Embarking on the journey of introducing a new product to the market is like stepping into the unknown for most businesses. However, in this exhilarating endeavor, the success hinges on mastering the art of crafting the perfect new product launch strategy.

Imagine it as a symphony where each note plays a pivotal role. From the careful orchestration of market research and pinpointing the ideal moment for the grand launch, to creating a compelling pricing strategy, designing captivating packaging, shaping of promotion, and targeting the hearts of your ideal audience. 

Yet, amid this symphonic harmony, one crescendo reigns supreme: product availability. After all, every meticulously planned detail can crumble if the consumer can't readily get their hands on your new product. It's a hurdle many businesses acknowledge and, in their quest to avoid the dreaded 'out of stock' sign, resort to amassing excessive inventory. The consequence? Soaring costs that come from production, warehousing, and distribution of the new product."

What companies truly want is a comprehensive solution that seamlessly addresses the challenges of demand planning during a new product launch. In the dynamic landscape of modern business, traditional forecasting methods may falter and leave you guessing. This is where cutting-edge technologies, such as AI and ML algorithms, step in to revolutionise the way companies optimize their demand planning processes.

In this blog, we will explore three ways of introducing a new product and the forecasting techniques associated with each of them.

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Upgraded Product - Replacing the Existing One‍

The first approach to introducing a new product is by upgrading an existing one. For instance, imagine a popular brand of biscuits deciding to launch a new version with 25% more milk. This is a classic example of an upgraded product. Forecasting demand for such a product requires understanding customer preferences and anticipating how the upgrade will affect their buying behaviour. 

In this case, we have a predecessor for the new or upgraded product. The predecessor and the new product are closely related when it comes to demand behavior. To forecast the demand for the new product, a new dimension is set up. Here’s how it works:

Milk Biscuits is the parent SKU with product code 200

Here’s a glimpse of the demand forecasts for Milk Biscuits

Milk Biscuits 2.0 (25% more milk) is the child SKU with the new product code 201

The Child SKU is now mapped with the Parent SKU. The new product code is then used to represent the forecast numbers in the front end.

Now, the system predicts the demand for Product Code 201(Milk Biscuits 2.0) based on the historical data of Product Code 200 (Milk Biscuits). 

Further, in the event of another upgrade in the future, a new product code is again mapped under the Parent SKU - Milk Biscuits. The historical data of the predecessors is then used to predict demand accurately for the upgraded product. 

AI powered demand forecasting for new products provides visibility into how the change in product features will impact sales, helping businesses make informed decisions about production quantities.

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Line Extension - Adding a New Product to the Current Line-Up‍

Another way to introduce a new product is through line extension. This means adding a new product to your existing line-up. Demand forecasting for such products requires understanding how the new addition will complement the existing products and whether it will cannibalise sales from them.

For instance, a popular biscuit brand decides to introduce a new flavour to its existing range of biscuits. They are adding the currently trending “Wild Honey” flavor to their biscuits range. In this case, there isn’t an exact predecessor to the new product. So, we associate the new product with an existing product that has the most similar/closest attributes. This requires a birds eye view of the product hierarchy.

In the above hierarchy, Wild Honey Flavour under Biscuits is the new product which has no historical sales. The closest SKU to that will be the Milk flavor under Biscuits. So, Wild Honey SKU is associated with Milk Biscuits SKU to forecast demand.

In the case that the Pasadena branch doesn’t sell Milk Biscuits we won’t have any other SKU under biscuits for mapping. Then we look for a higher level of hierarchy to associate with. Here, the Category - ‘Snacks’ is considered. Under the category Snack, we would have 3 different SKUs namely Wild Honey Biscuits, Chocolate Cookies and Cream Cookies. The historical sales of Chocolate cookies and Cream cookies are taken into account to forecast demand for the new product, Wild Honey biscuits. We calculate the median of the demand for Chocolate cookies and Cream Cookies and arrive at the demand forecast for the new SKU. We have used the Category * SKU combination here for association with the new product.

In reality, the associations we make differ according to the structure of the hierarchy and the attributes of the existing products. 

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Brand New Product - Exclusive and Launched for the First Time

The third approach involves introducing a brand new product, one that has no predecessors in your product line. Let's take the example of a popular biscuit brand launching protein bars.The company is launching protein bars for the first time, they have no historical data to rely on for forecasting. This makes predicting demand a unique challenge.

In these cases, AI powered demand sensing engines are very quick to analyse and learn from the emerging demand patterns of exclusive products. They have the capability to provide accurate demand forecasts with just 3 weeks of sales data. 

Additionally, for brand new products, AI can leverage market research data, competitor analysis, and consumer trends. AI & ML models can identify similarities with existing products in the market and estimate potential demand based on similar products' performance. 

Launching a new product is an exciting opportunity for growth, but it comes with its share of risks. Accurate demand forecasting is the key to managing inventory, production, and marketing effectively. 

Here’s a quick roundup of things that will help you achieve accurate demand forecasts for new products:

1. Historical Data Analysis: Examine historical sales data of similar products to understand demand patterns and behaviours.

2. AI and ML Technologies: Utilise AI and ML algorithms to predict demand based on historical data, especially for upgraded products.

3. Product Association: Carefully associate new products with relevant existing ones in the lineup, considering product hierarchies.

4. Market Research: Leverage market research data, competitor analysis, and consumer trends for brand new product launches.

5. Quick Adaptation: For exclusive products with no historical data, rely on AI-powered demand sensing engines, which can provide accurate forecasts with just a few weeks of sales data.

AI algorithms play a crucial role in this process, providing businesses with data-driven insights for decision-making. Whether you're introducing an upgraded product, extending your product line, or launching something entirely new, AI can help you make informed forecasts and optimize your business strategy. Embracing AI in demand forecasting is not just a modern approach. It's a necessity in today's competitive marketplace. 

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