Empty supermarket shelves and queues of lorries at British ports are only the most visible signs of a supply chain under stress.
Behind the scenes, today’s chief supply chain officers (CSCOs) and managers are under daily pressure to deliver goods on time, reduce costs and increase profit while mitigating issues and minimising risks. They must be aware of geopolitical issues and labour shortages as well as resource demands, cost pressures, transport and logistics delays, energy inflation and data issues like siloes and lack of visibility.
In a complex and increasingly automated world, it’s no surprise that 82% of CEOs in supply chain-intensive industries plan to increase investments in digital capabilities across their enterprises in 2023 and beyond.
According to Gartner, 95% of supply chains must quickly react to change, but only 7% have the information they need to execute decisions in real-time.
Consequently, many CSCOs are considering technologies like robotic process automation (RPA) and intelligent document processing (IDP) to solve their problems. They hope to achieve better insights, improve predictive analysis, mitigate risks and improve the accuracy of their financial forecasting with real-time data.
A chain of disconnected data
Achieving better results means replacing or updating legacy systems, manual processes, connecting and analysing data traditionally stored in self-contained databases or information silos.
When digitising information and automating processes, it’s vital to understand and address common factors that led to their creation of these systems, including:
– Organisational expansion: When organisations expand through acquisition, they introduce new business units with different capabilities or systems. If a business does not address and integrate these differences from the offset, a mismatch of siloed systems and structures can quickly result.
– Hierarchical structure: Supply chain functions such as planning, procurement and logistics often involve different departments or teams within a company. Yet the focus of specific activities, like performance incentives at a departmental level, often turns inward. Teams may become insular and give minimal consideration to what they are ‘throwing over the fence’ to another department down the chain. This results in unresolved issues trickling downstream to the finance team, who must then dedicate resources to issue reconciliation.
– Incompatible systems: Large, dispersed organisations often build stand-alone data systems as a quick fix for tasks like referencing and identifying individual suppliers. Ultimately, these systems are incompatible with others in the organisation, and gaps form between departments. What’s more, “hidden factories” of offline manual work to combat these gaps emerge, rendering the data unavailable to others in the organisation.
Data silos and manual processes derail the flow of information, hampering operations and hindering data collation. Data silos also make it incredibly challenging for supply chain organisations to understand and analyse the market landscape and harness the advantages of powerful AI analytics.
Fortunately, new technology solutions have emerged that make integrating, accessing and analysing data more viable and affordable and less disruptive.
Consolidating information with a data fabric
A key part of digital transformation is addressing siloed data. But this is easier said than done. According to McKinsey research, approximately 70% of digital transformation projects don’t achieve their desired goals, and the difference between success and failure almost always lies in leveraging existing processes and data.
Data fabric is a powerful solution for companies that need to pull data from a wide array of sources. These solutions differ from older data management architectures because data stays in its source systems, both on-premises or in a cloud service. Users across the organisation read, update, create, and delete it directly from an integrated abstraction layer. This unified architecture brings together a variety of integration tools to deliver consistent capabilities across it all.
The data fabric process
Data fabric creates a virtualised layer across any system connected to it, unifying all enterprise data into a single environment. Critically, this approach ensures that employees have all the information they need to effectively respond to any supply chain issues.
Supply chain automation in 2023
Supply chain automation is critical to operating at scale. By automating manufacturing production tasks, for example, organisations can produce more products faster, with consistent quality. Supply chain automation also helps retailers at the front end of the supply chain deal with increased volume of last-minute orders and speed up customer deliveries.
Automation also helps improve supply chain agility. When a company sets up automated alerts for signals of customer demand, it can adapt faster. The procurement team can start ordering products and materials sooner and avoid shortages.
Automated notifications can also alert companies when suppliers are running behind schedule or lack manufacturing capacity. In these situations, the company can find alternatives or arrange expedited shipping.
Supply chain automation does not replace human workers; it augments them. For instance, the world still needs truck drivers, airline pilots, and ship crews to continue routing deliveries in most instances. And automation tools can make teams working across the supply chain more effective.
Implementing modern low-code platforms
Modern low-code application development enables developers to create solutions up to 10-17 times faster than with traditional coding. Developers can also more quickly and easily change and update an application on a low-code platform. In supply chain management, this responsiveness to change provides a competitive advantage, especially during market turbulence.
Low-code solutions turn operational inefficiencies into AI-powered process automation. Developers and business users can create enterprise applications with a drag-and-drop interface, visualise workflows, and automate tasks to adapt to changes faster.
The ability to quickly connect to vendor and supplier systems, even if an API isn’t available, provides much-needed visibility, for decisions powered by up-to-the-minute data and intelligence. The right platform enables quick, secure, and scalable application development, so if the organisation requires a new type of application or process modification, teams can have it out the door faster.
Unlocking proactivity with AI process automation
Finally, modern low-code platforms enable organisations to transition from traditionally reactive data analytics to AI process automation and a more proactive approach. In a marketplace characterised by frequent disruptions, having real-time insights for action and pivots provides a huge advantage. Organisations improve decision-making, resilience and agility by getting in front of disruptions before they occur.
Autonomous access to data is a transformational change for supply chain management. Instead of using their resources to identify disruptions, teams will already know where they are and be able to use their time more strategically on resolutions. Departments can deal with issues whilst simultaneously working on improvements for prevention or elimination.
With supply chain issues becoming increasingly complex and frequent, many organisations have found out the hard way that a failure at any one point can cripple an entire system. Digital transformation has become a business priority and speed and agility true competitive differentiators. Using data fabric and process automation with a low-code approach check all of these boxes, enabling businesses to detect and respond to disruptions sooner for better operational agility and service delivery.
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