Despite market turbulence, the digital transformation plans of UK manufacturers are forging ahead, with a focus on investments that will help boost resilience and innovation. Data-driven approaches to manufacturing hold out the promise of leaner, more sustainable operations and agile responses to disruption and changes in customer demand.
Smart strategies for smart manufacturing
For UK manufacturers, market conditions remain challenging. Brexit, the ongoing impact of COVID-19, geopolitical upheaval, soaring energy costs and global supply-chain disruption have all conspired to send supply and demand order books and output on a rollercoaster ride.
With uncertainty set to continue in the sector, the leaders of these businesses are on a mission to build more resilience into their operations and to identify new opportunities for growth.
Many see digital technologies as their best chance of achieving these goals, a view widely shared by their global peers. In KPMG’s Global Manufacturing Prospects 2022 survey of CEOs from the sector, supply chain risk is seen by respondents as the top risk to organisational growth. And the top operational priority they give for achieving growth objectives is advancing the digitisation and connectivity of all functional areas.
Data generated by equipment, employees, vehicles and key business partners holds valuable clues as to how they might mount more agile responses to disruption; run leaner, more sustainable operations; and more accurately predict future demand patterns and customer needs.
UK manufacturers urgently require greater resilience in order to withstand and respond to supply-chain shocks. These have become all too common in recent years, with the impact of COVID-19, but ongoing issues they face, problems include shortages of raw materials/components, transportation bottlenecks, delayed shipments and staffing problems.
But the fact remains that getting a product manufactured and into the hands of customers typically relies on data that resides on numerous systems. These belong not only to the manufacturer itself, but also to the ecosystem of third parties with which it works, including customers, suppliers and logistics companies.
The challenge is to bring that data together to create a ‘big picture’ view of real-time situations, so that partners can work together on effective responses – and in some cases, predict and mitigate supply-chain risks before they even arise.
The first goal of manufacturers intent on tackling supply-chain disruption should be visibility. In other words, they need to assemble relevant data in such a way that it can be analysed to identify risks and potential breaks in the supply chain, preferably before they strike. This potentially involves a wide range of data, not just from internal sources, but also from partners: the availability of supplies, the location of delivery trucks, the order histories of customers. might include data from sensors on warehouse shelves and vehicle telemetry systems, as well as from back-end inventory management and supply-chain application software.
The Snowflake Data Cloud provides a venue in which all of this data can be stored and managed securely, regardless of its origins or format. Elastic scalability combined and performance with usage-based pricing enable means that manufacturers too can cast the net wider and to capture more data from more diverse sources, boosting insights and leading to better decision-making.
Using secure data sharing, partners can work together on enriching data sets with their own contributions, in order to create the end-to-end view that keeps everyone informed about what’s going on across the entire supply chain. This approach vastly improves on traditional data-sharing methods, eliminating the need for copying data and moving it around, a practice which frequently introduces unacceptable data security and data quality risks.
The second goal is agility – the ability to spot a problem and to act on it quickly, based on an accurate understanding of the real-time situation. This depends on applying analytical technologies, including AI and machine learning, so that insights can be drawn from data and presented to those best-placed to act on them, most commonly in the form of business intelligence reports, alerts and forecasts.
In this way, when hit by disruption, manufacturers can alter their plans quickly – perhaps switching to another supplier, using another material or part, or adjusting delivery dates or routes. Data-sharing, meanwhile, means that the wider ecosystem of business partners can be kept up to date on any changes and adjust course accordingly. These collaborations, in turn, can strengthen business relationships, as partners in an ecosystem come to better understand and help resolve each other’s challenges.
Improving insights and business outcomes by leveraging SAP data
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