Imagine you run a company that specializes in giant novelty jigsaw puzzles. Your puzzles are popular all over the world, so you own a series of warehouses in international locations. That way, when an order comes in from Marseilles, you can ship a box from your nearby European warehouse, greatly reducing the time that the customer must wait. While this system makes sense, it requires skillful planning to run smoothly. You must decide how many of each puzzle type you will stock in each warehouse at any given time. If you stock too few, a rush of orders could wipe out your supply, forcing customers to endure long shipping periods that might make them reconsider how much they want a giant puzzle. Conversely, if the demand for a product you’re storing suddenly drops, you’ll be left with ‘dead stock’ and may be forced to offer those puzzles at a steep discount or even destroy them just to free up room.

The solution to this issue lies in knowing how many of each product you will need to supply at any given time. This process is known as ‘demand forecasting,’ and, as you might expect, it’s easier said than done if you’re working manually. The extent to which you can accurately guess how demand will fluctuate over time is limited, even if it’s not your first year in the novelty puzzle business. For example, you can safely expect a spike in orders around Christmas, when people are looking for novelty gifts for the puzzlers in their lives, and perhaps at the start of the school year, when residence dons start buying decorations to make their dorm floors stand out. However, at other times of the year, it’s much harder to predict demand. How eager for novelty puzzles will people be in November? How will economic trends play in to people’s puzzle-buying habits? If you launch a marketing campaign, how should you adjust your supply in anticipation of boosted sales? Without effective demand forecasting, it’s difficult to know.

Due to this element of difficulty, enormous amounts of time and resources have been dedicated to determining how to make demand forecasting as accurate as possible. The rapid development of technology in recent decades has given rise to techniques such as predictive analytics, a process that employs machine learning to analyze the available data and output reliable forecasts. In the example of the puzzle company, you could use predictive analytics to maximize profits and ensure that you never face the tragic image of a stack of novelty puzzles gathering dust in a dark warehouse, but there are few limits to the range of fields in which this technology can be applied. To provide just two examples, Terrene’s sophisticated predictive analytics engine has been used to forecast demand for car parts and predict trends in electricity use with impressive accuracy. Based only on information about a company’s past performance, our technology can generate a demand forecasting model that will provide specific, actionable insights with no programming knowledge required on the part of the user.

For more information about demand forecasting and the work Terrene does, visit our website or get in touch.