About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

Unlocking the Value of Supply Chain Data

Subscribe to our newsletter

By: Gregory Van Droogenbroeck, head of enterprise reference data; Mark Schwartz, global data product manager for supply chain; and Tom Lagerman, supply chain data analyst, at Bloomberg.

A growing number of investors are exploring supply chains’ potential for fresh insights and actionable information on companies, industries, sectors and asset classes. They’re betting the data on companies’ customers and suppliers hold valuable market intelligence they can leverage to enhance their investment strategies.

Supply chain data is a relatively new branch on the alternative data tree, and its growth over the past several years has uncovered novel investment opportunities. Investors are harnessing the data to develop strategies that vary in sophistication. These can range from making bets based on companies’ interconnectedness to finding momentum plays, where a bundle of customers acts as a leading indicator for a particular supplier.

Sophisticated investors are combining supply chain data with traditional data sets, such as fundamentals and pricing, to paint a fuller picture of a company, its customers and its suppliers. Over the past decade, the proliferation of new data sets – and advances in the technology to gather and process them – has prompted investors to explore innovative ways to channel the data revolution’s full potential.

In 2011, procurement teams at large corporations made supply chain data public after the Fukushima Daiichi nuclear disaster. The incident exposed the limits of corporations’ knowledge about their supply chains beyond their first-tier suppliers. Today, data providers can show investors the second, third and fourth tiers of a corporation’s relationships, providing an unprecedented transparency that fuels more educated investment decisions.

Uncovering supply chains’ potential

Investors are using supply chain data to add value to traditional and other alternative data sets. Supply chain data express the relationships between a company’s customers and suppliers as a percent of revenue or spend, helping investors:

  • segment company’s financials by its supply chain
  • understand the different revenue-generating lines of business that each company has
  • quantify the supplier’s customer-generated revenue
  • determine what the customer spends on that supplier’s goods or services
  • categorize the type of spend or expenditure.

From there, investors can analyze companies’ interdependence along the supply chain, yielding valuable insights and buying opportunities. Take Intel as an example: data show that Dell, an Intel customer, represents 16% of the semiconductor chip maker’s revenue. As Dell outperforms, it may require additional goods or materials from its suppliers, which can boost Intel’s performance and value. In addition, investors can find momentum strategy opportunities by using a basket of Intel’s customers, including Dell, as a leading indicator of Intel’s performance.

Investors can also combine Bloomberg’s supply chain data with other quantitative data sets through common symbology to understand the contagion effect on a particular supply chain. Leveraging these sets helps them identify how political events can trigger risks within supply chains.

In one example, Japan in July implemented an export ban on certain chemicals used for processing semiconductors, a move that targeted South Korea’s large chip companies, such as Samsung. The supply chain data can show investors potential alternative chemical suppliers to the chip companies, what chemical suppliers are based in Japan and which are based in other countries that could take up the supply. The data can also reveal which Japanese suppliers are cut off from key customers.

From here, investors can use the data to determine whether there’s a buying opportunity for these alternative chemical suppliers – as Samsung still must buy product. They can also determine whether there’s a short-selling opportunity or an opening to exit positions on those Japanese suppliers to Samsung as they lose business.

Exposing industry risk and using raw materials inputs

Supply chain data can also highlight the risks within certain exposures to an industry. Investors in the iShares PHLX Semiconductor ETF (SOXX) can see how the entire semiconductor industry runs through TSMC, a semiconductor manufacturer. Examining SOXX* through the lens of TSMC’s supply chain reveals that 4.5% of the ETF’s weight is TSMC itself; 20.4% of the ETF’s weight is TSMC suppliers; and 61.6% of the weight is TSMC customers.

Here, the full supply chain picture shows that SOXX holds TSMC, as well as its customers: the companies that design the chips and depend on TSMC for manufacturing them. And by extension, the ETF holds the companies that make the equipment TSMC buys to then assemble those chips.

In all, while the data show that TSMC comprises just 4.5% of the fund, investor exposure to the company is far larger than what the weight implies. Using these findings, investors can consider the degree to which TSMC’s performance affects that of SOXX and determine whether to buy or short the ETF.

There are applications in commodities, as well. Using supply chain data, investors can link companies’ two-way specific inputs, the raw materials they use in their products: steel, aluminum, wood etc. By adding these critical inputs into its database, Bloomberg lets investors combine these new data sets with supply chain data to generate returns.

Moreover, commodities investors can segment companies that use steel as an input and monitor their performance and stock price. More fundamentally, they can also track how costs change quarter-over-quarter or year-over-year.

Extending utility into ESG

Looking ahead, the move toward sustainability-related investing practices will likely drive demand for supply chain data as investors look to manage ESG risk across the supply chain.

Many investors want to better understand the impact of climate change on their portfolios, which can manifest itself on the fund directly, as well as through the supply chain. In addition, these data sets can help measure a company’s carbon footprint by calculating the emissions related to their supply chain. Combining supply chain and ESG sets shows the greenhouse gas and emissions data of a company’s upstream suppliers and downstream customers, revealing which businesses have supply chains with more environmentally friendly practices.

As alternative data offer ever-deeper insights into corporate processes and workflows, sophisticated investors continue to mine these new data sets to help them measure the impact of signals up and down companies’ supply chains. These data yield a wealth of information on companies’ customers and suppliers, opening wide avenues of investment possibilities and greatly enhancing risk management.

*Data as of November 18, 2019

Subscribe to our newsletter

Related content

WEBINAR

Recorded Webinar: Strategies and solutions for unlocking value from unstructured data

Unstructured data accounts for a growing proportion of the information that capital markets participants are using in their day-to-day operations. Technology – especially generative artificial intelligence (GenAI) – is enabling organisations to prise crucial insights from sources – such as social media posts, news articles and sustainability and company reports – that were all but...

BLOG

The Data Year Ahead: More Data Formats and Use Cases

In the second part of our preview of the next 12 months in data management, we take in the views of experts who offered Data Management Insight their thoughts on a range of developments, including the increased use of unstructured data, the wider application of data sets and distribution challenges. 1 Data Governance, Quality and Technologies Ian...

EVENT

AI in Capital Markets Summit New York

The AI in Capital Markets Summit will explore current and emerging trends in AI, the potential of Generative AI and LLMs and how AI can be applied for efficiencies and business value across a number of use cases, in the front and back office of financial institutions. The agenda will explore the risks and challenges of adopting AI and the foundational technologies and data management capabilities that underpin successful deployment.

GUIDE

ESG Data Handbook 2022

The ESG landscape is changing faster than anyone could have imagined even five years ago. With tens of trillions of dollars expected to have been committed to sustainable assets by the end of the decade, it’s never been more important for financial institutions of all sizes to stay abreast of changes in the ESG data...