Supply chain disruption has been a key theme in the post-pandemic world, exacerbated by war in Ukraine and a range of other macroeconomic factors.

Industries across the global economy have been hit by labour shortfalls, changes in demand patterns and higher energy and commodity prices, leading to product delays, food shortages and other problems.

Investors have turned to alternative data to help understand the length and severity of the impact.

Data vendors provide information to a range of organisations including hedge funds trying to anticipate the impact of supply chain issues on their investments, to corporations running forecasts within their industry.

The field is growing – a search on Neudata’s powerful data scouting platform reveals more than 200 individual datasets focused on the “supply chain” theme.

This article rounds up the key types and use cases of supply chain data, and explains how Neudata can help in this closely watched area.

• What is supply chain data?
• Types of supply chain data
• Case studies on using this data
• How can Neudata help?

What is supply chain data?

A lot of alternative data on supply chain issues relates to commodities and logistics.

It can help answer questions including: how available are different materials and how efficiently can the logistics industry transport them?

Global shipping is a huge 24/7 industry in itself, transporting goods, raw materials and parts to manufacturers and consumers around the world.

Remember the disruption when the Ever Given container ship blocked the Suez Canal in March 2021? That was a classic example of supply chain risk that caused huge downstream effects globally.

Upstream and downstream

But supply chain data is more complex than merely logistics. An important concept to understand in this area is the distinction between upstream and downstream risks.

The further “upstream” you go in a supply chain, the nearer you are to source, i.e. the start of the manufacturing process.

In electric vehicle manufacturing, for instance, this could mean data about a specific mine in Chile which a manufacturer has contracted to provide the lithium for its rechargeable batteries.

Manufacturing, production and transportation datasets can help to assess upstream risks.

The further downstream you go, the nearer you are to the end user or consumer. So POS (point-of-sale) or purchase intent data can help assess downstream demand.

Rather than providing insight on a particular point in the supply chain, some datasets provide context on over-arching and relevant themes, like geopolitics or cyber security.

Aggregated datasets such as ESG ratings and revenue dependency mapping tools, or even ethical supply chain tracking datasets, can offer even more insight.

Types of supply chain data

As we have seen, the supply chain data landscape is rich, varied and complex in nature.

Let’s explore in more detail the different types of datasets which can help buyers better understand supply chain risks.

Raw materials production

Some datasets listed in Neudata’s catalogue record the production of commodities and other raw materials, while others focus on specific areas like oil and gas.

These are often siloed by industry, meaning multiple datasets may be required to track multiple components.

Movement of goods

Many data providers listed on the Neudata platform focus on movement of goods, commonly referred to as trade flows. This data can help users understand potential pinch points or bottlenecks as goods are moved around the world by air and sea.

As the Ever Given case showed, just one hold-up can have a huge global impact.

Geopolitics

Geopolitics can have a massive effect on supply chains. Russia’s invasion of Ukraine last year, for instance, unleashed huge turbulence across a range of industries.

Tension between the US and China, meanwhile, has manifested in the “chip wars” and America’s decision to limit exports of advanced semiconductors and semiconductor manufacturing equipment to China.

Some providers can add useful context here, through political risk indices or granular polling data, giving a sense of where political sentiment is heading in different countries.

ESG ratings and research

Supply chains have become a huge issue in the world of ESG – environmental, social and governance – investing.

ESG helps account for risks not always traditionally quantified, such as natural or human capital – which are now popular dataset types across Neudata’s client base. These risks permeate through supply chains and have financial, legal and reputational risks.

As we explore in the third case study listed below, ESG is a huge and growing area of acute relevance in the context of supply chain data. Providers offer ratings and research which aids transparency, informing business and investment decision making.

Demand levels

To fully understand supply chains, you need to understand market demand. Early indicators such as B2B intent data can help to predict which products may see increasing demand, and which suppliers will have to meet it.

Some suppliers have started using POS data for this purpose, which means the data will help users understand how suppliers are informing their supply chain management.

One Neudata provider supplies POS data on the ‘Call of Duty’ videogame’s subscription service. It provides a guide not only to revenues, but where Activision may need to invest in the future, for instance on maintaining adequate online servers.

Other types

• Company interdependency mapping datasets use financial metrics to help assess supply chain risks – such as a lack of diversification – associated with a company’s clients and source of revenue. These datasets are often classified by region, product or theme.
• Cyber risk — Given how reliant companies are on computer systems and internet connectivity, understanding cyber risk throughout supply chains has never been more important. Third-party vendors can act as a modern trojan horse, with hackers infiltrating less protected suppliers to access the internal data of bigger companies.
• Permissioned blockchain structures are being explored by academics, consultants and companies to enhance supply chain oversight through a blockchain-powered tracking tool.

If you are interested in discovering more about any of the alternative data mentioned above, contact our team or explore our news and intelligence.

Case studies on using this data

Let’s go through three examples to see how alternative data can help users better understand supply chain complexity.

Case study one: Oil and gas

The vast oil and gas market is worth hundreds of billions of dollars to the global economy. Neudata has scouted scores of providers that help users understand the market, mainly by providing supply chain visibility.

Some vendors supply data on permits issued by governments to operators. By understanding where, and how often, operators are requesting permission to drill for oil or gas, investors can obtain a leading signal of more assets potentially coming to the market.

From production to supply, satellite data providers are foremost in supplying insights on this stage, measuring inventory levels of crude oil from miles above the earth’s surface.

Geolocation data can be used to track refining, while vessel tracking can be used for the transport stage.

These examples just show the variety of ways in which the alternative data providers listed on Neudata’s platform can provide insight into the oil and gas market.

Case study two: Electric vehicles

Monitoring supply chains is a crucial step in staying on top of sectors that are prone to shortages. A prime example is electric vehicle manufacturing, a huge growth market, in which the shortage of lithium for batteries has been a key problem.

A key question for investors that want to understand this market is which manufacturers have an established lithium-ion supplier to make their rechargeable electric batteries.

One provider on the Neudata platform reports on monthly battery deliveries by type and supplier to OEMs (original equipment manufacturers) in 50 markets. Another offers an EV battery capacity and battery metal tracker for over 80 countries.

Case study three: ESG

Natural language processing (NLP) tools can be used by vendors to provide new levels of insight on a company’s supply chain.

NLP algorithms scour company filings, press releases and external news sources to infer potential supply chain connections and ESG implications.

One supplier that Neudata has researched links apparel manufacturing facilities to the brands they supply, which could help when evaluating all three parts of ESG: from environmental factors (are the supply chain assets exposed to physical climate risk?) to social (insight into local labour laws and human rights abuses) to governance (is there over-reliance on a single supplier or entity?).

To gain access to Neudata’s independent research on any of the above data vendors, reach out to info@neudata.co.

What is Neudata?

Neudata is an alternative data-focused research platform that specialises in the objective and neutral assessment of data vendors and datasets. We help institutional investors, corporations and leading global organisations find the most relevant alternative data sources to use in their internal data ingestion processes.

Our platform is the global authoritative source for unbiased, independent alternative data intelligence.

We don’t buy or sell data, or require data providers to pay us a revenue-share or commission in exchange for recommending their products to data buyers. That means you get unbiased intelligence that’s tailored to your specific research goals and strategies.

Since 2016, we have helped our clients understand the landscape of available datasets, increasing the efficiency of their data spending budgets. Neudata’s data buyer clients represent 60-70% of industry-wide spending on alternative data.

If you are a data provider and want advice on monetising your supply chain data, or if you’re a potential buyer looking for an introduction to the alternative data landscape, contact info@neudata.co to discover how Neudata can help.