Splice recently announced a strategic partnership with Turvo, a leading supply chain collaboration platform and transportation management system (TMS). It is a great milestone for Splice, for sure, but more telling is how the partnership reflects the need for data interoperability and automation in supply chain and logistics.
Data is everywhere, but too often it is inaccessible or incompatible with the applications that need it. Human intervention is the common fix, but that is slow, expensive and drags down productivity. Big data gets too heavy and sinks your ship in the data lake.
Top Challenges to Data Automation in Supply Chain and Logistics
The challenge to align systems and data is not unique to supply chain, shipping and logistics. Putting together a tech stack that best supports your business is a universal issue, but our industry exhibits several key barriers that make data automation difficult.
Diverse Systems and Standards:
Logistics involves a multitude of stakeholders, including shipping lines, ports, customs, truckers, and freight forwarders. Each of these entities may use different systems and data formats.
Lack of standardized protocols and formats can make it difficult to integrate and automate data flow seamlessly across the entire logistics chain.
Legacy Systems:
Many organizations in the logistics industry still rely on legacy systems that were implemented years or even decades ago. These systems may not be designed to easily integrate with modern automation technologies.
The cost and complexity of upgrading or replacing these legacy systems can be a barrier to implementing automation.
Interoperability Challenges:
Achieving interoperability among the various software and hardware components in the logistics ecosystem is crucial. However, achieving seamless communication between different systems is often easier said than done.
Interoperability issues can arise from differences in data standards, communication protocols, and technology stacks.
Data Quality and Accuracy:
Automation relies heavily on accurate and high-quality data. Inconsistent or inaccurate data from any point in the logistics chain can lead to errors and disruptions.
Maintaining data quality requires not only automated processes but also effective data governance practices.
Human Involvement and Trust:
The logistics industry often involves complex decision-making processes that may require human judgment. Trust in automated systems needs to be built gradually, especially when manual intervention has been the norm. Stakeholders may be hesitant to fully rely on automated processes without human oversight.
Integrating the Logistics Ecosystem
The graphic describes the challenge. To integrate and automate a supply chain and logistics ecosystem, it has to be able to handle a collection of languages, standards, and protocols. Thousands of combinations are possible to connect to your trading partners and customers. It poses a mind boggling puzzle that an integration layer like Splice simplifies, and in doing so, adds capability and capacity to all the parties involved.
Overcoming these challenges has become increasingly important, because the acceleration of new technologies create wider gaps. The longer companies and organizations wait to harmonize systems and enable automation, the more problematic data automation can become. While data standards will help tackle some of the barriers, they need years to propagate.
In the meantime, translators like Splice will enable automation. The power of any application is multiplied when it works seamlessly with more systems and applications, and Splice expands the capacity of TMS and systems of record to create unique ecosystems by connecting, translating, and automating the movement of data.
Splice bridges the data and process gaps to strengthen ecosystems and simplify complex technical challenges. Our platform has over 100 pre-built connections to leading platforms like Turvo, CargoWise, Oracle Transportation Management (OTM), Tai, Trinium, and many transportation providers and data sources.