Many industrial companies across the UK are exploring the benefits of digitalisation. A recent European digital health study from Zoho, for example, reported that 33 per cent of large UK businesses have a good digital health rating — meaning they have successfully digitalised. While implementing advanced technologies can streamline operations internally, businesses must also consider how processes outside of the business will be impacted. Here Jonathan Williams, partner at AI software consultant Xerini explores how businesses can adapt supply chain management as part of their digital transformation.
Traceability is crucial to maintain the integrity of the supply chain and ensure that products are of a high quality. By having a traceability system in place, companies can track product development across the supply chain and ensure regulatory compliance.
As more businesses embrace the benefits of digital data management with technologies like artificial intelligence (AI), they can streamline this process and improve visibility. However, unless the rest of the supply chain transforms in the same way, businesses must still deal with manual processes elsewhere in the chain.
Paper-based problems
The International Chamber of Commerce estimates that four billion paper documents move through the global trade system on any given day. Dealing with paper in an otherwise automated business can lead to inefficiencies, increased costs and potential risks caused by gaps in data.
Other businesses in the supply chain may continue to use legacy systems and keep records on paper because they have limited technology or infrastructure, perceive digitalisation to be too complex or believe that paper records can offer security in highly regulated industries.
While it may be the norm for some businesses, it’s clear that paper records are not always the most effective option. Paper-based systems are often slow and less efficient than digital data collection, with manual data entry creating gaps in information, while increasing the risk of delays and error. Paper documentation can also reduce visibility, as businesses cannot access real-time data on the status of the process outside of their organisation. If global challenges arise, such as political issues, changes in compliance or unforeseen circumstances such as the recent pandemic, paper processes limit how rapidly businesses can adapt and address any problems that might disrupt the chain.
Managing paper-based data from external sources is also time-consuming for the business that has digitalised. Data management systems are typically designed to work with structured, digital data, so integrating data from paper documents requires manual data entry, which can lead to inconsistencies. Dealing with paperwork can also disrupt workflows, increasing operational costs and creating communication gaps.
Integrating AI
To address these issues, digitalised businesses can use AI to support their data management and traceability across the supply chain. An AI-enabled data management platform can sit on top of existing management systems to streamline how businesses handle and leverage information from external sources. AI can centralise data management, enabling businesses to collate all their data from structured and unstructured sources, such as emails and personal digital notes.
When building an AI-enabled data management platform, businesses can work with a software consultant to include tools that will resolve any challenges created by paper-based documents from other businesses. AI-powered document scanning tools, for example, can help convert paper documents into digital formats using image recognition and optical character recognition to extract useful data. Natural language processing (NLP) algorithms can then understand the context of the information, ensuring the tool extracts meaningful information.
Advanced AI models can also streamline how businesses collate this data once it is digitalised. Machine learning models can clean and validate data to resolve any inconsistences, classify the information to integrate it with data from elsewhere and recognise any patterns that could be useful. NLP can provide further support by enabling team members to ask questions of the documents, helping them quickly interpret the document received from a supplier and act accordingly.
Eliminating paper records in the supply chain not only improves transparency between businesses, it can also reduce the time needed to handle documentation, improving productivity. According to a survey by McKinsey, AI can improve business efficiency by up to 40 per cent and reduce operational costs by 30 per cent. Businesses across the supply chain can also more closely collaborate, enabling them to streamline the process and shorten delivery times. Having real-time data enhances decision-making, so if any global issues do disrupt operations, businesses can react more quickly and effectively.
Paper documentation has traditionally played a crucial role in supply chain management, but as industries digitalise their own processes, paper elsewhere in the supply chain can pose challenges. By leveraging AI, companies can effectively manage paper documents alongside real-time data, empowering organisations to make faster, more informed decisions, adapt to changing conditions and create a more resilient and efficient supply chain.
