Being able to enhance the ability to predict future sales and take the uncertainty out of demand forecasting is key to driving value across many parts of a retailer’s business.
Making better, faster, and more accurate demand prediction decisions provides a host of benefits across the supply chain, customer base, marketing, and financial functions of a company.
1. Increased Demand Forecast Accuracy
Machine learning is a forecasting method which is proven to be more accurate than traditional methods such as moving averages.
Large amounts of demand driver data from internal and external sources are inputted and then the method uses high performing algorithms to process the data and learn from it.
The processing speed and learning capability ensures that the resulting sales forecasts are stronger and faster.
The more data from different sources and constant improvement over time means that forecast accuracy continues to improve.
According to McKinsey reducing forecast errors up to 50% is achievable using machine learning-based techniques.
2. Create a More Resilient Supply Chain
With more accurate demand forecasts for future inventory requirements, the guesswork can be taken out of deciding what replenishment quantities for products are required.
Being able to confidently give suppliers a more reliable anticipation of demand reducing the likelihood of an interrupted product supply.
Within TrueStock alerts can be added to include supplier lead times and notifications will be sent out when inventory replenishment is needed.
Overstock notification and identification of optimum sales promotion periods will free up warehouse space.
Teams will be alerted when inventory hits low levels or buffer stock levels allowing demand planners to take immediate action.
3. Increased Profitability and Reduced Costs
Improved demand forecast accuracy can improve the bottom line through revenue growth, cost reduction and productivity improvement.
More accurate demand forecasts using machine learning methodology results can inform about precise future replenishment quantities and costs.
This can reduce missed sales opportunities through stockouts or mark downs on products that have been overstocked.
Excess stock takes up extra space, ties up working capital and can result in increasing costs for the business.
The speed and accuracy of machine learning for demand forecasting can free up teams to direct their time for other business tasks.
4. Deliver an Enhanced Customer Experience
The eCommerce space has become increasingly competitive and is driven by customer demand.
Ensuring product availability is key to ensuring customer loyalty and dissatisfaction can occur because of stockouts.
If a product is not available when and how they want it there is a high risk that consumers will source that product from a competitor.
The effectiveness of B2C (business-to-customer) online transactions and order fulfilment is a significant determinant of customer satisfaction and retention.
Successful first-time orders can determine whether the customer will place future orders with the same eCommerce retailer.
Increased accuracy with machine learning demand forecasts can help reduce those out-of-stock situations and boost customer satisfaction and their experience with the retailer’s brand.
5. An Improved Focus for Marketing Operations
Within the TrueStock’s machine learning demand forecasting model many dynamic factors including seasonal trends, the weather, events, pricing history, promotions and other marketing activities are considered, and their importance scored and weighted.
Return on investment of marketing from different channels and locations are analyzed and the insights provided allow marketing to pinpoint their future spend and focus.
Generating fast, more precise sales forecasts with data-driven machine learning technology enables businesses to achieve better results, be more resilient and ultimately grow their business.