In 1776 Adam Smith introduced the concept of “division of labour”. In his infamous work “Wealth of Nations” he describes the benefits of dividing tasks among workers and observing a rise in firms’ efficiency as a cause of worker’s ability to concentrate on a single task. If a worker is doing a repetitive job at one position it requires less training, less movement around the factory, saves time and money, leading to economies of scale – a favourite economists’ term meaning that by producing more and more of one good the costs are lower and the profit is higher. Production at its best!
Ford motor factories, Apple products with its “Designed in California, Produced in China” badge, food production and many more have acquired and implemented Smith’s idea rather successfully. Nevertheless, the question “How to improve production?” has been and continues bothering everyone everywhere in the world. Improving production means developing, finding new ways of working, restructuring the old organisation, and simply being more efficient.
Thinking in real terms, apart from economics professors, people nowadays rarely think of the “division of labour”. Instead, we are overwhelmed with digital themed terms: Artificial Intelligence, Machine Learning, Data science or the term that has just started to gain its popularity “Digital Twin” technology. For many, it sounds incredibly tempting, however mysterious. The terms mentioned above are primarily based on data analysis – which is nothing more than looking at numbers, understanding what they represent and drawing some meaningful conclusions based on the results.
Digital Twin applied technology combines all disciplines but is simply based on the simulation (extremely self-explanatory term). The purpose of it is to create the real object digitally, work on improvements, try implementing them on the digital model, check if it makes any difference and in case the outcomes are positive – use the innovation in real life. At first, Digital Twin tech has been widely used in Formula 1 races. Teams would track the activity of car’s selected elements, by looking at data collected by 100+ sensors during the race; temperature, acceleration, pressure, applied forces and shaft speeds. It helps recreate the digital model and by using it analyse complex systems, observe the state of car’s elements as well as try applying the innovative change at the model first to see the possible improvements digitally. It reduces the uncertainty of a sudden deterioration, reduces risks and allows for great performance improvement.
How is this related to Adam Smith?
Economists realised that any activity under any policy or social structure is inevitably split into different actions. Even when there is no distinct division of labour in the company, there is a supply chain, tangible with trucks and factories or intangible with marketing force and sales of the service. Digital Twin technology originally coming from the engineering field can be applied to improve social organisation, work of the institution or a firm. Sensors can be applied to the supply chain – performance of transport, production and work of the machinery, crowd management in the office and many more. If every part of the production had a sensor it would accumulate an enormous amount of internal production data to analyse and find more efficient ways of delivering, producing or selling a good or service. The attempt has recently been made by Deloitte and McLaren. Two companies have decided to enter the market as the providers of the applied-to-supply-chain Digital Twin technology that will help answer the question of “How to improve firm’s performance” in a new, innovative way.
Image: [Capgemini]