Analyse to Succeed

Originally published by Logistics Business, September 2024

Mark Holmes, Senior Advisor for Supply Chain, InterSystems, looks at how to build the foundations for analytics success in the logistics and supply chain sectors

For logistics and supply chain organisations, analytics is a compelling proposition. Analytics, alongside powerful artificial intelligence, machine learning, and business intelligence tools, has huge potential to help businesses not just understand historical data, but also to predict future trends, automate decisionmaking processes, and obtain actionable insights.

In the logistics and supply chain sectors, where disruption is commonplace, these capabilities translate to many things, including the ability to predict disruption and opportunity in realtime, allowing businesses to re-route or resupply stock at the drop of a hat. These advanced capabilities which turn data into actionable insights can also help businesses to increase efficiency and accuracy, and adopt more sophisticated data-driven strategies to drive their enterprise forward.

However, it is often the case where analytics is concerned that businesses dive headfirst into tactics without having a clear strategy in place. This frequently results in limited success and a disappointing return on investment (ROI).

To leverage analytics effectively, logistics and supply chain business leaders should take a more strategic approach. The first step should be to develop a smart data strategy that will establish the foundation for future enterprise-wide analytics success.

Data is a vital requirement of any analytics initiative, and logistics and supply chain organisations have no shortage of data dispersed across their huge network of warehouses, carriers, and various suppliers. Therefore, a smart data strategy and the technology for acquiring clean, healthy, real-time data are essential.

A smart data strategy should capture three things: data collection, analysis, and integration into organisational operations. Technology like the smart data fabric offers supply chain and logistics businesses a clear path to achieving all three to bring their data strategy to life.

Built on modern data platform technology, the smart data fabric creates a connective tissue by accessing, transforming, and harmonising data from multiple sources, on demand. This enables supply chain and logistics organisations to leverage usable, trustworthy data to make faster, more accurate decisions.

Analytic capabilities, including data exploration, business intelligence, natural language processing, and machine learning, embedded within the fabric make it faster and easier for businesses to gain new insights and power intelligent predictive and prescriptive services and applications. Once these solid data foundations are in place, logistics and supply chain organisations can begin to identify the most appropriate use cases for analytics to unlock its real potential and augment human decision-making.

To identify the most impactful use cases and therefore where their analytics journey should begin, there are several key questions business leaders should ask. The first should be, where in the organisation would feel the most benefit from analytic capabilities and how would implementing analytics in this use case support your strategic business objectives? This should take into considerations things like future growth or other business drivers, such as sustainability requirements.

The smart data fabric’s self-service capabilities can be extremely helpful here, enabling every business user to interrogate live data to make timely and accurate data-driven decisions, and discern which use cases would benefit most from analytics. Armed with this knowledge, logistics and supply chain leaders will be able to map out the use cases and implement them strategically.

Taking this approach will help business leaders to understand which use cases are the most fundamental to achieving their business objectives. As a direct result of this, logistics and supply chain businesses should find their analytics applications yield better, more impactful results in both the long- and short-term.

This strategic approach will allow logistics and supply chain organisations to leverage analytics to make datadriven decisions. Adopting a smart data strategy, powered by a smart data fabric, that moves beyond manual processes to a seamless integration of robust data collection, analysis, and application is an essential component of this approach.

By arming logistics and supply chain firms with the actionable insights and end-to-end visibility that analytics can provide, they will be better able to see, understand, optimise, and act. Ultimately this will help them move towards a more agile and resilient supply chain model in which they are able to improve ontime in-full (OTIF) metrics, optimise sustainability decisions, for instance, by understanding which routes to take to increase fuel efficiency, and better manage constraints across the entire enterprise and global ecosystem.

Adopting a strategic mindset whereby they develop a smart data strategy and identify and prioritise individual use cases that align with strategic objectives is the key to logistics and supply chain leaders unlocking the full potential of analytics. Beyond improved ROI, access to on-demand analytics will enable organisations to adapt quickly to disruptions and anticipate future trends – both of which are vital for long-term success in today's dynamic logistics and supply chain environment.

Logistics and supply chain business leaders must, however, remember that the success of analytics hinges on the integrity of the data used. Inferior data quality directly translates to subpar analytics outcomes. Therefore, it is imperative for logistics and supply chain enterprises to invest in the smart data fabric technology needed to obtain clean, harmonised, real-time data. Ultimately, this commitment to data integrity is essential for organisations to extract the maximum value from analytics and obtain the actionable insights required for informed decision-making.