How data standards can transform supply chains into supply circles

(Credit: Unsplash)

This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum.

Author: Nea Laine, Head of marketing, Machine Compare, Benjamin Findlay, CEO, Machine Compare, David Stroud, COO, Machine Compare


  • The industrial manufacturing sector can find significant sustainability benefits by adopting circular models throughout its operational activities.This transition presents a $4.5 trillion circular-economy opportunity by 2030.Enriched and standardised data can help manufacturers make better use and reuse of stock, addressing waste and inefficiencies and changing how sustainable products are valued in the supply chain.

Traditional linear supply chains face a critical sustainability challenge amid historical disruptions, skills shortages, price volatility and geopolitical uncertainty. To combat this, the industrial manufacturing sector, a significant greenhouse gas emitter, can find significant sustainability benefits by adopting circular models throughout its operational activities. Specifically, data processing and utilisation are crucial for embracing circular principles such as reduce-reuse-repair-recycle.

This transition presents a $4.5 trillion circular-economy opportunity by 2030. Notably, the materials reuse market is estimated to reach a value of $300-400 billion in 2030. With the pressing need to deliver on the UN sustainable development goals within the next decade, original equipment manufacturers (OEMs) and industry players hold significant power in initiating greener supply chains by unlocking the value held in their master data.
Driving industry’s circular transition with data standardization

Industrial manufacturers accumulate surplus spare parts as insurance against unplanned outages, managing these spares in their enterprise resource planning (ERP) and maintenance systems. The critical descriptive data, however, is often unstructured, light on attributes (a type of data that offers additional description or detail on objects or entities), and held in multiple systems. This leads to inefficiencies in inventory tracking, cost analysis and stock optimization.At the most basic level, too much time is spent simply finding the right part. It is estimated that organisations spend between 10-30% of revenue on handling data quality issues. Annually, poor data is costing organisations an average $12.9 million. Incomplete and inaccurate descriptions contribute to prolonged lead times, excess capital tied-up in inventory and an increase in surplus parts ending up in landfills.Small to mid-sized manufacturing facilities often stock spare parts worth $200,000 and scrap around $100,000 annually. Globally, the value of this scrap is estimated to reach billions. In 2020 alone, the UK produced 40.4 million tonnes of industrial waste.To efficiently locate, allocate and move stock, accurate and consistent data is essential. This requires not only high-quality master data but also standardised data labelling within this data. Both aspects are needed to facilitate seamless data and inventory searches, interoperability of information between systems and sustainable inventory management.

Technical dictionaries are instrumental in establishing standardised and accurate master data records within manufacturing. International standard ISO 22745 has been developed as a guide to how an open technical dictionary should be built. It ensures data compliance and universal semantic encoding, while the ISO 8000 standard sets uniform requirements for exchanging master data among business partners, for example, making this data portable. Together these standards empower the creation of quality, uniform descriptions across multiple ERP systems and in any application and exchange of that data. Beyond stock visibility, open technical dictionaries and standardisation enable efficient stock recirculation by preserving the inherent value of items for end users through accurate descriptions. A clear data standard will ensure that items are defined by their critical attributes, capturing the most important element of any value chain: value.The integration of artificial intelligence (AI) and machine learning can expedite this standardisation process especially if all actors in the supply chain – from the component manufacturer and OEMs to the industrial companies operating machinery – work together in promoting and supporting circularity in the supply chain. By aligning data requirements and electronically exchanging master data, companies can continually improve data quality and interoperability, agreeing requirements in line with international standards. An additional benefit beyond the cost savings will be the ability to establish universal performance and information criteria, such as in environmental, social and governance (ESG) or other annual sustainability reporting.

Redefining the value of sustainable items in supply chains requires a cultural shift
Redefining the value of sustainable items in supply chains requires a cultural shift Image: Machine CompareRevolutionising sustainable products with a value-driven approach

Enriched and standardised data also offers opportunities for the reuse of products – though defining their user value is more complex than for new items. Preconceptions surround sustainable products, such as recycled materials, and this can hinder widespread adoption, as they are often perceived as inferior in quality compared to new products. The challenge for circular adoption in supply chains is therefore twofold: establishing the visibility and searchability of sustainable items and carefully (re)defining their user value in the market.This calls for an important cultural shift. For surplus spare parts it means looking at corporate practices, like procurement and inventory management, and tilting these in favour of greener options – specifically creating the desire to move pre-owned (but not pre-used) spares between industrial facilities and facilitating this trade with more accessible, trustworthy and accurate data.Initiatives like our Green Parts Promise (GPP) at Machine Compare are pivotal to the circular transition. Like Fair Trade, the initiative rewards and distinguishes green traders and parts with a globally recognized symbol. This helps generate visibility, redefine the value of surplus and give new meanings to surplus parts. BHS Corrugated is a major OEM and early adopter of GPP, helping to promote increased credibility and trust of green parts in the market by showcasing its surplus to a global network.

Bending linear models for a circular future

The possibilities housed within the human spirit are infinite, but our resources are not. To revolutionise supply chains and transform them into supply circles, we must embrace uniform standards and transparency around resources, their searchability and user value. Data standardisation lays the groundwork for facilitating circularity, but cultural shifts are needed to redefine usability and encourage adoption in the market.The great news is that we already possess many of the tools needed to transform our linear supply chains. By taking advantage of standardised information defined through the lens of value we can inspire the positive changes needed in decision-making, policy and reporting that can safeguard our planet.“We support environmentally conscious traders and materials. Our commitment to the Green Parts Promise initiative helps drive an important circular market transition and message – inspiring a move away from the traditional linear models of take-make-dispose,” says Thomas List, Group CFO of BHS. “We encourage all industry stakeholders to actively engage in and embrace this market paradigm shift.”Imagine an economy where businesses trade goods and services without it costing the earth – that reality is already here, but we need to be willing to

embrace it.


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