Data is the way forward, in order for cold chains to reduce unnecesssary waste, says Arindam Roy, Vice President, Client Partner at Straive.
Food and medical waste are crippling both our people and planet. A shocking 40% is believed to be lost along the value chain, and the consequences for the environment and communities are staggering.
The question, though, shouldn’t just be about how these goods get from A to Z. Other variables, like storage equipment conditions and temperature, make or break whether they’re actually usable once delivered. In fact, faulty temperatures were flagged as a culprit in estimated annual vaccine losses that total 50%. But these very factors are among the top challenges in cold-chain logistics, alongside poor documentation, human error, and compliance hiccups.
Technology and data science are at the crux of addressing these hurdles to ensure both safe and speedy delivery. Organizations in logistics need to rewire data methods and digital transformation strategies in order to optimize operational efficiency while boosting business outcomes. Here’s how.
Confront data reporting challenges
It’s no secret that logistics is reaching new levels of pressure. Geopolitical uncertainty, tariffs, climate change, and staff shortages are forcing providers to rewrite their operational strategies and make every mile count. To navigate this challenging landscape, providers are turning to technology, including AI.
However, while organizations have looked to digital solutions to drive better customer experiences and maximize efficiency, data reporting and management continue to be a weak spot. Integrating AI-powered automation is only one part of the digital puzzle. Reporting on collated data and translating it into actionable insights is another element altogether.
Relying on outdated Management Information Systems (MIS), which generate descriptive, backward-focused reports and business insights, is creating burdens. In an industry where things drastically change from one moment to the next, organizations must move from a reactive to a proactive data reporting approach. Teams need real-time, deeper, predictive analytics that facilitate agility as part of the wider bid to strengthen customer relations, slash delivery timeframes, and optimize operations on the ground.
Build a comprehensive, future-forward roadmap
Many technology transformations fail to take into account how digital tools actually pan out in real-world applications. These blind spots create unforeseen issues down the line, such as data silos, unexpected costs, and poor interoperability. That’s why organizations must build a thorough blueprint that encompasses advisory considerations through to final implementation.
First, build a roadmap of high-impact initiatives to identify a clear pathway towards data maturity. At this stage, the focus is on strategic data science initiatives crucial to driving corporate strategy and business goals while aligning with operational needs and challenges.
Once the prioritized initiatives and challenges are mapped out, the next stage is experimentation. This is the point where various solutions are tested and measured, and qualified pilots are brought forward to the production stage. Piloted solutions should meet certain performance criteria before deploying them, such as improved turnaround times.
Gain company-wide buy-in
Careful consideration should be given to ensuring seamless integration and nurturing adoption among business teams and other departments in everyday processes. Crucially, there are industry best practices in change management to fuel widespread adoption of data science solutions and other technologies. This revolves around practical know-how, so teams are well-versed in the business benefits of integrated solutions and familiar with working alongside them. Any business value generated from integrated data science solutions should be tracked and quantified using an ROI framework.
Moreover, digital transformation of any kind is not a one-and-done event. Future-forward strategies must be constantly monitored to ensure continuous improvement and tangible growth. Organizations committed to long-term digital transformation outcomes must establish a governance committee with institutionalized processes. This committee is supplemented by robust data engineering and technology expertise.
Importantly, strong deployment strategies feature a phase-wise approach to continuously identify an ongoing set of data science initiatives to propel organization-wide impact. This ensures continuous growth and the momentum of digitization as an ROI driver.
Maximize operational outcomes
Providers need more agile and proactive digitally-powered operations that keep pace with shifting supply and demand gaps. As mentioned, it’s important for organizations to ground their digital tools in real-world scenarios as part of maximizing outcomes, but many fail to do so.
For example, simulating warehouse operations enables teams to forecast potential demand spikes with a higher degree of accuracy. It’s also an excellent way to gauge any gaps in how data management systems and integrated tools collate and generate actionable insights. The predictive capabilities of digitally powered data management empower teams to tackle core challenges such as wasted miles, missed deliveries, customer complaints, insufficient supply or inventory.
Moreover, predictive insights can flag any potential additional burdens, such as possible detention charges, which can reach hundreds of thousands of dollars per year. This alone takes a significant weight off operational costs and empowers logistics organizations to be more resilient.
Data science transformation does not happen overnight, but following these steps creates a progressive and comprehensive strategy that keeps logistics one step ahead and moving forward.
