Profitable Fulfillment: Part Three--Enabling Capabilities
on Sep 28, 2016
Profitable fulfillment can be enabled through a combination of 1) precise, network-wide visibility, 2) network orchestration capabilities, and 3) networked order management. Here we describe what these capabilities mean in practice.
Part two of this series describes proven strategies for achieving profitable fulfillment, including segmentation and orchestration of fulfillment service providers, optimizing postponement with outsourced partners, and multi-party inventory pooling. Here in the third and final article in this series, we look at the underlying capabilities needed to enable those strategies.
Enablers of Profitable Fulfillment
CAT Tackles Ocean Transit/Port Variability
By leveraging its network platform, CAT obtained better visibility into sources of variability throughout the full multi-leg routing and the various costs those were adding such as detention demurrage, and inventory costs. This variability occurred in each stage, such as ocean transit, load and unload, drayage steps, customs clearance, consolidation and deconsolidation, and so forth. In one case, the manufacturer observed a specific lane where customs clearance took place on average 2½ days after vessel arrival and with lots of variability, in many cases over 5 days after arrival (see diagram below). With this visibility and ability to measure and share performance, CAT tightened assumptions and worked with the responsible service providers, and as a result improved the average clearance to 2 days before vessel arrival, while significantly reducing variability so that virtually no loads are taking more than 5 days after arrival to clear. This approach was applied more broadly, and as a result the average variability for CAT’s China to US shipments was reduced from 18 days to 8 days thereby reducing in-transit inventory by 10 days, freeing up $250M of working capital.
The scenarios and new approaches described in part two have only been made possible with the advent of a set of enabling capabilities. These capabilities can be broadly divided into three areas:
Operationally Precise, Network-Wide Visibility
Dynamic Network Orchestration Capabilities
Networked Order Management
Operationally Precise, Network-Wide Visibility
Achieving profitable fulfillment in today’s global, multi-party, omni-channel fulfillment scenarios requires an operationally precise view—accurate, up-to-date, granular visibility across the network of partners, as needed to optimize multi-party processes and flows. There are several dimensions to an operationally precise view:
Inventory visibility is precise, granular, multi-party, near-real-time—Accurate SKU/location inventory counts that are updated in real time, not just for inventory that the company owns, but inventory across the network, on order, being built, and in transit.
Lead times match current reality on the ground—Actual transit times for ocean freight can vary by several days from the estimates supplied by ocean carriers.1
Planning with precision requires knowing the actual average lead times and lead time variability by lane and mode (See sidebar CAT Tackles Ocean Transit/Port Variability).
Dynamic Precise ETA—Rather than using standard lead times, a Dynamic Precise ETA monitors various factors (vessel/vehicle speed/location, weather, port congestion, traffic, backlogs, events on the ground) to provide a much more precise estimated time of arrival. It also updates over time as conditions change. Precise ETA on inbound logistics enables better execution of advanced practices such as merge-in transit or DC bypass.
Costs are accurate, up-to-date, incident-specific—Traditional standard costs or average costs often diverge widely from actual costs in each situation. Without accurate actual cost data, even the best of optimization algorithms will struggle to find the most profitable approach. This includes not just knowing a firm’s internal costs, but knowing a supplier’s or partner’s costs, which are ultimately passed on to the buyer-customer. Getting a handle on the actual cost drivers and cost interdependencies across the network is the only way to truly understand the total cost-to-serve. That data in turn serves as the foundation for optimized decisions.
Condition—For certain types of cargo, accurately knowing the conditions it has been exposed to—such as temperature, humidity, exposure to shock and vibration, and other types of condition sensing—can be an important element of profitable fulfillment. For example, around 20%-30% of produce is lost to spoilage before it is consumed. Precise, granular temperature tracking2 can be used to drive process improvements from harvesting to precool to handling in trucks, loading docks, and DCs to dramatically reduce losses. Having a shock sensor on expensive, sensitive electronic equipment can increase handling compliance when service providers know they are ‘being watched’ and will be liable for damage based on that data. The new IMO SOLAS Container Weight Verification Requirement, effective July 1, 2016, requires all containers to have an actual (not estimated) weight before they can be loaded onto the vessel. Lack of compliance could cause delays in shipment.
Location—Knowing that an item or shipment is somewhere in transit between origin and destination is often not enough. There can be considerable value in knowing with some precision, in near real time, where each shipment is. This can be useful for security (preventing and detecting cargo theft) as well as anti-counterfeiting (knowing that a specific serial number item is not where it should be). It is a key element for calculating Dynamic Precise ETA, which is a foundation for all sorts of other benefits. It is often not cost-justified to attach a satellite- or cellular-connected GPS device to every item or order. In that case, location can be inferred from other data, such as AIS location data of a ship, truck location data, ‘sightings’ of the order at different milestones, current traffic conditions, current port congestion, weather, and other data. Algorithms are getting smarter all the time about using these combinations of data to calculate more precise shipment location.
Dynamic Network Orchestration Capabilities
In addition to an operationally precise network-wide view, a platform needs to provide dynamic orchestration across all the various trading partners and service providers involved in the end-to-end fulfillment processes. These include capabilities such as:
Dynamic Multi-Party Available-to-Promise—This requires an ATP (available-to-promise) engine that can see and manage inventory that is owned by multiple parties, across many locations, and dynamically commit that inventory to specific customer orders. This requires synchronization between each player and the network, creating and maintaining a synchronized, near real-time, shared view of what inventory is available for sale. When a dealer or partner or store or ecommerce site sells a unit, it is immediately taken out of the shared pool of inventory available for sale, regardless of which enterprise or entity has sold it. That pool of available inventory may include units that are still in production, or in transit, or returned items sitting in a returns processing center.
Dynamic Multi-Echelon Available-to-Promise—Multi-echelon ATP refers to the ability to reliably promise items that are still in the process of being built or transported or at different tiers of distribution. This requires accurate visibility into the current status of production at suppliers, as well as dynamic precise ETA for goods in transit. These estimates of production completion and transport arrival dates must be dynamic; updated the moment conditions change. With Dynamic Analytics (see below), predictions are updated well before a milestone is missed, based on a variety of predictive data (internal and external) and various algorithms, such as taking into account weather, other orders being built, other ships arriving at a port, and so forth.
Dynamic Multi-Echelon Postponement—An enabler of Dynamic Multi-Echelon ATP, this is the ability to wait until the last moment to decide where items will be shipped from for different suppliers or tiers in the chain. This requires the ability to remotely, instantly assign, control, and automate the ‘postponement functions’ at those various suppliers and partners, such as customer- and order-specific assembly/kitting, customer-specific packaging and labeling, and so forth. Thus shared inventory at many different locations can be dynamically pooled across all customers, while still meeting the widely varying requirements of individual customers.
Supplier Shipment Automation and Orchestration—This is orchestrating multiple third parties—such as contract manufacturers, packaging firms, third-party logistics, carriers, and so forth. The platform should provide automation and control of end-to-end fulfillment processes such as assembling, labeling, packing, and shipping, regardless of who is doing them. Streamlining these processes is required to efficiently scale to high volumes while improving reliability and reducing lead times.
Dynamic Analytics—Dynamic analytics provides situational awareness based on conditions on the ground across the network. When there is a change in weather, congestion at ports, a fire, or other event that might cause a slowdown or stoppage at or near one of the locations in the fulfillment network, the appropriate persons are alerted, potential impacts analyzed, and ideally potential remedies suggested. Dynamic Precise ETA and other types of proactive alerts are enabled by this type of analytics. This requires accurate network-wide data feeds and geospatially aware complex event processing capabilities.
Network-wide KPIs and Dashboards—The performance of the various players, manufacturinglocations, lanes, and services involved in fulfillment must be measured,monitored, and made visible to authorized stakeholders, including the partner providing the service. These should highlight both average performance and variability, identify underperforming and better performing suppliers and locations, and help pinpoint factors that may be affecting performance. This forms the basis for continuous improvement across the network; continually working on reducing the cost of transport, cost of delivery, inventory levels, service levels, cycle times, encouraging collaboration, and other factors contributing to success.
Networked Order Management
Distributed Order Management (DOM) has been around for over a decade, but is only now getting more attention and some adoption as omni-channel capabilities are becoming increasingly critical. A new set of capabilities called Networked Order Management (NOM) is emerging to support more flexible, multi-party scenarios. This includes managing orders to OEM/brand owners and directing how those get executed by contract manufacturers, logistics providers, and other upstream partners. It also includes more flexible approaches to last-mile delivery. This is an area we plan to explore further in future papers.
Fulfillment has become a key differentiator and determinant of success.
Fulfillment capabilities have become as important as the product itself. Failure to deliver in the desired manner and time has devastating consequences. At the same time, the challenges are increasing. Fulfillment requirements continue to get more complex, varied, and personalized. There are an increasing number of third parties involved in fulfillment scenarios.
To survive and thrive in this new world requires a new way of thinking. It requires ‘network thinking’— a network-first approach to manufacturing, inventory, shipments, orders, and fulfillment. It requires coordinating the network of players across end-to-end fulfillment scenarios. A network-first approach is the key to better execution in today’s world. It is the key to satisfied and loyal customers. It is the key to profitable fulfillment.
1 Ocean carriers’ estimated transit times are typically days longer than the average transit time, to ensure that considerably more than 50% of shipments will arrive within the estimated time. -- Return to article text above
2 I.e. tracking temperature at a case or pallet level, from the field/point of harvest to the point of delivery/sale to the end consumer. -- Return to article text above
To view other articles from this issue of the brief, click here.