There is a seismic shift occurring and brand manufacturers are at the epicentre of the disruption. Brand manufacturers are moving into direct to consumer channels, engaging customers directly while continuing to increase their presence with traditional retailers. As this transformation evolves, the challenges to become successful, profitable, and productive are many.

As brand manufacturers are reaching out to consumers directly, owning their brand messaging, and ultimately the customer engagement, demand planning for the channel explosion needs to transform, too.

Many brands now have their own D2C models—funnelling customers to their websites, mobile apps, or branded retail stores. This innovative omni-channel approach allows brands to engage with consumers previously unserved, or underserved, by traditional retailers. Brands now control consumer-facing content, their stores, and their supply chains—which means the business model of selling must pivot from a sole focus on major retailers to a balance between those valuable retailer accounts and more personalized direct customer engagements.

Tackling wholesale and direct-to-consumer demand

With this shift come challenges that impact people, processes, and technology. Demand and supply planners need to collaborate and align on demand for the various channels, considering both the wholesale account selling as well as the new direct-to-consumer (DTC) ventures. Manufacturing needs to know what styles and colours to make, the quantities needed for initial introduction and subsequent fill and the timing of shipments. Many brands lack modern technology, relying heavily on Excel with manual inputs, getting very little support from demand planning tools. And when tools are in place, the focus is on wholesale account planning and usually driven by outdated forecasting capabilities.

Innovating with machine learning

In keeping with the shift, Infor is innovating – fusing together machine learning and omni-channel planning into one solution for both wholesale account planning and DTC. Machine learning drives the demand plan considering Shipments and POS, product attributes, promotional/marketing events, and other features to automatically produce forecasts for all style/colours across the Lifecyle of any given product. This automation includes the ability to forecast new styles based on attributes and features, no longer requiring planners to manually set up “like SKUs”.

Seamless demand planning for omni-channel brands

Omni-channel planning converges the planning processes into one seamless plan across the entire company, aligning financials of the business with the capacity and capabilities of the supply chain. In real time, planners can seed with machine learning, plan sales, margin, and inventory KPIs, and collaborate both internally and externally across the entire planning horizon—allowing the business to identify and capitalize on opportunities early in the planning cycle.

That means excess or obsolete inventory is no longer hidden and trapped within the supply chain. And, potential inventory and availability problems that may arise over the lifecycle of the style are highlighted and shared across the organization, ensuring the most profitable decisions are made.

Ultimately, an omni-channel planning process aligns top level and bottom-line strategies with effective demand planning processes for the entire business, ensuring product availability to maximize revenue and margin. Utilizing an AI-based machine learning forecast guarantees a demand signal for accurate planning of wholesale accounts and DTC channels to reduce total inventory—lowering the risk of markdowns, which leads to increased profitability and greatly improved planner productivity.