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Inventory Management Tips & Tricks

March 11, 2020
Shopify

Keeping track of inventory and deciding what to order takes more than guesswork. Inventory management is a science. Getting it right makes your business run smoothly. If you're struggling with inventory management, this article can help. Today you'll learn some basic forecasting models to improve your current inventory management system. 


Don't worry -- we'll make it easy and painless.


Inventory stock forecasting models

Seasonal fluctuations, sales slumps, and market shifts make forecasting inventory a challenge if you rely on gut instinct alone. Forecasting models take the guesswork out of how much stock to order. Different forecasting methods can be used depending on your business type. 


Here are the ones you need to know.


Demand Forecasting

Demand forecasting takes many variables into account to predict buying trends. It works by revealing the products consumers are likely to buy, which markets will do best, and how to price goods accordingly. This gives you a picture of the inventory to buy, how much, and when. There are several types of demand forecasting.


Naive Forecasting

This method is beginner-friendly. Past data is used to predict future inventory needs. While easy to understand, this is not always the most reliable method. 


For example, using last month's sales to predict this month's inventory can be dicey. That's because month-to-month forecasting doesn't account for market shifts like holidays, etc. Sales may boom before Christmas, but January can be a dud. If you based inventory purchasing off December sales figures, you could be in a lurch. 


Another example is clothing sales. Let's say you sell women's clothing, and prom dresses sell like hotcakes in May. Come June; formal dress sales will slow as the school year closes. Naive forecasting month-to-month doesn't consider these factors.


A better method is comparing last year's month-to-month sales data to predict this year's inventory needs and, for example, using last December's sales to predict this December's sales. While more reliable forecasting models exist, this would give better accuracy than using the previous month's sales to figure out this month's inventory.


Examples of naïve forecasting include the time series model, moving average forecasting, and exponential smoothing.


Seasonal index

This inventory forecasting model goes a step beyond naïve forecasting. This method helps business owners see whether sales are changing due to the season or an overall increase in consumer demand. Knowing the difference is a massive help in planning inventory needs. First, the average demand is calculated. Then seasonal demand is calculated. The percent increase (or decrease) is given a value. Then, compare the change in demand this season to last season. Any sudden increase or decrease can tell you a lot about consumer shopping habits.


Consumer surveys

Perfect for a new product launch, consumer surveys seek to determine what consumers will buy, and when. For example, you may survey women 30-45 to see if they are interested in a new hair removal device, or ask families if they will buy a swimming pool this summer. With enough data, you can make an informed decision. This is also known as "buying intentions" forecasting.


Some types of consumer surveys are end-user surveys, enumeration surveys, and sample surveys.


Game theory inventory forecasting

For B2B companies, game theory forecasting works nicely. Mathematical analysis is used to see how consumers make purchasing decisions when faced with their competitors. Learning how competition drives buying behavior in certain situations can help B2B businesses learn what to buy and when. For example, if you sell POS systems or a new CRM software, this method could come in handy. B2C companies can use it, too.


Sales force composite method

If you sell nationwide, regional sales can tell you a lot. This method draws on the expertise of regional salespeople to predict buying behavior. This allows you to see changes in demand by region. This method is a bit limited in scope. Still, it works for getting a general idea of regional sales so you can plan what inventory to buy for each area, and even which warehouses locations are best for certain types of inventory. For example, winter coats may do great in the northeast USA, but sunglasses and beach gear do better in southern California and Florida.


Delphi model forecasting

All hands on deck! The Delphi method is an advanced forecasting method with many experts involved. A panel of experts forms an inventory forecasting consensus after multiple rounds of surveys designed to determine buyer behavior. Each round of results is reviewed with a statistical summary before the next round begins. The experts can adjust their opinion after hearing from the other experts. Ultimately, it gives you a solid picture of what inventory to buy and when.


Executive opinions forecasting

This method relies on executive advisors from different departments. Each department analyzes data and comes up with a consensus. Then, each department reviews what the other departments had to say. Then, they come together to draw final predictions about consumer buying behavior. The final consensus can be a good indicator of future sales trends.


There is more room for bias and mistakes in this method than Delphi forecasting, but it is more "within reach" for the average business owner because it is less complex.


Data mining method of forecasting

This method takes a close look at existing data to find patterns. Patterns can predict future buying behavior. Mathematical algorithms are used to identify trends. This method is similar to stock market forecasting techniques. If you know the stock market at all, that means it doesn't guarantee perfect results because unexpected events may happen in the future. However, looking at recent trends can steer you in the right direction as you plan what inventory to buy.


Predictive analysis method

Similar to data mining, prediction method forecasting locates patterns in past data to determine future buying behavior. Rather than mathematical algorithms, this method uses scoring to identify past trends and the relationships between trends. For example, customers who buy hiking boots in the winter may be more likely to buy camping gear in the summer. This makes inventory purchasing easier and strengthens your marketing efforts.

 

Conclusion

There are many inventory forecasting techniques. It's up to you to decide which is best for you. The size, type, and complexity of your business should point you in the right direction. For new and small business owners, naïve forecasting and consumer surveys are a good place to start. For better insight, a more technical method like those mentioned above may be more useful.

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