A Beginner's Guide to Inventory Forecasting
How to Predict and Plan for Future Product Needs

Inventory forecasting is a critical task for any ecommerce business. This is also one of the most difficult aspects of inventory management to get right.

If you don't have enough inventory, you'll run out of stock and lose sales. But if you have too much inventory, you'll be wasting money on storage and may end up selling products at a loss. 

That's why we've put together this guide on inventory forecasting for beginners. We'll cover everything you need to know about forecasting, including:

  • What is inventory planning?

  • What is inventory forecasting??

  • The future of inventory forecasting: predictive analytics

  • Key types of inventory forecasting

  • Importance of inventory forecasting for retail owners

  • How does sales forecasting correlate with inventory forecasting?

  • How does forecasting help with inventory management?

  • The Key Advantages of Inventory Forecasting

  • Key features of effective forecasting software.

What is inventory planning?


Inventory represents often the biggest part of a retail business’s assets – up to 80% of cash is usually tied up in inventory. 

Inventory planning is a strategic process that determines the right level of inventory a business needs to maintain to support its operations.

The goal of inventory planning is to strike a balance between two competing objectives: ensuring the business has enough inventory to meet customer demand, while at the same time minimising the amount of inventory on hand to free up cash flow.

To be successful in this competitive industry, it is essential to approach stocking up on products and selling items with a data-driven mindset.

By taking these factors into account, businesses can develop inventory plans that will help them avoid the costly mistakes of holding too much or too little inventory.

At the core of inventory planning lies data science, which involves using data to make informed decisions about how much merchandise to order and when to sell certain products.

By integrating data insights into their inventory strategies, retailers can ensure that they are always stocked with the right items at the right time, minimising waste and maximising sales revenue.

What is inventory forecasting?


Inventory forecasting determines the amount of inventory needed to satisfy future customer demand based on the sales forecast over a certain period. It’s a practical solution to manage your purchases better, increase your revenue, and reduce unnecessary costs.

With accurate inventory forecasting, you can keep enough inventory on hand without draining your cash reserves.

Forecasting inventory is critical to the financial viability of any retail business. It assists in finding a balance between putting too much cash into inventory at once and ensuring demand is constantly met without running out of supplies.

This, however, is one of the most challenging components of inventory management to master.

Inventory forecasting software, or demand planning software, provides tools to improve and stabilise the supply chain process, providing businesses with a better idea of what inventory is or will be needed, when reorders should occur, and assists with reducing both out of stock and overstocking occurrences.

Combining historical trend analysis with anticipated season fluctuations and promotional events, inventory forecasting software enables purchasing managers with the ability for better spend management planning and improved collaboration with vendors.

The most important inventory management practices are forecasting (61.3%), warehouse management (50%), logistics (46.8%), and back-end technology (32.3%). These are closely followed by training data scientists (21%), returns management (21%), and data interchange technology (17.7%). In addition, some prioritise investing in sensor technology (12.9%), training retail staff in eCommerce (11.3%), retooling DCs (9.7%), and refitting stores to have warehouse capabilities (3.2%). (Source)

Inventory management practices


Inventory forecasting vs replenishment


With inventory forecasting, you calculate the amount of the different types of inventory necessary for future periods. Factors include replenishment data such as timing, availability and delivery speed — also known as lead time.

Replenishment is the stock required to meet inventory forecasts based on inventory goals, supply and demand.

Sales and demand forecasting is one thing. But true inventory forecasting needs to go a step further and actually plan out how you’ll replenish stock for the upcoming period. This means considering: current stock levels, pipeline inventory, and lead time.

Example: There's no point purchasing 40 units to cover 40 forecasted sales if you already have 27 units on-hand. You also don't want to double buy stock that's already en route.

Forecasting inventory needs to be a strategic process that considers all relevant factors in order to avoid any disruptions in the supply chain.

Key types of inventory forecasting


The most successful businesses can reduce risks, meet future demands, and avoid future losses—and inventory forecasting is one way to achieve this. 

However, forecasts don’t have a one-size-fits-all formula. There are different methods and approaches to these formulas.

The most prevalent formulaic strategies for successful inventory forecasting are trend, graphical, qualitative, and quantitative. Choose the optimal strategy based on known stocking limitations, personal insights, sales feedback, customer input, quantitative analysis, and market research.

Trend forecasting


Trends are changes in a product's demand over time. Using previous sales and growth data, this technique anticipates probable trends while excluding seasonal impacts and inconsistencies.

More detailed sales data improves this forecasting approach by indicating how certain consumers and customer categories will likely purchase in the future. This data can help analysts discover new methods to promote and sell.

Example:

For the past eight years, a small eCommerce company selling accessories has been profitable. The company has proven successful over the years. However, there are no plans to grow at this time.

Over the previous three years, their historical data suggests that November, December, and January are their best-performing months, while May and June are their least successful.
Using this information, they may create a trend prediction that shows when they should place orders for supplies and products. This information might also help them plan a marketing campaign to improve sales during their lowest months next year.


Graphical forecasting


The same data that a forecaster analyzes for trend forecasting may be graphed to highlight sales highs and lows.

Because of its visual aspect and accessible insights, some forecasters favour the graphical technique. They may recognize patterns in a set of data points and add curved trend lines to graphs to investigate potential paths that might otherwise go unnoticed.

Qualitative forecasting


When challenged with a shortage of historical data, some businesses turn to the source: their consumers.

Focus groups and market research are two examples of complicated data collecting methods used in qualitative forecasting. Forecasters then use this sort of data to build out models.

Example:

A sustainable fashion company seeks to grow their consumer base in order to increase revenue.

They distributed surveys to their existing customers to perform market research. They created customer profiles by evaluating the data and determined that the majority of their customers are millennials and Gen Z.

To widen its reach, the corporation opted to use social media channels such as TikTok and Instagram, as well as advertisements.

Finally, they used statistical principles to assess the prospective influence of a new marketing campaign on future sales, which they might use to forecast demand.

Quantitative forecasting


Quantitative forecasting employs previous numerical data and is thought to be more accurate than qualitative research alone. Typically, the more data a company has, the more exact the forecast.

Time-series forecasting is an example of quantitative forecasting since it employs temporal quantitative data to create a model that predicts future trends.

Importance of inventory forecasting for eCommerce companies


As the global ecommerce market continues to grow exponentially, knowing what customers want and when: remains vital for success. 

This is where the importance of inventory forecasting comes in.

Forecasting is a process that predicts the future demand for products by using historical sales data. This can be used on both new and old products.

An eCommerce business can track patterns and trends by studying their order management system for sales data and then estimate future sales and how they may vary.
While some online companies categorise their forecast models on a macro-level, which is the broadest context, others prefer micro-level forecasting, short-term or long-term.

Inventory Forecasting Benefits


Inventory forecasting can mean the difference between profit and a warehouse full of unsold goods. eCommerce companies that use it effectively can better anticipate prospective trends, save money on storage, and keep consumers satisfied.

The McKinsey insight shows that better management of supply-chain uncertainty is a top priority for many executives: companies that get their bets right can boost revenues by about 3 percent, thanks to fewer stockouts and lost sales, while reducing cost of goods sold by a similar amount, through lower demand for expedited logistics or overtime production.

And the right bets reduce write-downs by 30 to 45 percent, while cutting working-capital needs by 10 percent and capex requirements by 5 percent as excess capacity falls. (Source
Creating and revising inventory estimates may take a substantial amount of time and effort. When demand forecasting is done correctly, however, there are numerous benefits to gain. 



Cost savings


Everything boils down to efficiency. You may take advantage of bulk buying without tying up money in unneeded inventory by ordering the optimal amount of product.

Unused supplies or components need warehousing space, which raises expenses.

Back-end improvements

Inventory and supply chain are strongly intertwined. Improved inventory forecasting helps supply chain management by allowing you to plan ahead of time to ensure you have the proper quantity of stock.

It can also reduce the amount of manual labour involved in inventory and supply chain management. Other procedures, such as reordering points, can be automated. 

Customer and supplier satisfaction


Having a product on hand keeps consumers satisfied and increases the probability of repeat business.

Understanding supplier procedures and timeframes also helps you prevent stock-outs and maintain strong relationships with them by reducing emergency orders and improving communication.

Strategic insights


Improved business communication may help you reach your goals, and inventory forecasting can play a major part in driving such communication.

For example, by analysing historical performance and predicted consequences of a marketing campaign, your inventory managers may guarantee there is enough goods on hand to fulfil client demand while potentially saving money through buying in bulk.

            12 Steps to succeed at inventory forecasting


            A better forecast will lead to higher profits and lower costs. So take note of these demand forecasting tips to make it more accurate and effective.

            • Evaluate the period's basic demand. For example, if the company sold 500 units in the previous quarter, the forecasting model's beginning data point will be 500 units.

            • Determine patterns and factors, as well as their impact on an increase or drop in sales, such as any promotions or other outside marketing activities that may have influenced baseline demand.

            • Analyse the sales velocity. Sales velocity is the rate at which sales flow through a company's pipeline. It is determined by the amount of leads, the average transaction value, the conversion rate, and the length of the sales cycle.

            • Analyse any relevant industry dynamics, such as new market rivals, supplier concerns, commercial buyer behaviour, risks of less costly product alternatives, and other competitive rivalries.

            • Consider seasonality as it relates to each product. Seasonality may be determined in a variety of ways, including computing a seasonal index for each month, distributing demand across a 12-month period, or employing more advanced statistical approaches.

            • Build models. This science is more of an art that is built on all of the collected trends and historical information. This data will be used to create forecasting models. There are several statistical approaches available. Which option you select is determined on the consistency of product demand.

            • Remove irregular or abnormal data points and look for and fill in missing information to clean the data.

            • Choose between a parametric and a nonparametric statistical technique. Nonparametric does not always indicate that the data has no parameters; rather, the parameters are flexible. Histograms and ranked-choice surveys are examples of nonparametric data.

            • Import or structure the data in a way suited for the algorithm to process it.

            • Calculate the model parameters: what is the best and worst case scenario for the data points?

            • Validation of the model (s). Use a different set of data than the one you used to calibrate the model.

            • Adjust the model on a regular basis or as events need it. Keep in mind that this is a projection based on assumptions. Real-world experience may reveal that it is incorrect, thus you may need to revise the settings.

            The  CommerceCore™ Inventory module


            An eCommerce business typically has numerous systems for Finance, Purchasing, Inventory, maintenance operations, production and manufacturing, sales and distribution, projects, and so on. Many of them have successfully replaced all of those old systems with a single integrated Merchant Operating System that manages the operations more effectively.

            The CommerceCore™ Merchant Operating System avoids data duplication and offers data integrity with a single source of truth by linking Odoo to a growing number of online store systems and marketplaces.

            The CommerceCore™ inventory module has several features that set it apart from its competitors. Here are some of them:

            • Low processing time

            • Inventory processes are simplified

            • Removal strategies

            • Traceability

            • Routing

            • Advanced routing

            • Rule of routing

            • Operations based on lead times

            • Replenishment

            • Product types

            • Product variants

            • Barcode integration

            • Inventory forecast.


            Automate your inventory forecasting with  CommerceCore™


            Striking a balance between having enough but not too much inventory might be the difference between a business's success and failure.

             CommerceCore™ inventory forecast refers to the quantity of products you can sell for a specific warehouse or location.

            The  CommerceCore™ inventory management module has a set of native features for tracking inventory in many locations, calculating reorder points, maintaining safety stock and cycle counts, and forecasting.

            By using the demand planning tools of the  CommerceCore™ Merchant Operating System's inventory module, you can create an inventory prediction for your eCommerce company.

            Conclusion:


            Forecasting is an important part of business, and making accurate predictions is essential to success.

            The forecasts you create are based on data and logic, as well as the refined models you use. Technology can also play a role in helping with forecasting accuracy.

            The  CommerceCore™ inventory management module can be a key component to your company’s success by making sure you have the right amount of product to meet customer demand while not unnecessarily tying up funds in unneeded inventory.

            If you would like more information about how our software can help your business or if you want to discuss your specific needs, please contact us.

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            Merchant Operating System (MOPS) refers to a type of software used by Shopify merchants to handle day-to-day online-business operations including warehouse management, multichannel product management, invoicing, payment tracking, order processing, and much more.

            The Merchant Operating System also includes corporate performance management software to help with planning, budgeting, forecasting, and reporting.

            The MOPS is built on the widely popular open-source ERP Odoo Enterprise Framework. 26000 apps/plugins/modules/extensions are available from an Integrated App Store.  We help our customers carefully pick and integrate apps based on their needs through our Professional Services.

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            A Beginner's Guide to Inventory Forecasting
            Irena Milovanovic 28 April, 2022
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