Supply chain technology leaders face an uncertain future — one that comes at them faster and faster every day. But thinking about the future is often not a priority. Because of technologically driven disruptions, these leaders will play an increasingly important role in forecasting the future. Yet it’s not always clear what an individual trend in application architecture means to supply chains or IT organizations.
Supply chain application architecture is complex and costly. There are many reasons for this, including each individual supply chain domain having a best-of-breed application strategy or because additional portfolios have come in through mergers and acquisitions. As a result, we see companies having to maintain multiple solutions from multiple vendors — or even multiple solutions from a single vendor — exerting all the effort needed to make integration possible. But what options do supply chain technology leaders have for modernizing and simplifying their supply chain application architecture?
Gartner has created three supply chain management application architecture scenarios that consider the factors influencing supply chains across industries through 2025. Companies can build their application architecture using the appropriate pieces of these scenarios.
The Three Scenarios
End-user organizations are looking to the integrated application platform to reduce complexity and lower integration costs. The market offers two architectural alternatives. There’s an engineered platform, which is a single, or common, system of record with process models generally implicit. This offers a single architectural foundation with a common user interface and data management model. The other is a synchronized platform, consisting of multiple systems of record but tightly integrated. Process models are likely implicit, but with boundaries.
Critical components that play a foundational role in the rise of integrated application platforms:
The breadth of different core functional applications connected through intelligent workflows, in which the output of one app acts as input to the next.
A single data and process model, allowing smooth execution along the supply chain and delivering a unique user experience through a single look and feel.
The second scenario is modular, composable services. A microservices architecture — a variant of the service-oriented architecture (SOA) structural style — arranges an application as a collection of loosely coupled services, organized around business capabilities. Like in warehouse management we’ve recently seen a modernization of traditional application architectures into “composable” applications along microservices. However, it should be noted that simply having this architecture and these features does not mean that a vendor is providing an off-the-shelf solution. Incorporating these architecture principles is certainly simpler for vendors with newly built solutions. It is much more difficult to modularize older solutions and break them into components that can then be reassembled and enhanced according to a customer’s needs.
The third scenario relates to intelligent data tools. Data resides everywhere in silos, not connected and often not shared. Processes are stand-alone, function-centric, not aligned and collaborative, not to mention converged. This is where organizations now try to reimagine their business through data-powered innovation, using end-to-end big data as an engine to enable individualized processes. Of course, this takes a bimodal approach, optimizing and transforming their existing landscape. Such tools facilitate advanced data processing and allow for the delivery of insightful information, predictions and suggestions. In essence, it consists of the pipelines from acquiring, processing and analyzing data, but not yet responding and delivering it, which would require additional technology capabilities.
How to Navigate to a Possible Future
By nature, supply chains are multienterprise and network-centric. Multienterprise-centric applications face tremendous issues in how to bring together diverse data — internal and external, structured and unstructured — allowing for orchestration processes as well as cross-functional analytics and intelligence. Companies must deal with thousands of downstream, upstream and sideways partners to connect. That burden increases exponentially. But organizations still face other challenges:
The need to deliver innovation and adapt more quickly to respond to the accelerating pace of business change.
Customers and employees increasingly expect more contextualized and personalized application experiences.
Current application portfolios were designed to address the challenges of the past. They are an obstacle to innovation. However, they cannot be replaced wholesale due to the cost and risks associated with major change.
There will be no single winner here. Companies will leverage pieces of each scenario for innovating their supply chain application architecture through 2025 and beyond.
There will be some early adoption of modular, composable services in the supply chain. However, there will be greater adoption in more enterprise-centric applications, those which support business capabilities within the four walls of an organization, like warehouse management or traditional transportation management. At the enterprise level, this seems feasible. IT can easily manage the connections to the internal systems it controls from a technical and economic point of view.
Companies that have already deployed an integrated application platform will continue to roll out selected functionalities, replacing stand-alone tools. With that comes less integration effort and cost. And vendors will also not sleep, modernizing the architecture toward more service orientation.
Nevertheless, organizations are also exploring intelligent data tools next to all of their other efforts. Why? Because these tools allow them to gain more connected data insights, be more flexible for targeted use-case adoption and realize benefits quicker. As with the adoption of any new technology, deploying tools for data intelligence requires a bimodal approach for success. Supply chain leaders who can navigate disruptive innovations using data intelligence alongside the need for stable growth can win the day.
Gartner Supply Chain
This syndicated post originally appeared at Gartner Blog Network