The pandemic exposed worrying fragilities in global supply chains, creating an urgent need to embrace a more intelligent approach which eliminates data silos and increases visibility
The severe circumstances of the Covid-19 pandemic forced organisations to reflect on the processes that are yet to reach digital maturity, holding supply chains back. The sudden shift in consumer demand, coupled with lockdown-related production outages, created a highly visible supply crisis documented by images of empty shelves in shops. Less visible impacts were felt even harder by businesses.
Despite their best efforts to adapt as best as they could, the lack of visibility and flexibility within supply chains was already so deeply entrenched that delays were almost impossible to avoid. Fragmented IT infrastructures, over many years, have created data silos that prevent ready access to critical information when it matters. An inability to make informed decisions quickly, based on the most up-to-date information, hinders organisations from responding to sudden supply issues or changes in demand, leaving companies scrambling during times of disruption.
“There has been significant supply disruption in both the first mile and last mile of demand fulfilment,” says Mark Holmes, senior advisor for supply chain at InterSystems, a leading provider of data technology. “Keeping manufacturing up and running has been incredibly challenging, as well as rebalancing inventory to meet customer demand through dramatic changes in buyer behaviour. The shelves have often been either bare or not the typical products we like to see.
“All of this has highlighted the inadequacies of a linear non-digital supply chain, as well as inefficiencies in predicting and managing the impact of constraints in the supply chain. These inadequacies were always there but have been exposed by the pandemic. Companies need actionable insights in a more collaborative and digital supply chain. The only way to get out of firefighting mode is to really accelerate digital transformation and improve supply chain visibility.”
In a recent InterSystems survey of retail, consumer packaged goods and manufacturing organisations, 83% of respondents said the pandemic worsened their technology-specific supply chain challenges. The most prevalent problem is the lack of flexibility in existing processes, cited by 43% of supply chain professionals, but many are also experiencing other data-related issues, including a lack of access to current data and accurate end-to-end visibility, and difficulties performing analytics and integrating and normalising disparate data.
All of these challenges existed long before the Covid-19 crisis and will continue to cause problems when the pandemic finally ends, if organisations don’t improve their data management and overall supply chain visibility. Commercial off-the-shelf software and siloed supply chain applications are not sufficient. An additional data management layer is needed to enable organisations to overcome the issues they are facing, removing silos with a single, accurate and current view of data that helps them gain better visibility internally and across their supply chain.
“The data problem is spread all over,” says Holmes. “There is no single source of truth, and it’s not a new problem. Previous approaches to data management and integration have been challenged with the complexity of moving data in batch mode, and manually using that data. Disparate data systems are holding back the entire supply chain ecosystem, which the end customer relies on to get the products they want. Putting data at the heart of supply chain resilience allows a company to orchestrate disruption and constraints of supply and demand within the supply chain to meet fulfilment in a predictive manner with a high degree of accuracy.
“It’s exactly the problem that InterSystems has been solving for decades. We connect disparate data sources and enhance visibility. It’s in our DNA. We identify critical business initiatives, including understanding the impact of variations in demand and supply, optimising replenishment centres and inventory rebalancing. Our architecture is built around a smart data fabric, an exciting new approach which allows us to connect to core systems and an ecosystem of supply chain partner systems, to access data on demand while also reducing complexity and accuracy and improving the customer experience.”
Processes such as demand forecasting and inventory optimisation can be further enhanced with the use of the latest artificial intelligence (AI) and machine learning (ML) technologies. Acquiring large numbers of skilled data scientists is an unlikely option for most organisations, least of all when such talent is so difficult and expensive to recruit. Businesses must therefore consider how they can capitalise on the benefits of AI and ML without the requirement for costly expert knowledge.
New innovations, such as InterSystems IntegratedML, allow organisations to easily add advanced analytics to applications without the need for such in-house expertise. The technology simplifies the process of building, testing and deploying ML models, and automates the process of integrating them seamlessly into production applications. InterSystems IntegratedML helps businesses to develop accurate ML algorithms directly within existing data management infrastructure, without requiring a team of skilled data scientists, while embedding the models into supply chain applications to take programmatic actions in response to real time events.
“Intelligent data solutions, and technologies like AI and ML, will power supply chains in the new normal. I cannot tell you how many discussions with our clients that I have where the VP of supply chain is now responsible for manufacturing. Why? Because of the link between intelligence supply chains and smart manufacturing. It is all about decision support to help business users. Our technology analyst referred to this as ‘augmented transactions’, which means embedding AI and ML into business software and workflows.
“With an interconnected supply chain, companies can be predictive and prescriptive around workflows to drive automated action, such as replenishing requirements from different inventory locations to meet peak period demand. Auto ML is a new technology we’re putting into those workflows. There are a lot of challenges around skill shortages in the area of data science and analytics, so building AI and ML into our supply chain workflows is more important than ever.”
For more information, visit intersystems.com/supplychain
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