With the ongoing shift in consumer behavior, we’re experiencing a considerable increase in demand for distribution network modeling of e-commerce supply chains. There are notable differences when modeling networks that supply retail versus support e-commerce. Here are eight crucial points for successful e-commerce modeling.
One: Model in units of measure consistent with e-commerce demand.
Distribution networks are often modeled in units of measure that readily support transportation or warehouse costing (e.g., units, pallets, cases, or weight). However, using such traditional units of measurement makes it challenging to apply fulfillment capacity constraints or represent the impact of creating split shipments due to different deployment strategies and consumer ordering behavior.
Representing demand in the same units of measure that drive utilization and cost (e.g., orders, order lines, or shipments) is essential to creating a distribution network that meets real-world e-commerce demand and provides decision-makers with confidence in proposed solutions.
Two: Develop an understanding of daily e-commerce demand variability and the mix of order line types.
Distribution networks are often modeled as single-period models due to the inherent complexity of representing the many flow-paths products that can follow through the network. In contrast, the complexity of modeling e-commerce fulfillment isn’t typically rooted in the number of flow paths that products can follow through the distribution network. Instead, the complexity is more likely found in the various types of fulfillment nodes required to serve lumpy daily demand and the mix of e-commerce order lines and omnichannel order lines.
Representing the lumpiness and mix of demand is vital to creating a distribution network that meets real-world e-commerce demand and provides decision-makers with greater confidence in the proposed solutions.
Three: Clearly articulate the difference between transit time to customers and click-to- delivery time.
Typically network models only measure transit time to the customer. However, shippers are usually more interested in measuring click-to-delivery time because that’s the waiting time e-commerce consumers experience. Click-to-delivery time is influenced by transit time and the time of day the order is placed, how long it takes to fulfill the order, and when orders are processed for fulfillment and shipping (order cut time).
Articulating the relationship between transit time and click-to-delivery time supports more informed decision-making by precisely describing the cost and service trade-offs in terms that often make the most sense to decision-makers.
Four: Use accurate transit times and be aware of potential conflicts between delivery services and desired transit time requirements.
It’s not uncommon to use distance as a proxy for transit time in distribution network models, particularly when most freight moves by full truckload.
However, it’s important to use actual transit times for e-commerce modeling. Using actual transit times supports informed decision-making and provides decision-makers with greater confidence in the proposed solutions. In addition, delivery services like USPS hybrid services, UPS SurePost, and FedEx SmartPost offer attractive rates but lack consistent transit times. Generally, you shouldn’t consider these types of services if desired transit times are three days or less.
Five: Consider actual shipment weight as opposed to an assumed or average shipment weight.
In many cases, package weights are such that a shipper pays comparable rates to ship packages over noticeably different distances. As a result, service requirements and facility capacity may be a more significant determinant of deployment than transportation cost. Therefore, ensuring that transportation cost is accurate improves accuracy and better-informed decision-making.
Six: If you can fulfill e-commerce orders from both distribution centers and retail stores, it’s vital to have an accurate landed cost for each store.
Developing the cost to land products at each store usually involves a level of detail that is typically out of the scope of e-commerce only projects. Often the scope of an e-commerce project assumes it’s only important to understand the landed cost to each distribution center.
Allowing enough time and resources to develop an accurate landed cost by store supports more informed decision-making by clearly describing the cost and service trade-offs of using all possible e-commerce fulfillment nodes.
Seven: If different products or orders incur additional distribution handling costs and capacity utilization requirements, you should model them differently.
Distribution networks are often modeled using a single set of distribution handling cost and capacity utilization assumptions because all products will be deployed to every distribution node. Therefore, all products within the same distribution location often incur a common handling cost and capacity utilization.
In contrast, for e-commerce networks, it’s often important to represent more discreet handling costs and capacity utilization rates. One of the main questions of an e-commerce network analysis isn’t just “how many distribution centers” should we have and where?” but “given the distribution centers that we have, which products should you deploy at these distribution centers?”
Eight: Develop an understanding of which markets represent lucrative opportunities from a demand density perspective.
Understanding which markets you can serve from existing fulfillment nodes – and understanding overall order density throughout the country – is vital to facilitating candidate site selection and focusing the analysis on markets with the most significant opportunity for improvement.
Implementation of an e-commerce model can be a very exactingly complex and lengthy process. So reach out to us and find out how Chainalytics can help you optimize e-commerce time-to-fulfill and improve customer service levels. Using one-of-a-kind tools and methods like digital assets and managed analytics services, we consistently deliver actionable insights and measurable outcomes to our clients.
Jeff Zoroya is a Principal in Chainalytics’ Supply Chain Design practice. Jeff leads data-driven engagements in supply chain network design and strategic planning across all major industries, including Retail/Wholesale, Food & Beverage, Consumer Goods, High-Tech, and Industrial.
