Agile Demand-Supply Alignment – Part 2E

This article is an excerpt from the report Agile Demand-Supply Alignment — Part Two: Evaluating ASDA Solutions.
A copy of the full report can be downloaded here.

In Part 2D of this series, we examined supplier-facing, quality, and logistics/GTM functional requirements for an ADSA solution. Here we look at demand management and analytics.

Demand Management Functionality

Scope of Demand-Side Visibility

Some ADSA systems focus exclusively on supply-side disruptions and do not consider demand-side data at all. Others ingest forecasts, but do not monitor actual consumption. Solutions with the most complete demand-side visibility monitor end-point consumption, as well as inventory at all downstream tiers between the enterprise to the end consumer.

Figure 1 – Different Solutions Provide Different Scope of Demand-Side Visibility

As shown above, a solution that includes downstream inventory and end-customer consumption provides a picture that more closely matches the reality on the ground and is more complete and up to date than ones that only look at the forecast or sales/deliveries to the immediate customer. The higher up this demand-side visibility stack a solution is, the more precise and earlier the warnings of shortages and outages it can provide.

Time-Phased Views, Simplifying High-SKU Large-Network Visualization

Some of the systems will display a forward-looking time-phased view, making it easier to visualize which SKUs will run out or have excess, including when that will happen and how large and prolonged the imbalance will be. Visualization is trickier when there are thousands of SKUs and hundreds of locations across the network, creating potentially millions of SKU-location combinations. An excellent UI/UX design is required to help the user to easily monitor those millions of combinations, aggregating the related ones and bringing the most important ones to the top. A good UI helps the user intuitively navigate the data and find the required information to resolve the issues. This is one of the areas where various ADSA platforms tend to be differentiated. That is why it is important to get demos of scenarios with high-SKU/location counts within large, complex network settings to evaluate the platform’s UI/UX’s ability to make those scenarios manageable.

POS and Channel Data Visibility

The ability for a supplier to obtain and use retailers’ POS data is not trivial in either the value it brings, or the effort required to realize those capabilities. Services such as IRI and Nielsen syndicate data from multiple retailers, but that data is not as timely or granular as getting daily POS data directly from retailers. Two of the solution providers we reviewed provide services and systems to obtain retailer’s POS data, cleanse it, format it, and bring it into a common canonical data structure so that it can be used in ADSA processes.

Another specialized area is channel inventory data. This is frequently a blind spot for manufacturers selling through distribution channels. Only one solution provider amongst those we reviewed has the capabilities and preconnected network of channel partners required to provide channel inventory visibility off-the-shelf.

Order Pegging

Some platforms provide the ability to peg available supply (purchase orders, inbound shipments, and on-hand inventory) to demand (customer orders and/or store or DC locations replenishment).¹ This lets users know ahead of time how many unallocated units there are in each stage of the pipeline. As soon as units get allocated to an outbound customer order or to a specific location and purpose,² those units are no longer available for a different customer (unless due to significantly higher priority of the new order, the units are reallocated and ‘taken away’ from the first customer’s order or location’s allocation).

Demand Sensing

Some solution providers offer forecasting and demand-sensing capabilities. Demand sensing is particularly relevant to ADSA as it provides a more accurate short-term prediction of demand, incorporating a variety of causal factors such as weather, competitors’ actions, and other events. Demand sensing thereby helps better predict when demand will deviate from the plan which makes the time-phased views of future overages and stockouts more accurate. Some solutions also have specialized demand capabilities, such as managing limited-time offers (LTO) for the restaurant and food service industries and the impact on the supply chain of associated promotional programs.

Retail Planning Capabilities

Some platforms support Open-to-Buy and/or WSSI³ planning capabilities, which helps to ensure demand-supply alignment. A few have deep retail suites, including capabilities such as category management, assortment planning, allocation and replenishment, promotion planning and execution, and product transition planning and execution. These tools all play a role in keeping demand and supply balanced, which is particularly challenging for retailers trying to get the right inventory levels for a mix of size, color, and style at each location across a large and diverse network of stores and ecommerce fulfillment centers.

Collaboration Capabilities

Every one of the ADSA solutions we looked at allows trading partners to collaborate in one way or another. Most provide a way to invite trading partners into an online conversation, often via an email address. These collaboration conversations are often tied to a particular order, shipment, or issue (e.g., shortage or delay), so that participants automatically see all the relevant information displayed in the context of the discussion. Some have case management functionality, providing a more structured approach to creating and tracking issue resolution, including tasks and escalation when steps are not being completed on time.

Analytics

An ADSA platform brings together a tremendous amount of information about demand, customer orders, POs, production status, shipment status, and more. These platforms essentially create digital twins⁴ of the supply chain. Having all of this data in one place, correlated together, presents enormous opportunities to gain value and intelligence via analytics. Some examples of what we see solution providers offering include:

• Supplier Performance — Near real-time KPI reporting on manufacturers’ and suppliers’ performance, such as perfect order rate/trend, compliance, product quality, response time for issue resolution, on-time in-full (OTIF) performance,⁵ and so forth. These can be incorporated into a supplier dashboard, inform ongoing improvement discussions with suppliers, and aid in contract negotiations.
• Forwarder and Carrier Performance — On-time delivery performance, delay reason breakdown, and analysis, exception-free/claims-free delivery rate analysis, carrier load preferences, tender response time, tender accept/reject rate, customer service responsiveness, and information sharing by the carrier (e.g., do they provide accurate real-time location and status, ePoD, etc.). This information can be used to drive performance improvement discussions, as well as aid in carrier selection. Sometimes the data reveals that certain carriers are better at specific routes or handling specific cargo, enabling a more nuanced and targeted approach to selecting the best carrier for different parts of the business.
• Network Lead-Time Analysis — In most ERP systems, the lead time is just an estimate and is often very out-of-date and inaccurate. An ADSA system can provide lead-time data, based on actual events happening within the network, such as the true order-to-ship lead time from suppliers, transit times for each transport leg, and dwell times at each node. The data on actual, up-to-date lead times can be sent to planning and replenishment systems enabling more accurate optimization. Some systems let the user query, aggregate, slice and dice lead time data in various ways–such as by SKU, supplier, lane, node/facility (e.g., CFS, port, DC), carrier, or combinations of those.
• SKU-level Inbound and Outbound Order/Shipment Flows — Historical views of the actual journey each SKU takes through the network, from supplier to importer and outbound to the importer’s customer or retail stores can be informative. Analytics may show statistics by SKU, by lane, by node (DC, CFS, port), etc. Providing data on actual origins and routes enables planning engines to create more accurate supply chain models.
• Landed Cost Analysis and Optimization — Some solutions provide the ability to calculate actual and forecasted total landed cost per unit. The cost components that are included in the total landed cost calculation vary from one solution to the next, but almost always include actual transportation costs. Many include other costs such as duties and tariffs, insurance, and so forth. Analytics may roll up by product category, business unit, geography, or other customer-defined segments to track VAT/duties for financial management at the company. Two of the ADSA platforms we looked at also provide duty optimization tools, to help users consider alternative approaches or locations of assembly and packaging, in order to reduce tariffs and duties in a compliant manner. These can be particularly useful in today’s world of trade wars and fast-changing tariff regimes.
• Carbon Footprint – few providers offer tools for calculating the logistics contribution to the carbon footprint. Only one provided broader CO2 data collection and reporting.

When selecting an ADSA platform, it is worth learning about the analytics provided, to get an understanding of which specific analytic use cases are built in and pre-configured, what data sets can be exported to external analytic tools, as well as whether the analytics are primarily for longer-range strategic decisions or provide in-context near-real-time decision support within the window of execution.

In Part 2F of this series, we talk about the role of implementation, services, pricing, ROI, and TCO when evaluating a solution.

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¹ For example, if a brand owner gets three orders from different retailers for 1,000, 5,000, and 10,000 of a specific item, and the brand owner has 5,000 at their DC, 5,000 in transit, and 5.000 on order (in production at their supplier), they could allocate those units against the orders, based on expected receive dates and the priority of each retailer. In this hypothe­tical scenario, the brand owner would still be 1,000 units short as the total on order (PO to supplier), inbound, and on-hand is 15,000, whereas the orders from the brand owner’s customers total 16,000 units. — Return to article text above
² Such as anticipated replenishment for an ecommerce DC or store location. — Return to article text above
³ WSSI = Weekly Sales, Stock, and Intake, a process whereby retailers monitor actual consumption and perform planning to ensure the right inventory will be in stock. — Return to article text above
⁴ Digital twins are a lot more than just the raw data. To provide a high-fidelity representation of what is happening in the real world, solution providers continuously update current and predicted supply chain flows. Some build out and maintain up-to-date knowledge graphs of the network, using transactional data flowing through the network. These graphs model and represent all of the products, parties, and places in the network, and the constantly changing relationships between them (e.g., this product is now at this location). Sophisticated modeling is required to get a more accurate representation of the real world, for things like understanding the feasibility of various solutions. Some solutions use process mining to understand typical patterns in the network and detect anomalies, such as a stalled shipment. Intelligence is needed to filter out noise in the data as well. Furthermore, an intelligent supply chain digital twin includes the policies, targets, rules, and constraints to identify deviations (e.g., stock threshold breached), as well as prescribe the best course of action. Thus, an advanced digital twin encapsulates a tremendous amount of knowledge and intelligence about the supply chain network. — Return to article text above
⁵ This is especially important when the supplier is drop-shipping to large important customers. Over the past three years, Walmart has been progressively raising the required OTIF performance for suppliers from 70% to 98% and is now levying a fine of 3% of COGS penalty for non-compliant shipments. That kind of customer mandate makes OTIF analytics even more important. — Return to article text above

To view other articles from this issue of the brief, click here.