Inventory Forecasting: An Introduction for Business Owners

Manager and staff reviewing inventory

Inventory management is one of the less-talked-about aspects of running a business, yet it is one of the most crucial elements of your day-to-day operations. If you are a new business owner coming to grips with the daily realities of stock management, things can get daunting pretty fast. 

In plain terms, inventory management will always remain a significant struggle for businesses due to the various internal and external factors that can affect your demand for the stock. However, mastery over inventory forecasting can undoubtedly help make the struggle less painful.

With this in mind, we've compiled an in-depth guide on how to forecast inventory, including a brief look at some of the methods, software and models involved. Therefore, if you're brand new to the concept, this is an excellent place to start. 

What is Inventory Forecasting?

Forecasting involves various techniques used by managers to make accurate predictions about future stock demands in business. Its primary focus is removing any element of guesswork involved in the costly process of placing orders with your suppliers.

This is important because unused inventory can be a significant financial and logistical drain on your organisation. With inventory forecasting, you can aim for quick refill of orders while minimising the quantity and duration of idle stock.

It will also help you answer the two most pressing questions faced by any business owner involved in the manufacture and sales of merchandise, the first of which is:

How much stock should I order in the immediate future? 

This is a fundamentally important question in business, and the answer is dependant on several factors, including past data on demand for stock in your organisation, the expenditure involved in procuring it (fixed costs), and the cost of the storage and management of stock purchased (carrying costs). 

Using these factors and a mathematical formula, you can calculate the economic order quantity (EOQ) – the ideal amount of stock your business should order to keep costs to a minimum while satisfying demand.

The second question is:

When should I place the said order at my suppliers?

The answer to this query lies in what is called a "reorder point" in inventory management. It essentially determines the timing of your next order and is the point in time when your inventory reaches the level where it becomes necessary to make your next order. 

It takes into account the following factors: the time taken by your vendors and suppliers to deliver the stock (the lead time), and the current daily usage rate on your existing inventory. The formula then involves multiplying these two factors.

The aim is to have enough stock in your inventory to last your business demands until the new stock arrives after the lead time waiting period. 

The Need for Advanced Approaches to Forecasting

The above formulae and techniques depend almost entirely on past data; that is, you are basing your future inventory demand calculations on previous transactions. The inadequacy of this model in a fast-moving, dynamic environment should be abundantly clear. 

After all, companies grow and shrink; consumer demands hit peaks and troughs; new products are unveiled, and new rules come into play: many things can happen in business.

Although this approach – known as "naive forecasting" – isn't perfect, it does not mean that it is completely useless, though. It provides an idea of the average demand for inventory in your business, and it can be used as a baseline against other, more advanced, forms of forecasting.

Demand Forecasting and the Importance of External Variables

Your business organisation does not exist in a vacuum, nor does it function independently of other agencies and organisations. Many external factors can affect the sales and inventory demand in your organisation. 

Factors like new competition, advances in technology, seasonal changes in consumer demand, production and labour issues, problems with your vendors, macroeconomic factors (such as inflation and recession), new laws: all of these can have a significant impact on future inventory demands. 

This is why demand forecasting is essential. It tries to take into account all these external variables. However, given the complex array of potential factors, it is impossible to have a single technique or approach that covers them all. 

As a result, demand forecasting can be split into many different techniques, all of which fall broadly under two major categories.

Quantitative and Qualitative Techniques

As its name suggests, the quantitative approach focuses heavily on number crunching but differs from naive forecasting by including various external variables as well. The one major limitation of a purely quantitative approach is its reliance on existing data. This can be a hindrance if you do not have any relevant numbers available, which is likely to be the case if you are launching a new product into the marketplace, for example. 

In these instances, a qualitative approach may be necessary. Instead of overly relying on numbers, this method tries to derive meaningful insights from more subjective sources of data. These can include extracting opinions from experts, analysing consumer behaviour and psychology, and utilising executive experience. 

In larger organisations and conglomerates, managers often rely on a combination of quantitative and qualitative techniques to arrive at a complete picture regarding future inventory demands. 

Inventory Forecasting Techniques

Both the qualitative and quantitative approach account for at least half a dozen forecasting techniques and, while it is impossible to give a detailed analysis of each one here, we can take a brief look at some of the more prominent variants:

  • Exponential Smoothing is a quantitative technique which assigns an extra weight to specific data points from your past. For example, if your forecasting is based on the last 12 months of inventory data, then you may want to add more importance to data from specific months (such as festive periods), especially if you think that they might provide a more accurate reflection of upcoming months.

  • Box Jenkins is another highly advanced quantitative technique that predicts data within a time series.It uses programmed software to create short term forecasting under 18 months and is considered relatively competent at deciphering seasonal variations and trends. 

  • Data mining is a relatively recent technique, facilitated by the rise of big data. Companies can use complex algorithms and artificial intelligence to uncover patterns within data for forecasting future demand.

  • The Delphi method is a qualitative technique based around input from a group of experts. Instead of having just one round of contributions, a facilitator will send multiple rounds of questionnaires to the group, with individuals given the freedom to change their opinions each time. This technique is better suited to long-term forecasting.

  • Executive opinions are precisely what they sound like. Instead of depending solely on numbers, you take opinions from expert advisors and seasoned company veterans. Usually, the views of these executives are based on hard data they have acquired from their respective departments. This is a qualitative technique that can deliver penetrating insights on inventory and sales patterns. 

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Inventory forecasting is a highly advanced and specialised field within business management, and it is recommended that small business owners start with basic methods of quantitative analysis in the early days of their business. Other advanced techniques should only come into play once the company grows in size and complexity. Note, as well, that you can often find software calculators for mathematical forecasting techniques online.

What do you think? What is the best approach for inventory forecasting in your opinion? Let us know in the comment section below!