In retail, meeting customer’s expectations becomes a task once you expand your business to different locations.
Inventory forecasting plays a major role in effectively and consistently meeting changing customer expectations. It could also backfire, if the inventory forecasting isn’t accurate and tailored on a store level.
Luckily with the advent of AI and ML, businesses can predict and procure only the products that your inventory needs, tailored for each location.
This article will help you understand what inventory forecasting is, its benefits, and AI & ML’s role in the same.
What is inventory forecasting?
Inventory forecasting is the process of predicting future inventory requirements based on historical data, customer behaviour, market trends, and other influencing factors.
The goal is to make sure that businesses have the right amount of stock to meet customer demand while minimizing excess inventory and reducing storage costs.
Also, accurate inventory forecasting helps businesses optimise cash flow, reduce wastage, and improve overall operational efficiency.
Inventory forecasting vs. replenishment
While inventory forecasting and replenishment are closely related, they serve very different purposes:
i) Inventory forecasting
This involves predicting future demand based on past sales, market trends, and external factors like seasonal changes and economic conditions. Ultimately, it helps businesses plan stock levels proactively.
ii) Inventory replenishment
This focuses on maintaining optimal stock levels by restocking products as they get depleted. It is a reactive process that ensures continuous product availability based on real-time stock levels and sales velocity.
Both processes must work in tandem to create an effective inventory management system that maximizes efficiency and profitability.
Why traditional inventory forecasting doesn’t always work?
Often, inventory forecasting is done based on gut feeling or anecdotal data. For example, if you run a chain of retail stores and if one location sells a particular product more, you may overstock the same product at multiple locations thinking there would be sufficient demand for the product.
But this isn’t always the case. Each location’s demography demands different products depending on various factors such as customer lifestyle, market trends, affluence level, etc.
Because of these reasons and more,
- 40% of fashion inventory is never sold at full price
- $500bn wasted in overproduction of fashion apparel, accessories, and other products globally
- 30%+ Styles fail to resonate with local market buying patterns or trends
Businesses have relied on several traditional methods to forecast inventory which may or may not succeed in its purpose.
Let’s dig a bit deeper now.
Traditional inventory forecasting methods and their challenges
1. Historical sales data-based forecasting
This method uses past sales data to predict future demand. This approach assumes that past trends will continue, which may not always be true due to changing consumer preferences, economic shifts, or unexpected disruptions.
For example, a fashion retailer relying on last year’s sales data might overstock an item that has fallen out of trend, leading to markdowns and inventory losses.
2. Rule-based forecasting (Simple moving averages)
Rule-based inventory forecasting uses fixed time-period averages to estimate demand.
The issue is that this method does not consider seasonality, external factors, or sudden changes in market demand, making it less adaptive to dynamic conditions.
A grocery store might fail to predict a surge in demand for certain products before a major holiday, or certain local event, leading to stock shortages.
3. Manual spreadsheet forecasting
Here, inventory managers manually input data and apply forecasting formulas.
This is prone to human errors, lacks scalability, and is time-consuming. It also struggles to process large amounts of data efficiently.
For example, a retail chain with multiple locations might struggle to aggregate and analyze demand trends manually, resulting in inaccurate inventory forecasting. This can lead to either excessive stockpiling or frequent shortages.
4. Fixed reorder point strategy
In the fixed reorder point strategy, the inventory is replenished when it reaches a predetermined threshold.
The issue is that this approach does not account for demand fluctuations, leading to stockouts during high demand periods or excess inventory during slow periods.
While these traditional approaches have been useful, they come with certain limitations which won’t be fruitful in many cases, as we discussed.
The best approach to inventory forecasting: Real-time store-level interventions based on local market trends
Given the limitations of traditional methods, modern inventory forecasting relies on data-driven techniques that incorporate multiple factors for higher accuracy.
Location intelligence tools can analyze consumer density, nearby competitor presence, local consumer behaviour, and more to optimise inventory forecasting.
Apart from these, the following components play a crucial role in optimising inventory forecasting:
1. Incorporating consumer and market trends
Location intelligence gives insights into consumer behaviour, seasonal demand shifts, and local market trends to help businesses forecast accurately.
Understanding local customer preferences and purchasing patterns allows for more refined product stocking strategies. You can also gain visibility into the local customer base’s affluence level to better make decisions on product SKU levels.
2. Forecasting demand at the store and SKU-Level
Leveraging these tools won’t be prone to error as you make decisions not on regional location data but at the store level.
Analysing multiple factors on a store and street level helps determine which specific products need to be stocked more or phased out based on demand insights.
This granular approach to forecasting enables businesses to optimise their assortment strategy, ensuring high-margin products are prioritised.
Also, SKU-specific forecasting reduces unnecessary stock buildup, improves shelf space utilization, and increases overall sales efficiency.
3. Real-time data and AI-driven interventions
AI & Machine learning algorithms analyze vast amounts of real-time data and continuously refine demand predictions based on new data.
This helps in improving forecast accuracy over time and it is tailored to multiple locations to ensure you meet the specific demand of the consumer base.
Benefits of accurate and data-backed inventory forecasting
1. Minimised stockouts and overstocking
The immediate result you’ll witness with accurate inventory planning is that you can prevent loss due to stockout and overstocking of a particular product or a product category.
Overstocking also increases warehousing and holding costs, further impacting profitability. Accurate inventory forecasting ensures a balance between supply and demand, optimizing inventory turnover rates and improving overall efficiency.
By accurately predicting demand on the SKU level across multiple retail stores, the chances of dealing with stockouts or overstocking are significantly lesser.
2. Improved supply chain efficiency
Accurate inventory forecasting enables your stores to synchronize their supply chain operations with actual demand.
This helps reduce lead times, optimize procurement cycles, and ensure better coordination between suppliers, manufacturers, and distribution channels.
A streamlined supply chain minimizes waste, improves supplier relationships, and ensures that stock levels remain consistent.
3. Better seasonal and promotional planning
Businesses that experience seasonal fluctuations in demand need accurate forecasting to plan for peak and off-peak periods effectively.
A well-structured inventory forecasting model helps businesses stock up on high-demand products before seasonal surges while avoiding excess stock during slower periods.
Similarly, for promotional events, businesses can use data-driven insights to determine the right amount of stock needed to meet increased demand.
4. Increased profitability and reduced costs
Inventory forecasting helps businesses optimize storage capacity by ensuring that only necessary inventory is kept on hand. Reduced storage costs directly impact the bottom line, improving profitability.
Additionally, when inventory levels are well-managed, businesses can maintain healthier profit margins by reducing the need for heavy discounting to clear unsold stock.
5. Improved customer satisfaction
Customer experience is significantly impacted by inventory availability.
When customers find what they need in stock, they are more likely to return and remain loyal to a brand. Stockouts, on the other hand, can lead to frustration and loss of trust.
By accurately forecasting demand, your retail chain can ensure that all your stores consistently meet customer expectations, leading to higher customer satisfaction, increased brand loyalty, and positive word-of-mouth marketing.
Conclusion
Accurate inventory forecasting is critical for maintaining optimal stock levels, reducing wastage, and improving profitability for the retail store.
Investing in predictive analytics, machine learning, and real-time data monitoring will help retail brands achieve a streamlined, efficient, and cost-effective inventory management system.
Book a 15-minute discovery call with us to analyse the said factors and improve profitability with inventory forecasting.
