Inventory Forecasting: Methods and Formulas to Avoid Stockout and Overstocking
December 7, 2022
Stockouts and overstocking are not good for business. One is an indication of wasted resources, while the other means wasted opportunities.
All businesses must institutionalize inventory forecasting to prevent losses in any form. It doesn’t matter how big or small the company is or how long it has been in operation. After all, every penny counts.
What Is Inventory Forecasting?
Also known as demand planning, inventory forecasting is the process of accurately predicting inventory levels based on previous data, trends, and upcoming events. The goal is to be as close as possible to maintaining ideal inventory levels regardless of the season, time of year, or changing consumer demand.
The process involves meticulous data gathering and expert analysis. When done right, it can save the business a lot of money while avoiding financial loss.
Inventory forecasting is best complemented with robust inventory management to ensure that all data is accurate and up-to-date.
Accurate inventory forecasting is important because consumer demand is constantly changing. A top seller today may not be popular a week or a month from now, so whatever remains of your inventory will become overstock if you don’t keep close track of trends in your demand planning.
The opposite may happen too: because you didn’t anticipate the demand based on season, you may fail to manufacture or order ample supply to meet customer demand, resulting in stockouts and loss of profit for the business.
Methods of Inventory Forecasting
Predicting consumer demand can be quite a gamble. If you want to make intelligent predictions for accurate inventory forecasts, they should be based on existing data and formulas.
Here are the most common inventory forecasting methods:
1. Quantitative Forecasting
Quantitative forecasting involves using historical sales data to anticipate future sales. Businesses may look at sales numbers from 2021 and 2022 to determine inventory levels for 2023. You can identify trends, see which months have an uptick in sales, and plan to increase stock levels during those periods.
It’s an objective way of predicting trends as the business looks into past sales and market growth. It can be used to estimate sales growth, which helps determine ideal inventory levels.
For example, a clothing retailer determines that sales for red clothing items are always high every February. So, every year, production for red items will be higher to meet higher demand during that month.
Based on that trend, the business could also employ various strategies to ensure that non-red items will also see an increase in sales. The retailer can showcase how a red dress will go well with a silver scarf or how a red jacket will pop with a black T-shirt underneath.
With historical data, you can determine seasonal fads that you can take advantage of to increase profits. It’s ideal to stay on top of trends and fads to remain competitive in your niche.
Ideally, historical data should be visually represented in a graph. When you put the numbers into a line graph, pie chart, or histogram, the trend and historical numbers are clearer, making it easier for the business to make an inventory forecast.
Advantages and Disadvantages of Quantitative Forecasting
The main advantage of using quantitative forecasting is that it’s easy to predict. You just look at your data, and you can predict ideal inventory levels without much complexity.
However, the disadvantage is that wrong data input could jeopardize your entire inventory prediction process. There are also instances when there is no data to work with, as in the case of new businesses.
Quantitative forecasting works best for established businesses with large data samples, likely due to top-notch inventory management. Promotions add variability to demand, and forecasters should remove the demand effects of promotion to have accurate forecasts.
Choosing the proper inventory forecasting method depends on available data that can provide the most accurate forecast. Here are some metrics that should be on hand:
- Inventory levels
- Purchase orders
- Sales history
- Consumer trends
- Seasonal demands
- Maximum inventory level
2. Qualitative Forecasting
This process is not dependent on historical sales figures and other such data. Instead, it looks at external factors, such as politics, economic trends, and environmental changes. Qualitative forecasting uses sales feedback and market research—not specific to the company—to make predictions.
It’s not an ideal method because it lacks figures related to the business. However, it is the best forecasting procedure for companies where historical data is very limited or not yet available.
Established businesses can also benefit from qualitative forecasting. This data can be helpful if you want to introduce a new product or service to your market. It may provide insight into how the target audience will respond to this new release and what is needed for its success.
Qualitative forecasting is subjective by nature. However, you still need a seasoned sales expert to make intelligent forecasts.
Advantages and Disadvantages of Qualitative Forecasting
The main advantage of qualitative forecasting is that new businesses can do it even without data. Moreover, it can predict changes in sales trends and patterns as well as customer behavior based on the judgment of experienced sales executives or third-party experts.
However, as mentioned, it is highly subjective, bordering on speculative.
Subjective it may be, there are different types of qualitative forecasting designs a business could employ to ensure that forecast is as close to accurate as possible:
Types of Qualitative Forecasting
This is essentially “the majority rules” among a group of experts who congregate and discuss the business to make a forecast. To make the process more scientific and objective, the experts answer a questionnaire, and a team will analyze the answers to come up with a consensus forecast.
- Jury of Executive Opinion
In this qualitative forecasting method, the business owner calls on their high-level managers or division heads to share their opinions about the business. Whatever consensus is made during the deliberation will be the basis for the forecast.
- Grassroots Forecasting
Instead of managers, the personnel who deal most with the customers or end-users take the lead in grassroots forecasting. Because they deal with clients directly, they know what the business should sell and when. When all answers have been collated, a forecast will be made.
This is the most data-driven type of qualitative forecasting as it uses consumer surveys as a forecasting tool. If the business doesn’t have the resources for a survey, it will be limited to doing interviews with its customers.
3. Combination Forecasting
This combination of quantitative and qualitative forecasting takes the best of both methods, but it is also the most taxing.
How can a business use both quantitative and qualitative forecasting methods? Let’s go back to the clothing retailer example. Historically, November is one of the best months for the business since it is when many consumers begin shopping for Christmas and other holiday gifts. So, based on previous sales numbers, the business should increase its inventory significantly for the month.
However, in the past months, store staffers have heard customers asking if the business would be selling World Cup merchandise by November 2022, which is the start of the FIFA World Cup. It prompted experts to forecast additional inventory, specifically for World Cup-related merchandise, for the last quarter of the year.
Inventory Forecasting – The Formulas
Let us consider the following table as the numbers of T-shirt sales for 2022:
|Months||2022 (Number of items sold)|
There are several ways to perform inventory forecasting, but the simplest ones are moving average and trend analysis.
Moving average = (Sum of the quantity of items according to N months) / N
Trend = (Ending value – Starting value)/N
Calculating Moving Average
Moving average determines the average demand over a certain period, usually every quarter or three months, sometimes every six months. The figure will be used to forecast future demand.
To determine inventory for the first quarter of 2023, particularly in January, we can look at the data available for the last quarter of 2022.
Moving Average = (October + November + December) / 3 = (156 + 288 + 318) / 3
Based on the moving average, you can set 254 as your January inventory. For February inventory, you can use the moving average of November, December, and January.
You can also use the 12-month moving average since you have the data to calculate it:
Moving Average = 2,197 / 12 ≈ 183
You can set 183 as your January inventory. The advantage of using 12-month data is that it will not be skewed by a boost in sales during the holiday season.
For February 2023 prediction using 12-month data, you can use the sales from February 2022 to January 2023.
For trend analysis, you can also use the Q3 2022 data:
Trend = (Ending value – Starting value)/3 = (318 – 156)/3
To predict January inventory, just add 54 to the December inventory, which will give you 372 (318 + 54).
Or you can use the numbers for the entire year, which will give you 333 (318 + 15), based on the following:
Trend = (December – January)/12 = (318 – 138)/12
Moving Average Vs. Trend Analysis: What to Use?
The best inventory forecasting method to use is based on the data available. Moving average is preferable when you intend to forecast for a short period, such as a month or two. Trend is better for forecasting extended inventory, like three to six months.
You must also consider the data that you have. If the numbers show a trend, then the trend analysis is the better option. However, if the numbers are very different, it’s best to use the moving average formula.
In the given example, the numbers look erratic until the last quarter, with November and December showing a clear increase in sales.
You can also experiment with both methods and determine which works better for the company. Even better, add qualitative forecasting into the mix.
How to Avoid Stockout and Overstocking
Historical data is a great reference, but it’s not perfect. And it is only one element of overall inventory forecasting.
To avoid stockout and overstocking, you must also consider the following factors:
There are products that are seasonal. For example, for a clothing retailer, the month of May will likely see an increase in the sales of lighter clothes in preparation for the summer. By September, the demand will be for thicker clothes and jackets, which will then shift to full-on winter clothes a month later. All businesses must adjust their inventory based on such trends.
Seasonality isn’t just about literal changes in the season. It can also refer to events like Super Bowl season, where football merchandise is in high demand around February every year. And as previously mentioned, February is also a time when red clothing sees a boost in sales.
In a World Cup year, it is reasonable to expect a boost of sales in soccer-related merchandise.
Businesses don’t remain stagnant; at least, they shouldn’t. You also need to consider that when making inventory predictions. If the business expanded this year, then inventory should be higher than what is reflected in the moving average or trend analysis, which was based on past demand.
Inventory forecasting also depends on the current climate. One of the best examples of unanticipated events that rocked the business world is the COVID-19 pandemic. Many businesses had to limit operations or shut down entirely for months. Long-term inventory forecasts for companies were rendered unusable, and the situation led to a scramble for stability and survival.
As the holiday season looms, millions of consumers will begin shopping for gifts. How can retailers prevent products from going out of stock? Stockouts are wasted opportunities to make a profit, hence, must be avoided at all costs. Overstock isn’t any better, so the best thing is to make inventory forecasting as accurate as possible with comprehensive and precise data.
Inventory forecasting must strike a balance between quantitative analysis using historical data and trends, and qualitative analysis of external factors such as inflation, consumer spending and geopolitics. But to make it work, you need a reliable and efficient inventory system to track your inventory with ease.
For a simplified data entry process that makes organization extremely easy, contact Nest Egg here or call (510) 270-5798.
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