Transforming the supply chain with unified data management
Companies need technology to manage supply chains effectively, but those same supply chains use a wide variety of software tools to collect data. Thanks to the rise of artificial intelligence and machine learning, platforms have come to market that can help to connect these disparate data streams and derive insights from them.
Many organizations lack the technology and architecture required to automate decision-making and create intelligent responses across the supply chain, as has been shown by the past few years’ supply chain disruptions. However, these critical breakdowns can no longer be blamed solely on the COVID-19 pandemic. Rather, they can be blamed on businesses’ slow adoption of automated supply chain decision-making, which has resulted in inventory backlogs, price inflation, shortages and more. Further contributing to backlogs is continued single sourcing to one region rather than leveraging distributed regional capabilities. These factors have added to the complexity of systems and the disadvantages of lack of automation and the pandemic brought these existing critical breakdowns into stark relief.
This brings us to today and how this inability to effectively manage data streams is proving debilitating to many companies. In a Gartner study of more than 400 organizations, 84% of chief supply chain officers reported that they could service their customers better with data-driven insights. An equal number of respondents stated that they needed more accurate data in order to predict future conditions and make better decisions.
The challenge here is that companies are managing their supply chains with a series of disparate and disconnected tools and datasets. Instead of residing in a centralized location, critical information may be scattered across the supply chain, kept in functional siloes and tied to individual technology solutions and operating teams, limiting transparency and optimization.
Ultimately, this impacts the overall results of supply chain digitalization. Human analysts, as well as advanced technology engines, may have trouble accessing data that is relevant, current and reliable. Data may be segregated across functions, resulting in a lack of end-to-end transparency. Lag times can significantly impact an organization’s ability to sense and respond immediately to disruptions or new information.
End-to-end connectivity across the supply chain
The supply and demand disruptions in 2020 and 2021 clearly demonstrated the need for digital transformation and end-to-end visibility and orchestration. And the availability of new digital capabilities like artificial intelligence (AI), machine learning (ML), data science and advanced analytics has been nothing short of a game-changer for connecting the world’s supply chains. To keep pace with manufacturers’ and retailers’ demand surges, supply chains must evolve to become real-time, adaptive ecosystems.
Whenever an exception or a disruptive event occurs anywhere in the ecosystem, it can be recognized and addressed autonomously in a synchronized and collaborative manner. No matter how geographically distributed the value network is and how many suppliers it includes, today even the most complex global supply chain can be digitally connected via intelligent solutions in near real-time.
The advanced technology that enables near real-time monitoring and communication depends on data for its success. Across the value chain, each supplier is digitally contributing information regarding costs, timing, inventory levels, availability and other key metrics – offering the opportunity for key partners to gain and offer feedback in real time, thus gaining key insights into the evolution of demand.
But that is just the beginning. Today’s forecasting, business planning and execution optimization engines also depend on enormous volumes of third-party data – including news, weather and even social media – that impact end-to-end supply chain performance. Enabled by new, advanced capabilities such as AI, ML and predictive analytics, these new cognitive engines are incredibly powerful and accurate at translating huge amounts of raw data into strategic, actionable recommendations, often autonomously, allowing supply chain teams to shift focus from firefighting to strategic improvements.
Leveraging partners to build a supply chain ecosystem
Digital platforms can bring together these disparate data sources and functions to enable faster decisions and greater collaboration. Unified data management makes companies more agile and flexible in responding to changes. Through a best-of-breed network of partners and internal developers, companies can share data and ideas across teams, enabling real-time response and cognitive planning across stakeholders. However, to deliver a synchronized response across the global supply network, traditional walls will have to be overcome with advanced technology that supports real-time, end-to-end orchestration.
Breaking down these traditional walls requires a partner- and developer-friendly platform, fully integrated across the network, to help democratize data access, streamline data management and encourage self-learning and continuous improvement. Through a digital command center, information can be shared across the supply chain to generate cognitive insights, identify disruptions and opportunities, and recommend strategic actions. These partnerships can transform data into a competitive edge by unifying the entire supply chain around a holistic, truly integrated technology ecosystem.
And as data is aggregated and made accessible to every stakeholder, companies can make intelligent, strategic decisions based on a single set of real-time insights. The supply chain is a robust ecosystem fed by data, and it requires scalability, security, data integrity, real-time responsiveness and exceptional processing speeds. Think about the massive amounts of data from customers, partners and suppliers consumed by companies. Millions of bits of information inundate every network touchpoint. Without collaboration, users will find themselves siloed by their disparate data-driven workflows, making decisions based on slow, incomplete and disconnected data.
To truly harness this vast amount of data, companies should be looking to solutions that support self-learning. Democratized supply chains are not created overnight. They require every partner and function to have equal access to data and optimization engines that take into consideration every outcome and priority – ingesting data and making decisions more rapidly than ever before. Such ecosystems result in supply chains that are strategic, functional and built to withstand today’s fluctuations and obstacles.
Jim Beveridge is Senior Director of Product Marketing at Blue Yonder
This article was written by Blue Yonder and Jim Beveridge from VentureBeat and was legally licensed through the Industry Dive Content Marketplace. Please direct all licensing questions to email@example.com.