Organisations that bring a product or service to the market, whether a small local hair salon, a multinational corporation, or even a government providing public services must make a few key decisions about their offering, such as for instance the price to attribute to the good/service and the quantities to supply If you are a local hairdresser, you likely already have a very good understanding of the going rate for your services, and through basic observation of other salons you can gauge how to operate and how to organise tasks among your staff. However, when we look at the entire economy for a particular good or service, the exercise grows much more complex.

So what motivates a company to supply the market with a good/service? There could be different reasons such as gaining market share but profit is a very important driver. 

Companies make a profit when the price of a good/service is larger than the cost of producing it. When costs are low, and productivity increases, the incentive to expand production grows due to the rising profits. Vice versa, if the costs increase and productivity is low, firms become less motivated to produce at the current market price, instead they might decide to either reduce output or charge higher prices to protect the profit margins. 

Productivity is pretty much a measure of efficiency, measuring how efficiently inputs are converted into outputs. Better/improved technology and/or working methods tend to increase productivity enabling business to produce even more with the same or even lower amount of resources, therefore decreasing the cost per unit of each output. This increases profitability. 

Let's take the example of a hair salon deciding on the number of resources to employ (the variable input). With 5 stylists, a total of 115 clients are served in a week, averaging the output to 23 clients per stylist. The output keeps on increasing with every additional new input of labour. This goes on until a new additional input does not result in any additional output. This could be because the salon can take only so much styling chairs due to limitation of space.

So businesses expanding their workforce will likely see output growing at first, due to specialisation. If we take the example of the hair salon, instead of 1 employee juggling colouring, cutting, cleaning, dealing with consumers etc., each employee can focus on the area they are best at, thus increasing efficiency. Having said that this has a ceiling since with more additional workers added to limited/fixed workspace/equipment, the gains from adding another resource starts to shrink, until at some point there are zero gains. This is known as diminishing marginal returns. The only way to push past this is to increase the fixed inputs. With our hair salon for instance we could take over additional space allowing more styling chairs. This shifts the production frontier outward. 

The Marginal Product (MP) curve shows the extra output created by each extra unit of labour. The point where the MP and Average Product (AP) curves intersect i.e. where MP equals AP is where the additional output is exactly equal to the average. At this point, the AP stops increasing and reaches its maximum. Operating at a point where MP = AP is the highest output per worker, and so an indicator of efficiency. 

MP and AP curves help companies with hiring decisions as it helps identify maximum labour input efficiency, therefore the profit-maximising number of employees. It enables companies to understand when to hire more resources and when to hold. These curves also help companies avoid a stage where MP is 0 or also in the negative. A negative MP is usually caused by too many workers employed with a fixed capital. In our example when too many hairdressers are employed in a confined space with not enough styling chairs, workers start getting in the way of each other leading to a reduction in output. Therefore, this leads to inefficiency and losses. 

Modelling, simulation, and data science offer powerful tools for understanding and optimising these economic relationships. Using techniques such as discrete event simulation, agent-based models, or machine-learning forecasting, firms can explore how changes in labour, equipment, technology, or pricing affect output long before they commit resources in the real world. These methods help reveal bottlenecks, quantify the impact of productivity improvements, and test “what-if” scenarios such as hiring additional staff or adopting new equipment without disrupting day-to-day operations. For economists, these tools provide richer insights into how supply behaves under different conditions, enabling more accurate predictions of market responses and more informed decision-making for businesses and policymakers alike.