Maximizing efficiency and profits with AI-powered finished goods inventory management: a quick guide

The nuts and bolts of finished goods inventory management

Finished goods inventory management is the third (and final) stage of inventory management that’s used to track product information like available stock, cost and revenue before an item is sold to a customer. But a product you buy off the shelf has gone through a manufacturing journey to get it there. 

Every product on a shelf starts, in some way, as a raw material. As it enters the manufacturing stage, materials and costs are tracked, and it’s labeled as a work in progress. The next stage will see the product classified as finished goods. Finished goods inventory means that the product is ready to sell, and ready for customers to buy.

Here’s a (hypothetical) example of what this process would look like at a soda company. Before a customer buys a can of soda at a store, it goes through these three stages of manufacturing, starting with the drink’s raw ingredients:

• Raw materials: The product’s ingredients like sugar, caffeine and coloring are gathered to kickstart the manufacturing process. These materials are mixed together to create a syrup for the drink’s base. To ensure production isn’t interrupted, a raw inventory of ingredients is maintained to track quantities.

• Work in progress (WIP): Once the syrup is ready, carbonated water is added to create the soda, and then it’s poured into cans and bottles. Not only are quality control checks important here, but inventory like materials, wastage and labor will be tracked to help calculate efficiency and revenue.

• Finished goods: At the final stage, the soda is moved to pallets and prepared for distribution. Each product is labeled with a batch number and expiry dates to help with tracking and inventory. Only once a can of soda reaches this stage should it be offered to wholesalers and consumers to purchase.

Without tracking each step individually, it would be hard for this company to know how much shippable product it had on hand or how much soda was in the production stage. 

An accurate finished goods inventory is crucial for the success of any business that manufactures products. But tracking inventory can be challenging, especially as a company expands, or perhaps doesn’t have the right systems in place — which can lead to inaccurate forecasts, and customers choosing to buy from elsewhere. 

What is the importance of finished goods inventory for online businesses?

A finished goods inventory is the last (and arguably the most important) part of any product inventory. 

It not only tracks available stock for sale, but it’s also essential for record keeping and accounting for when it’s time to calculate profit and loss. A well-managed finished goods inventory does this by:

1. Tracking final output: Finished goods inventories can be compared to WIP and raw material records to see just how much product is making it to the shelf. This gives business owners a better idea of production costs and efficiency to see if there are any opportunities for cost savings in the manufacturing process.
2. Keeping accurate accounting records: Finished goods are considered an asset when it’s time to do your taxes and profit/loss margins. An accurate finished goods inventory is crucial to calculating gross profit, as it has the data to figure out the cost of goods sold (COGS) and the revenue generated by sales. Tracking these figures ensures that the gross profit and net profit figures are on point.
3. Optimizing storage space and stock levels: Finished goods inventory optimizes stock levels, so a business isn’t keeping too much (or too little) stock on hand. This reduces waste, and orders can be adjusted to match projected sales. The big win here is that it cuts unnecessary storage and warehousing costs, so there’s more money in your pocket.

The problem with traditional finished goods inventories

When a company is just getting started, it’s normal for employees to handle the moving parts of the manufacturing process like orders, stocktake and material inventory.

But when that company starts to scale, a traditional finished goods inventory system like this can become problematic. These employees will struggle to track growing datasets and keep on top of fluctuating customer demand, particularly in times of volatility and uncertainty as we’ve experienced in recent years. 

And, if a business is spread across multiple warehouses or locations, it can lead to inaccurate inventory counts and problems managing stock levels.

There’s also the issue of accuracy. Traditional finished goods inventories require constant updates in spreadsheets or other inventory management software, but one human error can cause a potentially serious ripple effect. 

Imagine a small electrical company that produces CPUs. The business is scaling, so it still relies on employees to keep accurate inventories. But one of the employees accidentally inputs the wrong number of finished devices into a spreadsheet during production, causing inaccurate stock levels. 

The next day, the business takes an order to ship 5,000 CPUs to a new client which, according to the finished goods inventory, it has in stock. But when it was time to fulfill the order in the warehouse, there were only 4,000 CPUs available. The company had to delay the shipment, and the client went to a competitor. 

One small human error was all it took for this company to lose thousands of dollars of revenue. 

AI is changing this. Thanks to increasingly-utilized use cases like demand forecasting, machine learning and RFID (Radio-Frequency Identification) tags, businesses can now keep accurate inventories and respond to customer demand in real time. Let’s dive into demand forecasting specifically in a bit more detail…

How AI-powered demand forecasting is revolutionizing finished goods inventory management

The ultimate goal of finished goods inventory management is to juggle profitability, minimal inventory waste and storage costs while spending minimal time and energy on it. 

But that’s not always the reality. The problem with traditional inventory management is that most supply and demand planners just don’t trust their own numbers. Although the goal is a lean inventory with maximum profits, changing customer demand and volatile supply chains make accurate demand forecasting incredibly difficult. 

AI-powered demand forecasting is different. It optimizes a business finished goods inventory by planning for uncertainty. Here’s a snapshot of what that means ?

1. AI can optimize the entire manufacturing process

Think about a traditional inventory goods management process. At best, an employee updates systems manually, when they get time, with details on price fluctuations and supplier information.

But what if the cost of a particular raw material shoots through the roof? What happens if a product that’s essential to the WIP stage is running low, and someone from the production team has forgotten to order more? ? 

Traditional approaches to inventory management aren’t agile. Systems are slow to respond to real-time market changes and can result in slow manufacturing times and bottlenecks in production.

 The risks of a traditional manufacturing process. Image source: Medium

AI can help businesses overcome these situations because it’s built to deal with uncertainty. Thanks to predictive and real-time monitoring, AI can analyze historical data for trends and patterns to optimize demand and stock levels. 

Let’s look at an example of a consumer goods company that produces wine. Around Christmas time, orders usually shoot through the roof thanks to popular products like mulled wine and cider. But in 2022, the company used AI demand forecasting to predict that, due to inflation, rising costs and customer sentiment, demand for its premium Christmas products would be down by roughly 60%.

This allowed the company to: 

• Order and store optimum levels of stock to meet consumer needs without over-ordering raw materials

• Adjust production schedules to produce mid-range products that matched customer needs and sentiment

• Monitor supply chains and consumer demand in real-time, so if appetite for premium products rises, it can pivot production schedules to meet demand

By utilizing real-time data and AI demand forecasting, the company was able to prevent overproduction of premium products that were unlikely to sell during a tough economy. But it was able to make up for the loss by keeping warehouse costs lean and meeting consumer demand by producing more of its mid-range products instead.

2. Automated inventory and optimized customer preferences are becoming a reality

Losing a customer because an item is out of stock is one of the most frustrating situations for a business to find itself in. It’s also very avoidable! 

Instead of relying on a time-poor human to reorder essential materials to keep your manufacturing process humming, AI can automate the process, so that nothing falls through the cracks. A finished goods inventory software can track orders, product, location and historical sales data to optimize demand and ensure a company has the ideal inventory levels, even if you have multiple locations. 

For fashion retailers like H&M, AI technology has truly been a game changer.

Back in 2018, the brand announced it was deploying AI to meet consumer demand in its brick-and-mortar and online stores. It invested in AI that analyzed customer receipts, product returns and loyalty program spending to determine what product customers liked more, so that it could ship items to specific brick-and-mortar outlets. 

On top of localization, H&M also automated warehouse operations. After its 2018 annual report showed the company had an excess inventory of around $4.3 billion, the company invested in RFID (Radio-Frequency Identification) tags which automated its inventory management process. The tags accurately track item availability in the company’s warehouses to automatically update inventory levels in real-time.

Thanks to the combination of automation and AI, H&M hopes to achieve next-day delivery for 90% of the European market.

3. AI tracks stock levels and leverages warehouse space

Warehouse space and logistics costs are huge expenses for companies that manufacture goods, and they’re on the rise. 

According to Savills, warehouse costs increased by an average 8.4% in 2022, with logistics skyrocketing by an average of 20.1%. Researchers at McKinsey say that process automation, omnichannel warehouse management and a deeper understanding of customer needs can bring massive value to direct-to-consumer (DTC) retailers:

“In the traditional supply chain model, companies often choose a purely quantitative approach to model the perfect fulfillment network needed for the service offering. This generally involves a rather rigid and time-consuming approach: three months of data collection, six months of modeling, and three months of decision making before implementation. 

However, in an ever-volatile environment with constantly changing customer needs, evolving partnerships, and newly developing competition, reacting quickly is critical to ensure that the supply chain network is responsive, flexible, and efficient. 

Therefore, companies should remain agile in their thinking and assemble a cross-functional team. One best practice is to develop the future supply chain network in a workshop-based environment. In practice, this means determining the fulfillment options suitable for each customer, product, and location segment and defining the required product flow.”
— McKinsey, Future of retail operations: Winning in a digital era

AI can help optimize parts of your fulfillment process like stock levels, trigger reorder points for each product and automate what warehouses items are distributed from. It can even track historical and real-time data to keep warehouse stock lean while meeting consumer demand. 

For example, Peak’s Dynamic Inventory: Finished Goods application uses AI, targets and historical data to forecast how much it will cost to increase inventory and stock units for individual SKUs. With AI, Peak tracks individual SKU stock and will update you on the status. You can then see if a product is overstocked, understocked or in good shape on your dashboard:

Peak’s application will alert you when it’s time to reorder based on stock status and order lead time. This allows you to always have an optimal amount of product in stock without paying for storage space and over-ordering products.  

In an uncertain world where customer demand is always changing and supply chains are volatile, AI can help you weather the storm and plan for uncertainty.