The importance of visibility in the global supply chain is well known and remains a key priority for any logistics and transport company looking to analyze their costs.
The sector is moving fast, and advancements in AI and machine learning have enabled process automation to unlock new opportunities to streamline business operations, while continually developing competitive advantages.
Ocean freight forwarders are dealing with a lot right now and data offers them a new way to compete, optimizing processes, cutting costs and saving money. Many companies still require employees to note and enter data manually and accept their client’s estimations of the weight and volume of shipments, which inevitably leads to errors.
To move past this, forwarders will need to automate and harness the power of AI systems to streamline the process while cleaning up their existing data, making it much more valuable. This means investing in more technology, and in more data talent as well.
Why do forwarders need to consider their data strategy?
Hiring data analysts and developing in-house optimization tools simply won’t be possible for many logistics and transport companies. Time is not always a luxury that a busy forwarder has on their side, and neither do they have all the expertise required.
A quicker option would be for logistics companies to work with external providers who specialize in AI, machine learning and logistics, to get more done and see a quick ROI.
The amount of data generated within the global logistics industry is vast and although the digital transformation of the global supply chain is well underway, many forwarders have yet to capitalize on the opportunity. The sector is still very hands-on and often high-skilled employees are forced to spend too much of their time on these everyday processes, when they should spend their time focusing on more business critical activities such as customer engagement and new business generation.
Key data and the profits of freight forwarders
Big data is a big deal for freight forwarders, shippers and third-party logistics providers alike. The effective analysis and integration of data findings can yield enormous benefits, but only if those can be identified, captured effectively and then implemented as part of a business wide approach and adoption.
Data analysis differentiation and sustainable profitability are all crucial to building a successful data approach.
Data analytics should not be a side project but considered as part of a company-wide strategy where data-driven decision making is the norm. For logistics companies, capitalizing on their data is a crucial strategy for implementing an effective business model. However, many stop at data accumulation and don’t apply true data analytics to their strategy and fail to uncover new efficiency gains.
Data quality is very important and many companies are not always taking adequate steps to clean-up theirs. Although more focus has been placed on the importance of effective data management in recent years, many leading supply chain companies cannot enforce unified data standards when recording it, which leaves gaps and makes it very difficult to use or potentially share.
What’s the bottom line?
Logistics companies need to cooperate in building a data-driven process based around transparency, which would allow them to reach a single version of the truth and allow them to stay competitive.
Day-by-day, the volume of data generated by logistics companies through digital technologies is increasing. To get insightful information from those data points, companies need to adopt new strategies, improve their expertise, and implement more powerful tools. Data quality directly affects the bottom line for freight forwarders and they need to adopt a strategy that focuses on the extraction of data and structure to use it afterwards.
Why not try a live demo of the Expedock solution today and learn more about how cleaning up data can directly impact the bottom line.