example, some years ago I spent half an hour brainstorming some of the key systems affecting the UK do-it-yourself market, which was in a slump. I came up with 29, including economic growth, house prices, house moves, etc. I clustered these on Post-its on a flipchart and drew in causal interdependences, their direction and whether they were strong or weaker. Finally I assumed a transitional event - renewed economic growth.
I then used the picture to visualise what might then happen, along with any feedback loops. The insight was that this market could easily take off again - as indeed it subsequently did.
Evidence-based management is a discipline that comes from the scientific and legal worlds.
Much decision-making is done in many organisations by instinct, intuition and gut feel. This may be a necessary process for moving forward but can pose a huge danger in that decisions may be taken without a solid grounding in the issues and the facts. While evidence can be time-consuming to collect, mistakes can be even more time-consuming - plus very expensive - to correct.
As a strategy consultant/facilitator, I am very familiar with the need to assemble and evaluate data on, for example, 'where we are now' via management workshops. Sometimes you even have to go out specially to collect data on markets and competitors as well as operational and financial data. This data has to be handled efficiently and effectively and I often recommend doing a short 'position paper' to assemble, interpret and evaluate it.
Another reason I find evidence-based management appealing comes from my experience of supervising MBA projects. Part of an MBA project involves conducting systematic empirical work on a particular, challenging topic and I have observed that the quality of the thinking can double or treble as a result of using evidence-based management.
Dr Tony Grundy is an independent consultant and trainer, and lectures at Henley Business School