Assurety Consulting champions data, analytics, AI, and automation as the main drivers of change and organizational improvement in the postal and parcel industry. In our work with commercial mailers, delivery organizations, and posts in developing nations, we encounter a common refrain: our clients often believe their organizations are too small to take advantage of big data systems or are not yet ready and need to focus on operations first.
We’re here to make the case that parcel, post, and logistics businesses of any size can put data and analytics to work for them and that well-governed data capture and measurement are crucial elements of digital transformation. From logistics to customer experience, a strong analytics program grows revenues, cuts costs, and reduces costly errors and inefficiencies.
We’d also like to point out that most public organizations are hierarchical, requiring the blessings of HR and legal and validation for everything they do against rules and regulations, which in itself is a hurdle to innovation. If the PMG or CEO can create a separate department whose job is to lead and innovate without hierarchical hurdles, and where the goal is to drive innovation through data, information, and governance, those organizations can compete within the ever-changing marketplace.
These organizations can deal with regulatory and legal issues once they have enough experience with pilot development. Lawyers can always be told to find a way out.
Myth: It’s too difficult to integrate and share data across business units.
Breaking down organizational silos is more of a cultural problem than a technical one, and it can be overcome. To develop a vision for big data, the first task is to identify each point in the supply chain at which data is being or can be captured.
From acceptance and induction to sorting, transportation, and delivery, and through to customer management, data is being generated and used in an intra-department approach. Customer data is captured at the physical retail sites and through B2B systems for commercial transactions.
Address-driven analytics where everything revolves around address coding and barcoding is the most successful strategies. Leadership must recognize the relationships between all these data and understand how all the datasets can work together, benefiting other departments and the organization as a whole. Alongside data capture, processes should be evaluated for opportunities for automation and continuous refinement through AI and machine learning.
After cataloging all sources of data, the next step is formulating a plan of governance, which sets the standards for data collection and management all job roles must abide by. Once everyone is on the same page about the true potential of the complete set of organizational data, everyone can move forward with the implementation of systems and procedures to communicate and share data organization-wide.
Myth: We need an enterprise-scale team to implement big data and automation.
The reality is that even a small data-focused business unit can make major contributions across the organization. Thanks to increasingly powerful software solutions that can be implemented out of the box and customized to suit, it doesn’t take a hundred-man IT department to build and maintain the systems to capture customer and performance data and leverage them into results.
Most of the cloud platforms like AWS, Azure, and Google provide built-in analytical and AI technologies that don’t require the purchase of additional software. Its part of your package and the scalability and processing power is better than you will ever experience in your own server environments.
Myth: There’s no way we can catch up.
Our client executives and consultants frequently encounter paralysis by analysis. Organizations are rooted in the old ways of doing things, and the notion that they’re more than a decade behind on technology adoption can be daunting. The reality is that everyone has to start somewhere. The simpler transformative tasks of replacing paper workflows with computer-based systems serve as a primer to get everyone on board with the power of technology, and the efficiencies gained from these exercises illustrate the true power of sophisticated approaches to data.
As with any project or task, the hardest part is getting started—when an organization commits to crawling, walking, and then running, momentum enables full digital transformation and the path forward becomes clear.
Even the smallest delivery and express businesses and posts have a lot to gain from big data analytics.
In addition to the concerns above, there is a wide range of barriers to technology adoption at smaller organizations and those in developing nations. Suitability of software and systems, less-developed technology skillsets, and cost (chief among these) are hurdles that must be overcome. Assurety Consulting has built its business around helping the largest players and then using that expertise and experience to help smaller and medium players surmount these challenges and come to the table with the tech-driven leaders in logistics and delivery.
