Pseudo-Science in Supply Chains

00:35 What is Scientism?
02:18 Are there any real world examples of how people have become over reliant on science?
04:06 In a supply chain, what is an example of things that look easy on the service but, when you get into details, aren’t so rational?
06:49 What’s the alternative?
08:26 How can these oversimplifications impact on a Supply Chain professional? What is the result of them?
10:43 Has anyone noticed the shortfalls of their approaches?
13:36 Humans are naturally risk averse, is there really an alternative to using these scientific approaches?
15:40 You could argue that some of our clients have a fair bit of naive rationalism at Lokad. How do you ensure our clients at Lokad understand everything we do?
18:27 What’s the key lesson? It’s good to have scientific approaches, but don’t they need to be combined with a fair bit of common sense too?


Like most complex systems, supply chains are difficult to comprehend. Most naive measurements, such as the forecasting accuracy, only give a partial view of the problem. As a result, during the 90’s and 2010’s, multiple methods like ABC analysis or safety stocks have persisted while they had neither theoretical grounding nor empirical results to support them.

From airplanes to internet banking, often as humans we have to place our trust in things we don’t completely understand. This overconfidence in science, often referred to as ‘Scientism’, is unfortunately all too frequently seen in the supply chain industry. In this episode of LokadTV, we investigate the various elements of a supply chain that are so difficult to model and try to understand how the “dark art” of forecasting can result in many professionals actually overcomplicating the challenges they are faced with.

Scientism can be said to be a form of naive rationalisation, where a person takes a first-order problem and makes incorrect conclusions without assessing the far reaching consequences.

We discuss just why the supply chain industry is often so happy to rely on mathematical models that they don’t fully understand and whether something that simply looks superficially clever on the surface actually is in reality.

Due to their inherent complexity, it’s very tempting to want to try and take shortcuts in supply chains, using mathematics to easily produce figures and benchmarks. We go in to more detail on why a purely algorithm based approach to demand forecasting doesn’t really function that effectively in reality. For example, because this frequently ends up creating a self-fulfilling prophecy effect.

To wrap things up, we learn how companies can fight the illusion that everything can be solved uniquely with numbers and how instead they can promote the importance of high-level human judgement; to keep perspectives in check, to make sure that visions are aligned and above all remain rational.