Stop guessing: How data analysis tools build predictable supply chains

Today, powerful data analysis tools are dismantling the guesswork that used to define supply chain management. By forging clarity from what was once an impenetrable mass of information, these applications have become integral to any company seeking stability and competitive advantage amidst the high-stakes challenges of modern global commerce.

However, successful implementation of these tools means first understanding how these can serve companies’ needs and the challenges involved in unlocking their return on investment (ROI), including devising key performance indicators to persuade decision-makers to act. 

As Rodolphe Bey, Group Information Systems Director, FM Logistic, says: “Key performance indicators are crucial for demonstrating to the board the impact of these technologies on processes and their ROI so we can continue to invest in them.”

Central to gauging ROI is understanding how modern data analysis applications can generate significant improvements across three broad areas in terms of operational efficiency, risk management and overall performance.

One application involves studying key metrics like historical sales figures, seasonality and market trends to forecast demand accurately. Companies can manage their inventory smartly to avoid lost sales while reducing the carrying costs of overstocking¹.

FM Logistic’s approach involves real-time analysis of inventory data to continuously gauge current consumption patterns. Rodolphe Bey says that by using this data to model and simulate sharp demand fluctuations (such as those caused by an emergency), the company can make informed decisions about optimal stock location within the facility, thereby improving their overall speed to market.

Supply chain data can be mined to make fleets more efficient by fixing poor routing plans to make the best use of underutilised capacity, with one estimate suggesting this could cut fuel consumption by a quarter². And, with poor maintenance routines and unplanned fleet downtime costing an estimated $50 billion annually across industries³, such tools are vital for predictive maintenance and maximising fleet use. 

Rodolphe Bey notes that FM Logistic crunches data to plan deliveries based on fleet availability and parameters like product type and quantity, the client, delivery frequency and distance. “This allows us to merge deliveries for clients in the same vehicle, which helps even small clients enjoy the low costs derived from economies of scale enjoyed by our large clients,” he says.

Analytical tools can also sift through performance metrics to rank suppliers by performance and flag those exhibiting financial instability. This helps reduce risks and fosters closer collaboration with key vendors. 

These tools also enhance the ability to plan and build capacity, which is vital to cope with supply chain disruptions. Estimates suggest companies proactively managing supply chain risk spend half of what non-proactive peers do in handling disruptions⁴.

The value of a data-driven supply chain is also reflected in returns, with a 2014 study finding the average ROI on analytics projects was $13.01 for every dollar spent⁵. Several factors drive such returns:

*Cost savings: They help lower inventory costs, reduce freight and fuel expenses, and enable better risk management. Performance data also helps users to negotiate better supplier contracts resulting in further savings. 

*Agility and resilience: Companies using data not only become leaner, they become more agile and better manage supply chain disruptions, which can potentially reduce sales by an estimated 7% and cost $1.5 million daily⁶. 

*Competitive advantage: Cumulatively, these tools deliver the most significant ROI of all: satisfied customers and a competitive advantage in a crowded marketplace.

Despite the benefits, crafting a data-driven supply chain is not without its challenges.

Many organisations’ information is incomplete, inconsistent or siloed in unconnected systems, while just half of all supply chain companies have data of sufficient quality⁷. Poor data, Gartner estimates, costs companies an average $12.9 million annually⁸.

“The real difficulty is to link the various legacy systems that tend to be silos and to create a technical and information bridge to unify all that data,” says Rodolphe Bey. “That is the key.”

Furthermore, companies must deal with a mix of structured data (including inventory and sales information in spreadsheets) and unstructured data (like emails and text documents). Here, says Rodolphe Bey, digitalisation and technologies like generative AI hold real promise with FM Logistic deploying AI to enhance overall productivity by automating various administrative tasks.

As the use of data grows and supply chains become more complex, both are more vulnerable to cybercrime⁹, with costly breaches¹⁰. Companies must therefore prioritise security and privacy to retain stakeholder trust and comply with data protection regulations¹¹. 

Then there are the upfront costs of investing in data analysis software, hardware and other infrastructure. Companies must also spend on building and maintaining effective analytical models, which requires specialists with data science skills and knowledge of supply chain principles. Because such personnel are hard-to-find and expensive¹², many firms would need to invest in training and upskilling existing teams¹³.That said, as the logistics industry increasingly leverages innovative solutions and data analysis, it is clear they offer transformative benefits. Additionally, today’s increasingly complex operating environment means data-driven insights are not an option; they are essential to survival and success. Firms that overcome the hurdles and embrace these data analysis tools will manage their supply chains more effectively and acquire a decisive edge over their competitors.

¹ https://datahubanalytics.com/inventory-management-optimizing-stock-levels-to-reduce-costs-and-prevent-stockouts/
² https://nextbillion.ai/blog/route-optimization-to-reduce-co2-emissions
³ https://www.deloitte.com/us/en/services/consulting/services/predictive-maintenance-and-the-smart-factory.html
⁴ https://www.deloitte.com/us/en/services/consulting/articles/risk-management-in-supply-chain.html
⁵ https://www.visionbi.nl/wp-content/uploads/o204-Analytics-pays-back-13-for-every-dollar-spent.pdf
⁶ https://procurementtactics.com/supply-chain-statistics/
⁷ https://www.mckinsey.com/capabilities/operations/our-insights/taking-the-pulse-of-shifting-supply-chains
⁸ https://www.gartner.com/en/data-analytics/topics/data-quality
⁹ https://www.weforum.org/stories/2025/01/5-risk-factors-supply-chain-interdependencies-cybersecurity/
¹⁰https://deepstrike.io/blog/cybercrime-statistics-2025
¹¹ https://iclg.com/practice-areas/data-protection-laws-and-regulations/01-the-rapid-evolution-of-data-protection-laws
¹² https://engineeringonline.ucr.edu/blog/data-scientist-shortage#
¹³ https://www.horizonrecruit.com/why-supply-chain-leaders-are-struggling-to-hire-data-and-ai-talent