November 15, 2023 Order Promise:Secret Sauce of Multi-Channel Fulfillment

Use of AI/ML in improving the accuracy of order promise and in turn reducing costs for business.

Disclaimer: The views and opinions expressed in this article are those of the authors solely and do not reflect the official policy or position of any institution, employer, or organization with which the authors may be affiliated.

Amritha Arun Babu Mysore, Ameya Deshpande

Multi Channel fulfillment is essential for B2B and B2C businesses today, due to the complex and globalized supply chain. A company like Apple Inc. which sells through online apple.com, walk-in stores, BestBuy, and several other ecommerce platforms. Apple or any other OEM of such scale need to balance their supply to ensure all first/third party customers are satisfied at all times! Well- how do they do that? One word, Order-promising!

Now, what is the order promising? It is a process of assigning “priority” to every order for available supply.

Here is a simple example:

We will just extend Apple Inc. example further. Imagine there are only 1000 iPhones in the US warehouse at this moment. bestbuy.com orders 600, apple.com orders 300, walk-in stores order 300 more. As a result, you have more “demand” than “supply”. Order promising engine continuously looks at the “priority” of the order. In this case it would be a really bad customer experience to run “out-of-stock” in walk-in stores vs. bestbuy.com. Hence Walk-in stores get what they asked for. Remaining 700 units are shared by Apple.com and Bestbuy until the next supply from factory lands!

What happens without Order promising:

• Samsung Galaxy Note 7: Samsung released the Galaxy Note 7 in August of 2016, but it had to recall the device just a few weeks later due to battery problems. The recall was handled poorly, and Samsung did not provide customers with accureat information about when they would receive replacement devices. This led to a lot of frustration and anger from customers.
• Apple iPhone X: Apple released the iPhone X in November of 2017, but demand for the device far exceeded supply. As a result, many customers had to wait weeks or even months to receive their devices. Apple’s order promising system was not able to keep up with the demand, and many customers were left disappointed.

These cases show that when order promising goes wrong, it can lead to poor customer experience and damage the reputation of the manufacturer. AI solutions can help businesses with multi-seller and multi-manufacturer fulfillment challenges by providing a unified view of inventory levels and shipping times, and developing order-promising algorithms.

Here are a few tips – how businesses can improve their order promising in a multichannel fulfillment setup.

• Use machine learning (ML) to improve your order-promising accuracy. ML models can be used to predict demand, estimate shipping times, and identify potential fulfillment disruptions.
• Use real-time inventory data: This will help you to accurately set customer expectations. If you don’t have real-time inventory data, you may underestimate or overestimate how long it will take to fulfill an order.

Leveraging AI to Enhance Order Promising Accuracy

AI improves order promising accuracy by dynamically estimating delivery lead times, allocating stock across fulfillment channels, and generating precise demand forecasts. AI-powered predictions enable retailers to set delivery expectations and notify customers on order status.

• Demand surge: If AI predicts that demand for a particular product is likely to surge in a particular area, the business can allocate more inventory to that area.
• Warranty callbacks: If AI identifies a particular batch of products as being more likely to need warranty repairs, the business can prioritize the replacement of those products.
• Optimized inventory allocation: AI can optimize inventory allocation across channels and locations. This helps to ensure that products are always available where they are needed most.
• Reduced costs: By improving order promising accuracy and rebalancing sales and warranty replacements, AI can help businesses to reduce costs associated with overstocking, stockouts, and late deliveries.

Get started in building an AI solution and factors to consider in designing such a solution

Developers or scientists and business leaders should consider the following when building a solution for improving the accuracy of order promise in B2B or B2C domain:

• Data quality: The accuracy and completeness of the data used to train and deploy AI models is critical to the success of the solution. Data should be cleaned and preprocessed to remove errors and inconsistencies.
• Data volume: AI models require large amounts of data to train effectively. Businesses need to ensure that they have enough data to train their models and to keep them up to date.
• Data variety: AI models are most effective when trained on data that represents the real-world environment in which they will be deployed. Businesses should include data from all relevant sources, such as historical sales data, customer data, and inventory data.
• Model selection: There are a variety of different ML models that can be used for order promising. It is important to choose models that are appropriate for the complexity of the task at hand and that are compatible with the existing infrastructure.
• Model interpretability: Businesses need to be able to explain the predictions made by their AI models, especially in cases where the models are used to make decisions that impact customers.
• Model bias: AI models can be biased, which can lead to inaccurate predictions and unfair outcomes. Businesses need to take steps to mitigate bias in their AI models.
• Deployment: AI models need to be deployed in a production environment. This can be a complex task, and it is important to have a plan in place for monitoring and maintaining the deployed model.

Conclusion:

By using AI to improve order promising accuracy and rebalance forward sales and warranty replacements, businesses can improve customer satisfaction, reduce costs, and increase revenue.

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