Understanding the science of business

Published: 05 May 2014

A large number of entrepreneurs focus on their gut feel and intuition to get ahead in the game. This may not be the only or best approach, since much can be drawn from science, theory and research.

Cognitive research shows that what we experience as spiritual competencies such as intuition, the power of purpose and instinctive responses, is often just the quick processing of lots of subconscious information. This allows us to recognise underlying patterns in systems below the surface of normal cognition. This implies that you act in a certain way – because you have seen it before, read it somewhere or reflected on a similar situation.

It is tempting to act intuitively at all times without taking the time to train your mind to better patterns of thought and development. It is also tempting to run the business without numbers or controls and to simply rely on doing things the same way as before. This may work for some, but as businesses grow it is important to continue processing at the required level of complexity to get all the parts moving. The brain is an amazing tool for this complex type of analysis. Many argue that this is what indeed makes us so uniquely human.

Using analytics for making better decisions
While the ability to draw deeply from one's own thoughts may take years to perfect, analytical tools can also break down problems to allow for better holistic reasoning. If we understand the underlying parameters and their implications, we can reach better decisions to consider the limitations. Tools such as root cause analysis, modelling and forecasting indicate what will happen in the future and this creates a clearer idea of the constraints. We can then develop clearer strategies by considering the given parameters.

This type of 'innovation within boundaries' is something that no amount of modelling or analysis can reveal. It is simply easier to reason and reach the best solution for the business when the data is presented clearly.

With the advent of big-data and a society that is both data oriented with lots of computing power for analysis, new patterns and structures are being revealed. Previously considered intuitive or implied relationships are now confirmed by large-scale research and data sets that combine to reveal non-intuitive patterns.

Most interesting in the area of managerial economics and management sciences are the developments that happened as the result of disciplines such as project management, cost accounting, quality management, six sigma, value engineering and process and quality control taking root. These analytical disciplines have taken manufacturing from a haphazard activity to a precise and perfectible exercise. The application of the same principles to services yielded similar results. Large companies have used these approaches to achieve efficiencies where none were apparent or available intuitively. There are many examples where a better risk formula; a better production layout or different approaches to the value chain have disrupted industries and created value.

The use of analytics for results
Most managers frown when they are confronted with ideas such as linear programming, stochastic modelling, policy optimisation and business model simulation. Yet, in real terms, the complex interactions in large-scale businesses require these analytical dimensions to create new insights for actionable results. How processes can be optimised and where the next breakthrough will come from, actually determines the innovation horizon of the modern business.

It is also important to realise that terminology such as internal rate of return (IRR), weighted average cost of capital (WACC), net present value (NPV) and others represent well-formulated theories and tools that good managers use to decide where to go to next. While every industry has its own specific terminology, it is always important to understand what is behind those measures and norms, when to push the limit, and when to apply standards.

So, while many managers are people-oriented individuals who shy away from analysis, with terms such as 'analysis paralysis' being bandied about, we must remember that management has always been a science. Modern management started to observe concepts such as labour division efficiency, modernised production systems, and the effects of environment and structure on productivity and worker inspiration.

Major questions remain unanswered in management science. Also lacking here is a comprehensive analysis on the impact of specific interventions on the performance (financial and otherwise) of the firm. We are getting better even though no holistic theory of the firm has been developed. In part the challenge is that academic research lags corporate research due to the better funding (driven by the profit motive) that is present in the corporate sector.

Innovation and design thinking is another frontier that highlights the need for analytics and data driven design. Substantial consumer preference information is available. Analysing this rich data source leads to a better understanding of preferences and ultimately delivers better products and services to consumers.

We can never forget that customers, employees and shareholders remain human. No amount of analytical thinking will change that. It does mean that we need to master our spiritual and emotional competencies to effectively manage and build purposeful teams and organisations. We can use analytics to better understand aggregate behaviour, but we must also be careful not to ignore the different, the strange, the unexplained and the unanalysed. We must also not err on the side of ignoring the facts and relying on charisma, intuition and good people skills to drive our results.

Conclusion
The aim of this week's newsletter was to show that management remains a science. While it is often easy to get lost in the detail of day-to-day management, it is important to sometimes question what is going on here. In the end, scientific methods in business still makes sense of complex arrangements.
- Regenesys

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