Agile Demand-Supply Alignment – Part 2A

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.

This is the first article in Part Two of our research series on Agile Demand-Supply Alignment (ADSA). We define ADSA as “the capability to effectively realign supply and demand, during execution, in the face of demand volatility and supply disruptions.” Regardless of how good companies get at planning, ‘stuff happens’ and companies then need to be able to rapidly adjust to the evolving actual situation on the ground. Part One describes the phases of supply chain planning and execution and lays out a framework for understanding the Elements of ADSA, as shown in Figure 1 below. Part One also includes examples of specific ADSA capabilities, such as In-Season Reordering, Agile Customer Mandate Compliance, and Opposite Hemispheres Strategy.

Figure 1 – Elements of Agile Demand-Supply Alignment

Here in Part Two, we look at how to evaluate ADSA solutions, what questions to ask solution providers, and how to shortlist providers.

What Problem(s) Are You Trying to Solve, Now and In the Future?

Agile Demand-Supply Alignment (ADSA) is not a category of software, per se — rather, it is a set of capabilities (systems, processes, and practices) to better align demand and supply. Nevertheless, in this report we talk about ‘ADSA solutions,’ i.e., software and services that help companies achieve Agile Demand-Supply Alignment. Most of these are not marketed or sold as standalone ADSA solutions,¹ but rather the ADSA functionality they provide is a portion of some broader set of functionality, that may be an ERP suite, or a supply chain suite, or some best-of-breed soft­ware such as a sourcing and procurement system, quality, issues resolution, or demand management/”‹forecast­ing software.

Thereby, buying ADSA solutions is usually wrapped up within an initiative to buy one of these broader or more focused enterprise software solutions. Users are often looking for solutions for their particular functional area, such as a logistics manager buying a transportation management system or a demand planner seeking a better forecasting system. These functional systems can help solve demand-supply mismatch because you need to know when a shipment is delayed or how actual demand differs from the forecast. However, each of these solutions only provides a piece of the puzzle. Supply chain processes are inherently cross-functional² and inter-enterprise so these systems should be integrated together to provide holistic situational awareness and identify and execute a globally optimal response to issues that arise. To achieve an optimal response requires a holistic platform that can see and execute actions across processes. Buying functional solutions without regard for how they will be integrated into a holistic platform is shortsighted. When evaluating enterprise solutions, immediate needs should be balanced with the longer-term needs and vision for the future.

Agile Demand-Supply Alignment encompasses a broad range of organizational functions, processes, and systems across the enterprise and between trading partners, as shown in Figure 2 below. This is not just about the technology; it includes the people, practices, and systems within each functional area. Ideally, an ADSA solution integrates teams, data, workflows, and systems from across all these areas, incorporating trading partners and third-party service providers (e.g., 3PLs, forwarders, etc.) as well.

Figure 2 – Many Organizational Functions and Systems Required to Achieve ADSA

Questions to Ask Solution Providers

Data and Supply Chain Visibility — Getting the Complete Picture

ADSA requires network-wide data about inventory, demand, supply, and logistics:

• Data and Visibility Potential Questions for Solution Providers:

  What data does your platform ingest to provide visibility? How is that data obtained (e.g., via EDI, supplier portal, API, XML, etc.)? Does your solution incorporate network-wide inventory (across multiple tiers)? At what level of granularity and scope — down to the SKU-location level? For which locations and entities? Does it incorporate forecast data? Near-real-time consumption data? Does it incorporate near real time order and production status? What data is incorporated and how is that obtained; from what sources? Does it incorporate near real time logistics status? What granularity of status and what milestones are incorporated? How is that data obtained? Which external systems (e.g., SAP, NetSuite), service providers (e.g., 3PLs), and trading partners (e.g., Walmart) do you provide pre-built connectors for? What is the effort required to implement pre-built integrations? Do you integrate data from proprietary systems? How is all of this data presented and visualized?

  Inventory – An accurate and up-to-date picture of per-SKU, per-location inventory across the network. This starts with data feeds from a company’s own perpetual inventory systems, for their factories, DCs, and stores. But it really should also include partners’ inventory, including upstream finished goods inventory at suppliers’ locations, in-transit inventory in containers and trucks, inventory at service partners such as 3PLs, and downstream inventory at channel partners, retailers, and customers’ stocking locations. A broader network-wide picture is harder to attain but provides a more complete picture of supply and enables finding many more options for resolving shortages (such as using inventory from one dealer to fulfill demand at another).
• Demand – At a minimum, a company’s own forecasts should be used to understand expected future demand. On top of that, demand sensing can help detect when there are changes in demand that are deviating from the forecast. Ideally, the solution helps enable a demand-driven supply chain, where visibility into end-user consumption and downstream³ forecasts, consumption, and inventory data enable more accurate supply-chain-wide forecasting and execution. This is part of what CPFR was trying to accomplish. This turns out to be hard to do for a variety of reasons, so not many companies are able to pull it off. However, FMCG companies and some others are leveraging tools to pull together POS,⁴ syndicated retailer data, and other data, combined with analytics, to give them a demand sensing capability that incorporates end consumption. Demand sensing can include both the actual consumption information from the most downstream point possible (ideally end demand – such as POS or stock room withdrawals), as well as casual factors that have arisen that might not be considered by the traditional forecast tools (such as weather or major relevant events).
• Supply — This starts with full visibility into all supply-side orders, including purchase orders and/or material releases being sent to suppliers, as well as work orders for internal manufacturing. The ability to track the status of an order throughout its lifecycle (issuance to receipt and payment) should include both production and shipment phases, thereby encompassing the logistics visibility described below as well. Ideally, there is visibility into product status at each stage, including knowing about delays in inbound raw materials to the suppliers’ or your own factories. Most firms have some level of visibility into their own factories, but often have poor visibility of the status of orders at suppliers’ factories. More broadly, it is valuable for the platform to incorporate visibility into Industry-wide capacity constraints and material shortages, as well as disrupting events, such as storms or political upheaval or pandemics, with insights into how those events will impact supply for the company using the solution.
• Logistics – DSA requires the ability to see the status of shipments correlated to the purchase orders, sales orders, and stock transfer orders that the shipments are fulfilling. This includes seeing when shipments are booked, tender­ed, and have passed various milestones on their journey such as ‘left factory’, ‘arrived at consolida­tion center’, ‘loaded onto ship’, ‘departed origin port’, ‘cleared customs’, and so forth. More advanced platforms will also ingest data to provide a more precise ETA,⁵ such as weather, port and road congestion, and major events.

Understanding the sources, granularity, and accuracy/reliability of the data is important. For example, the platform may show the date that the order is estimated to be shipped from the supplier’s factory. If that date is based on the expected ship date stored in the buyer’s ERP system, it might be out-of-date and inaccurate by days or even weeks, and not reflect the actual delays that have happened after the order was placed. At the other extreme of advanced visibility, the supplier’s factory might be instrumented to automatically track each production step, and that data automatically sent in near-real-time to the buyer’s supply chain visibility platform. In that case, the buyer will have a highly accurate, granular, and up-to-date picture of the current status of their order. They will know within hours, or even minutes, when a milestone has been missed and a delay has occurred. Most implementations will be somewhere in-between those extremes. For example, a common approach to supplier visibility is using a supplier portal, relying on the supplier to update the status of their order in a timely manner.

In Part 2B of this series, we present some questions to ask ADSA solution providers about how they detect, contextualize, and prioritize issues and demand-supply misalignments that arise in the supply chain. We also ask what to look for regarding how the solution predicts and prescribes the best resolution to the issues it detects.

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¹ One exception to this statement is ‘control towers,’ an emerging category of solution. Control towers have been evolving and expanding beyond the initial definition to include to more predictive and prescriptive platforms, and more integrated planning and execution. Agile demand-supply alignment is one of the main purposes of control towers and their next generation iteration (sometimes called autonomous supply chain platforms). — Return to article text above
² For example, a procure-to-pay process involves order management, supplier management, transportation management, inventory management, global trade management, warehouse operations, and more. — Return to article text above
³ Including visibility through multi-tiered distribution channels — Return to article text above
⁴ POS = Point-of-Sale — Return to article text above
⁵ ETA = Estimated Time-of-Arrival — Return to article text above

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