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Demand forecasting. It may sound complicated, but with the right tools and resources it’s an easy way to strengthen your stock control. In fact, demand forecasting holds the key for effective inventory management – ensuring you always carry the right amount of stock.
So, how exactly can forecasting demand improve stock control for your business?
In this guide, we shine a spotlight on the ins and outs of demand forecasting and how it can bolster stock control. We’ll dive deep into the techniques associated with the process and end on how you can easily forecast demand with inventory management software.
At its core, demand forecasting resides in the field of predictive analytics. It enables you to understand and predict demand so that you can optimise your inventory and ensure that you always carry the correct amount of stock.
In other words, understanding how demand fluctuates prevents issues like overstocking and stock outs, all of which can be a financial drain for your business.
That’s not all though.
Critical business decisions, such as annual turnover, profit margins and cash flow, are all highly dependent on accurate demand forecasting.
If you don’t have the ability to predict demand, then you may risk poor decision-making regarding your products and markets. This can impact your inventory holding costs negatively, decrease customer satisfaction rates and reduce your overall profitability.
While there are multiple ways you can forecast demand (from manual calculations to automated inventory forecasting systems), elements of an effective forecast include:
In addition to inventory control, demand forecasting methods can also be used to support production planning, new market potential and future capacity requirements. However, this article will focus primarily on the use of the iterative process for inventory and stock control.
Making use of demand forecasting ensures that you are never out of stock. Therefore, you decrease the risk of a potential customer purchasing a similar product from a competitor.
What’s more, taking advantage of forecasting techniques for future selling periods allows you to alter and optimize your marketing strategies in order to satisfy expected demand.
Engaging with demand forecasting techniques allows you to optimize your inventory management and stock control much more effectively.
As a result, it’s likely that your inventory turnover rates will increase and any associated carrying costs will decrease due to ensuring the right amount of stock at all times.
As sales forecasts can provide insight into upcoming cash flow, you are better able to budget in order to pay your suppliers and foot any other operational costs involved.
What’s more, the more accurately you can forecast demand, the craftier you can be with your marketing spend. For example, shifting between paid marketing and organic marketing to match slow and busy selling periods to encourage even more sales.
In other words, demand forecasting enables you to invest in the growth of the business.
Thanks to the wealth of data insights that can be extrapolated from accurate forecasting, you are less likely to make poor decisions regarding your products, markets and customers.
If you choose not to make use of demand forecasting methods, then you may find it’s to your own detriment. Your inventory holding costs, customer satisfaction rates, supply chain management and overall profitability are likely to be at risk.
Accurate demand forecasting gives insight into when you need to increase your picking and packing teams(e.g. hiring seasonal temps) and other resources to ensure that your operations run smoothly during peak selling periods.
In other words, forecasting demand can help you to build accurate schedules and reduce the risk of under or over staffing – a key advantage as staffing is a major cost driver.
An awareness of product demand and how to predict it accurately is imperative to ensure efficiency between your manufacturers, your suppliers and your business.
Sales forecasting enables you to better schedule production, warehousing and shipping. In turn, this gives insight into the best time to schedule any necessary maintenance shutdowns throughout the year, away from peak selling periods.
Understanding the peaks and valleys of product demand can help you manage your cash flow more effectively.
As you’ll likely know, poor cash flow management can lead to an inability to pay your manufacturers and suppliers – which may put you at risk of being cut off.
Instead, knowledge of demand can help you negotiate credit terms, reserve cash or negotiate short-term loans in advance.
Broadly speaking, demand forecasting can be split into two main categories:
While we explore the techniques associated with these types of demand forecasting in more detail in the next section, both can work upwards and downwards in the supply chain.
These two categories can also be split further into the following forms:
Qualitative demand forecasting enables you to apply knowledge of your business, market, product or customer to make a judgement call on your anticipated sales forecast.
Primarily, this type of demand forecasting is based upon:
In terms of opinion data, one of the most common qualitative demand forecasting techniques is known as the Delphi method.
This is where a panel of specialists is questioned on a particular situation. A forecast is established based on their individual documented opinions.
A disadvantage to this approach (and the vast majority of qualitative demand forecasting methods) is that it’s not based on real, statistical sales data nor historical trends. You are unlikely to establish accurate product demand from this technique.
As you’ll likely know, market research targets your customer demographic.
It helps to estimate market sentiment and can generate a demand forecast based on various hypotheses. Of course, the data insights you can glean are likely to be vague when it comes to effective inventory control.
Unlike the Delphi method, this process assumes a group of experts will result in more accurate predictions; there are no mediations and the panelists themselves are responsible for coming up with a conclusion in regard to the predicted forecast.
As you may have guessed, qualitative demand forecasting techniques aren’t specific to your inventory. Instead, these methods are based on broad and subjective data.
Consequently, the question of accuracy comes under fire with these methods.
That said, it doesn’t mean that this type of demand forecasting isn’t useful. Rather, it could be used to help forecast new product sales where there is no historical data available. Qualitative methods can also be used to predict sales for a new market.
In addition to the above issues, qualitative forecasting usually comes at a much higher cost than its counterpart and can take two or three months minimum to be developed properly.
In comparison, quantitative demand forecasting uses statistical data based on historical demand or relationships between variables (e.g. trend projection). Such methods use data that is collected over a period of time to generate an accurate demand forecast.
There are two main types of quantitative demand forecasting:
Time series forecasting is largely based on chronologically ordered historical sales data.
These forecasts are made up of components that repeat themselves, such as demand trends, seasonality and sales cycles.
In other words, time series forecasting techniques can help a business to identify cyclical patterns, growth rates and identify any irregularities or variations in datasets.
Time series data sets can be composed of the following trends:
These trend projections are based on the assumption that the factors that contribute to past trends will continue to play a role in the future, in precisely the same way.
Some of most commonly used time series forecasting techniques include:
Such forecasting methods work best for short to medium predictions for up to a year. Typical use cases include sales forecasts, inventory forecasts and margin forecasts.
Keep in mind that for any forecasting where seasonality is involved, it’s best to have a minimum of two years of data to ensure accuracy when using time series techniques.
Causal forecasting is a demand technique that assumes that the variable being forecast will have a case and effect relationship with one or multiple other variables.
Typically speaking, this method takes into account all the possible factors that may impact the dependent variable. Therefore, the data needed for this type of demand forecasting can come from historical sales data or external areas, such as surveys or other market research.
Some of the most common causal forecasting methods include:
In essence, quantitative demand forecasting takes historical sales data and combines it with a specific formula in order to predict future demand.
However, the above mathematical techniques are very complex and technical.
Working out formulas for each of your products on a regular basis is also highly likely to be a massive time suck, especially if you’re selling more than a few items. Therefore, it’s a good idea to explore how to automate these calculations for improved inventory control.
So, how can you save countless hours and still ensure accurate demand forecasting to strengthen your stock control?
Simple. Through the use of automated inventory management software (IMS).
To start working out what inventory you need, the best thing to do is start with a forecast of your future sales across 30-day, 60-day and 90-day trends based on previous sales velocity and the seasonality of your products.
Your sales velocity is the rate of sales over a time period that doesn’t include any day that you were out of stock of certain products.
Rather than taking your sales average, sales velocity is looked at to help establish your rate of sales when your inventory is fully stocked.
If you don’t remove out of stock days, then you may underestimate your future sales.
Sales velocity (per month)=(365 day sales / # of days in stock across 365 days) x 30 days.
How does inventory management software help with this?
While we can’t speak for every solution available, Linnworks users can easily create demand forecasts for each stock item based on how much they sold the previous year.
To resolve days where your product has been out of stock, Linnworks makes the remaining data up based on the average of the data that you have available.
Seasonality is slightly different to predicted demand as it is the product that dictates when it will sell more. For example, Christmas trees during November and December.
Therefore, an accurate demand forecast for seasonal products must reference trends from the previous 12 months in order to maintain accuracy.
A modern inventory management system can multiply the value of an item by the stock it thinks you’re going to sell, but the product drives the sales rather than the retailer.
Without access to historical sales data, how can you predict demand for new items?
Let’s say you sell trainers (sneakers) and you have a new style that’s just come in. The best thing to do to forecast demand is to look at a similar older product that you expect your sales to be similar to as you won’t have any sales data for the new item just yet.
In an inventory management solution such as Linnworks, you can quickly set your demand forecast to use the historical sales data for an alternative stock item.
This then replicates a demand forecast for your new pair of trainers using the data of your older item, helping you to strengthen your inventory control.
Another challenge to consider is how your marketing promotions affect your sales forecast trajectories. After all, a default forecast only shows sales based on the current trend.
Therefore, if you’re planning a campaign during the forecasted period, you’ll need to increase your prediction; however, if promotions occurred during the period used to calculate the sales velocity, then you may not need to increase the prediction.
Regardless, let’s say you’re planning a spring marketing campaign for March, or perhaps you’re launching your business across a new marketplace or country that month.
With a system like Linnworks in place, you can plot and predict (or import a .CSV file) an increase of 50% additional sales during March and then bring down your sales forecast the following month.
To forecast demand, the software looks at what you sold in March the previous year (e.g. 20 items a day) and adds a 50% increase to this figure automatically for you. Your system will then predict that you will sell 30 items each day for March of the following year.
As you will know by now, a demand forecast looks at your predicted sales for X time period.
Stock replenishment, on the other hand, is the additional amount of stock needed to cover those predicted sales. Both play a pivotal role in strengthening your stock control.
Stock replenishment takes into account the following:
While not strictly related to demand forecasting, knowing exactly when you need to reorder stock and at what quantities plays a fundamental role in maintaining optimum inventory levels so that you’re never overstocked nor out of stock.
With a system such as Linnworks managing your inventory, you can work out the exact stock you need and when you need it for without needing to do any complex calculations.
Opening the ‘My Inventory’ screen in Linnworks, you’ll see three key columns:
Consumption is the key to making the reorder point functionality work. Knowing how much you’ve sold over a set time period gives you a foundation to build a forecast on.
In Linnworks, you can easily see how your daily average consumption for a particular product has changed over a given timeframe (e.g. 90 days).
This gives insight into how your item is selling and it will smooth out things like a spike in sales where you sold lots in one day which then wasn’t replicated on other days.
On top of this, Linnworks automatically calculates the reorder point of each stock item, which helps to ensure you always carry an optimum level of stock for your business.
It displays the stock forecast in a line graph, split into historic data (one color on the graph) and predicted data (a separate color). Your historic data represents the performance of your stock levels over a set time period.
Your predicted sales are then calculated based on your historic data and the system will generate the exact date it believes you will need to reorder stock on, along with the estimated day of arrival, having already taken into account your supplier lead times.
Of course, this barely scratches the surface with how granular you can get with forecasting to improve stock control with inventory management software. To see it all in action, as well as what else such a system can do for you, schedule a demo with one of our specialists.
Regardless of the route you go down, whether it’s qualitative or quantitative and manual or automated, accurate demand forecasting is essential to improve stock control.
From optimizing your inventory to reducing holding costs, there are so many benefits of demand forecasting that it’s an element that you can’t afford to ignore – especially for those of you with visions of a highly profitable and sustainable online business.
Looking for other ways to optimize your profitability and productivity? Check out our comprehensive inventory management techniques resource.
Speak to us to find out how Linnworks can connect and automate your commerce operations so you can capture every revenue opportunity.
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