In today’s challenging business environment, life sciences finance chiefs are increasingly focused on operational savings to fund research & development and deals. How might leveraging global trade data and analytics help the finance function realize incremental savings opportunities?
Rick Fonte, EY’s global health sciences and wellness tax leader, shares best practices for CFOs and their teams.
How can data analytics help overcome supply chain challenges?
For many life sciences companies, poor supply chain visibility is preventing them from accurately visualizing and analyzing their import and export transactions. This is often a result of a lack of access to accurate and comprehensive trade data. Consequently, many companies perform analyses and make decisions that reflect assumptions rather than reality. This discrepancy can lead to missed opportunities to save costs and cash taxes.
Full supply chain visibility and data-driven insights are critical for accurately informing companies on how to drive cost savings. Life sciences companies are now exploring more effective ways to leverage external data and trade analytics tools to unlock these opportunities.
With electronic customs declarations now required in almost every country, access to a rich and unique data set is no longer an issue. In addition, significant advancements in data analytics means that analytical tools are now available to process and analyze significant data volumes quickly and efficiently.
Even one year of external trade data represents an unmanageable volume using traditional means. Given the amount of data to be analyzed, analytic tools incorporating industry-specific analytical capabilities are critical for driving actionable insights.
Without access to accurate and comprehensive trade data, life sciences companies may be leaving significant opportunities on the table. Can you expand on this?
Companies typically do not miss out on opportunities due to a lack of effort or interest, but rather, usually because of a lack of access to the right data. All data is not created equal. Each time goods cross a border, there is an export entry and a corresponding import entry that needs to be filed. Each filing is, in effect, a tax return submitted to the authorities. The data on these customs entries is voluminous and highly detailed.
To complete the entry, the company’s customs broker must combine several sources of data, including importer and exporter transactional data received from the company’s enterprise resource planning system, shipping data, source and destination data, among others. By combining these data elements, the customs broker creates a new and unique data set that now resides outside of the company’s ERP systems. It is this unique data that is submitted electronically to the customs authorities, but generally not to the company, unless requested.
Unfortunately, no other single source of data provides the information needed to derive meaningful insights. ERP data is insufficiently detailed, and the data attributes are not available in a single location. Other data sources, including data from shipping or logistics systems, are too narrowly focused. While companies typically ask their customs broker for the filed customs entries, the format and large volume of these entries make it challenging to self-audit the declared information and analyze the underlying data.
How can life sciences finance functions apply advanced trade analytics to inform and support a wide range of supply chain, customs and tax decisions and opportunities?
It’s a common misconception in life sciences that significant cost and cash tax savings are unlikely to be identified through an analysis of trade data. It’s true that finished pharmaceutical products are generally exempt from customs duties, and as such, life sciences companies may not benefit from as much low hanging fruit as might a consumer products company. That doesn’t mean significant opportunities don’t exist.
There are two crucial points to understand. First, finished pharmaceuticals are not always duty free. Moreover, active pharmaceutical ingredients, raw materials and excipients, and packaging materials used in R&D, clinical trials and manufacturing are often subject to duty.
Second, the focus is not solely on saving customs duties, but on using trade data to identify a range of potential cost-saving opportunities throughout the supply chain. It’s helpful to think of trade data as a means to an end—essentially tax data being used to realize non-tax benefits. Yes, life sciences companies are identifying incremental customs savings, but it’s the cost savings resulting from supply chain optimization that are often driving the most significant and long-term benefits.
For example, we’re seeing significant cost savings from improved product sourcing, supplier rationalization, customs broker performance and pricing, improved Incoterm compliance and more cost-effective decisions regarding product shipping methods. The benefits are there—companies just need the right data and the right tools for the job.
What are the top considerations for finance chiefs looking to use trade data and analytics to achieve cost and cash savings and improve supply chain performance?
Executive sponsorship is often a critical element to maximizing and sustaining the potential savings opportunities, especially in larger organizations. While significant opportunities exist for customs duties savings, often the most significant benefits are operational cost savings generated from optimizing the supply chain.
Using trade or “tax” data to identify cost savings opportunities in the supply chain can create a lack of clarity among internal stakeholders as to which function should be leading the initiative. Is it tax, customs or supply chain? Creating internal alignment between these functions and an effective governance model for identifying, assessing and pursuing available opportunities is key to success.
Consistently maximizing available savings and maintaining high levels of sustained supply chain efficiency require an effective cross-functional effort. Companies should be willing to challenge historic practices, including data sources and analytical tools.
Ask yourself these questions: Is the data you have been using the best and most accurate for providing insights to optimize decision-making? Are the analytics processes and tools adapted to your industry and business? Are findings presented in a clear and actionable format?