AI best practices for retail order management today

Consumers of the present day are multifaceted. They are vast and contain a multiplicity, similar to the American poet Walt Whitman.

They are not satisfied with the traditional shopping options of in-store, online, or mobile shopping in 2024. They want all of these options at different periods. Nevertheless, they are aware of their preferences, irrespective of the location of their shopping. They want the correct item from the right channel at the right moment.

This implies that effective customer experiences are increasingly determined by a variety of factors, including the availability of in-store products and the timing of deliveries.

It’s an ostensibly basic demand that retailers are struggling to deliver. In the past year, 78% of U.S. shoppers encountered out-of-stock items while purchasing in-store, and 73% of online shoppers reported experiencing this issue, according to a consumer survey.

Shoppers are rapidly transitioning to competitors when brands are unable to consistently supply the inventory they desire, as brand loyalty is in a state of decline.

Consequently, in order to thrive in a fiercely competitive global retail landscape, brands must anticipate consumer requirements, optimize inventory management and order fulfillment, and promptly adjust to consumer preferences and trends.

Brands will be able to accomplish this with consistency and proficiency through the use of artificial intelligence and machine learning capabilities.

For instance, these technologies facilitate complex queries of inventory audit or reconciliation reports, predictive order promising, and conversion probabilities. The method is as follows.

Comprehending the models that influence the retail industry
AI and ML are not isolated technologies. They are multifaceted, big-tent technologies that have undergone numerous iterations and forms. Not all of them are beneficial for inventory management and brand development.

Nevertheless, a variety of models can revolutionize the manner in which retail manages its inventory, enabling them to improve inventory management and prospective.

To begin, predictive order prediction accurately predicts the processing lead times for a variety of order fulfillment components, such as:

Centers of fulfillment.
Warehouses.
Drop-ship vendors.
Vendors in the marketplace.
Transit periods for shipments from carriers.
Predictive order promising can generate real-time, instantaneous predictions for delivery dates by utilizing historical data to accurately predict the duration of the delivery process.

Furthermore, dynamic inventory management systems employ extensive data sets, such as historical demand patterns and sales objectives, to generate customizable inventory levels that are accessible to both online and offline consumers.

Dynamic inventory optimization continuously monitors and adjusts inventory levels based on real-time demand signals and market fluctuations, in contrast to traditional, static inventory systems that rely on predetermined storage levels. These levels are revised through periodic updates.

When combined with Generative AI, co-pilots that enable business users to query data sets and decisions the system is making, inventory management teams gain a greater understanding and control over their supply chains, facilitating strategic decision-making and proactive adjustments.

These AI and ML models, when combined, enable organizations to more effectively adapt to real-time market fluctuations, thereby fostering stronger consumer relationships and enhancing sales results.

The advantages of models that are propelled by artificial intelligence
AI and ML models are assisting companies in the conversion of their extensive data sets into actionable insights and proactive inventory adjustments that improve the overall performance of their brands.

Retailers can anticipate cost-to-serve reductions of 3% to 15% and conversion rate enhancements of 5% to 20% when they implement the appropriate predictive order promising model. They can decrease the overall inventory by up to three percent and enhance inventory-related cancels by up to 40% by utilizing dynamic inventory models.

For instance, a national wholesale club was required to enhance its enterprise inventory capabilities due to the absence of visibility, which resulted in lost sales opportunities and canceled orders online. The company enhanced node controls with no picks, eliminated static safety stock calculations, and transferred inventory reservations into the checkout process by updating its inventory tech stack to the latest solutions.

Furthermore, the updated technology solution included comprehensive reporting, alerting, and reconciliation capabilities for inventory data, which will abbreviate the duration of root cause analysis cycles.

In general, inventory managers can anticipate the following from AI and ML models:

Agility: Proactively adjust inventory levels in response to changes in consumer preferences.
Segmentation: Customize inventory strategies to meet the specific requirements of various market segments.
Cost efficiency: Prevent stockouts and overstocking.
Enhanced conversion rates by providing precise and precise estimated delivery dates.
Risk mitigation: Prior to deployment, predictive models are evaluated on historical orders to mitigate risk and enhance decision-making confidence.
Nevertheless, the actual advantages are contingent upon the technology’s capabilities as well as its implementation, necessitating that leaders thoughtfully and intentionally integrate this technology into their existing workflows and capabilities.

Before updating to AI/ML models, take this into account.
Modernizing intricate supply chain and order management systems with the most recent AI and ML capabilities necessitates deliberate and meticulous planning.

Begin by assessing the integration capabilities of a solution and determining the extent to which this new system will integrate with existing software and tools. Data can be maintained in a continuous flow across all systems through seamless integration, whereas less compatible solutions can result in information silos that restrict the effectiveness of your inventory management strategies.

In the same vein, consider the solution’s capacity to expand. Choose an AI and ML solution that can expand in tandem with your brand.

Finally, guarantee that the organization has a comprehensive understanding of the data that your models will be utilizing. For instance, is the data set being utilized by all teams thoroughly baselined and comprehended when you are attempting to deploy an ML model to enhance sourcing decisions?

It is undeniable that the modern consumer is complex and selective, with constantly changing preferences that can be challenging for retailers to accommodate. The most successful brands will confront this challenge by utilizing technology that is prepared to adapt to the current circumstances.

In this manner, the emergence of AI and ML solutions is in perfect alignment with this challenge, enabling supply chain and inventory management teams to surpass their competitors who are unable or unwilling to adapt to the evolving landscape.