Ecosystem Orchestration Enabling Order Management for Direct-to-Customer Business Model

In today's rapidly evolving landscape, brands that fail to adapt to changing consumer preferences risk losing their market relevance or, at best, their customer base. For the Retail and Consumer industry to thrive and endure, it is imperative for brands to continuously monitor their consumers’ needs and pain points. Traditionally, brands had limited direct interactions with consumers and relied heavily on third-party intermediaries for accessing consumer data. As a result, brands have been reshaping their business models to establish more direct connections with consumers by embracing the "Direct-to-Consumer (D2C)" approach to sell their products. This approach involves accepting orders directly from consumers. With D2C, brands bridge the gap between themselves and their audiences, fostering more intimate and direct connections.

Today, an increasing number of brands are contemplating a transition to the D2C model. For instance, Adidas unveiled its "Own the Game" strategy, which includes a shift toward the D2C business model, with a projected contribution of approximately 50% of net sales by 2025. 

However, adopting the D2C business model presents its own set of challenges, with one of the most significant being the order fulfillment process.

Figure 1: Order Fulfillment Process Flow

As depicted in Figure 1, brands can ask the 3PL provider to pick the item from warehouse or from their retail store or utilize last mile carriers to fulfil orders from retail store. This choice depends on consumer's preferred delivery time and location, product availability, packaging requirements, etc. Consequently, the complexity of choice varies from one order to another, presenting a significant challenge for brands. 

To operate profitably in the D2C landscape, brands must confront the following related challenges:

Distributed Inventory: In the D2C model, brands must locate inventory that is closest to the consumer. This means that brands count inventory as items available not only in their warehouses but also the items present in the nearby retail stores. To process individual orders efficiently, brands must account for all inventory distributed across this network. 

Order Size Variability: The shift to D2C results in daily processing of orders from consumers, in addition to periodic orders from retailers and distributors. Moreover, order sizes vary from smaller individual parcels to bulk shipments, altering the dynamics of order processing. 

Local Delivery Partnership: To successfully implement D2C model, brands need carrier partners capable of delivering bulk orders in addition to small individual parcels. 

Order Processing Enhancements: Processing daily individual orders poses a different set of challenges compared to bulk shipments at regular intervals. Brands must consider various parameters such as delivery region, delivery time, stock availability, and more when choosing the appropriate fulfillment channel for each order. 

Integrated Ecosystem: In the order fulfillment process, the ecosystem comprises consumers, brands, 3PLs, regional delivery partners, and a network of retailers, all exchanging data daily for multiple orders. Additionally, order cancellations and returns can occur at various stages, from after online payment to after the consumer has used the item for a few days. Different domains, including warehouse, logistics, finance, and customer care, become involved in processing cancellations or returns depending on the stage at which the order is cancelled. Without a collaborative system facilitating accurate information exchange, brands will struggle to implement order fulfillment in the D2C business model. 

To address these challenges, it is essential to examine all stakeholders involved in the end-to-end process and transform it by considering each stakeholder's requirements and contributions.

Figure 2: Order Fulfillment Ecosystem

When examining the order fulfillment process within the D2C ecosystem, three primary types of stakeholders emerge, as illustrated in Figure 2 :

• Consumers: Consumers provide details such as delivery addresses and delivery instructions when placing orders.
• Brands: Brands share the consumer orders with the designated delivery network. Brands require accurate inventory data spread across their network to fulfill orders.
• Delivery Network: The delivery network shares comprehensive order tracking details with other stakeholders once they begin shipping orders.

It is evident that various stakeholders within this ecosystem contribute their data to complete a single process while maintaining their distinct data architecture. However, attempting to manage this disparate data through a centralized system is challenging and costly. Here, the concept of Data Mesh architecture offers a solution. Data Mesh is based on a distributed architecture for analytical data management and revolves around four key principles:

• Domain ownership: As per this principle, each distinct stakeholder owns its data separately managing all the raw analytics, metadata, and computations. For example, a brand’s warehouse department will maintain inventory metrics like inventory turnover, product inventory, etc. but share only fulfillment related details with 3PLs describing the pick-up and drop location. However, 3PLs will maintain route planning data, fleet data, etc. but share only order tracking related details. With this clear definition, different stakeholders can maintain their separate data environment without disruption.
• Data-as-a-product: As per this principle, the data owner creates readily consumable data products for exchanging information with other stakeholders. For example, brands get information like item details, delivery address, and any discounts applied while processing orders. From this, brands should remove additional information like discounts, to create “order data products” describing only order fulfillment details for delivery partners. This ensures that the data products are relevant to the stakeholders and can be readily consumed with minimum effort.
• Self-serve data platform: This platform facilitates one stakeholder to access required data products without concern for disparate data environments of others, enabling collaborative ecosystem.
• Federated governance: As per this principle, the data governance standards are centralized, and each domain is responsible to comply. However, each domain can have additional governance guidelines to ensure data quality. For example, brand’s warehouse domain and 3PL’s order tracking domain can have their unique schema definition requiring additional guidelines to ensure data quality. This enables data to be interoperable ensuring data quality for the entire ecosystem. 

The evolving priorities of the industry and shifting consumer preferences necessitate a revaluation of processes beyond order fulfillment in the D2C model. This includes considerations like enabling KYC, analyzing marketing campaigns, and more, that requires seamless collaboration within the ecosystem while achieving individual KPIs. With our bold vision of enabling Intelligent Ecosystems, Wipro ensures that the entire ecosystem evolves to deliver an enhanced customer value proposition.

About the Authors

Satish Vaidhyanathan 
Strategy and Market Research head, Wipro

Yogit Tembhurne
Strategy & Market Research Consultant, Wipro

Jayakumar Rajaretnam
Consulting Partner, Data, Analytics & Intelligence, Wipro