Supplier Risk Management, a Top Priority for Global Procurement Leaders
Supplier management, specifically supplier risk management, is now a top priority for procurement leaders around the globe, trailing only behind cost reduction according to The Hackett Group’s 2021 Key Issues Study.
By Martin Hubert
Freight Forwarders and Non-Vessel Operating Common Carriers (NVOCC’s) now more than ever need to quickly adapt to fast-changing market conditions and manage risk, evaluate spot options and find solutions to space shortages that have been a byproduct of the global pandemic. While Articificial Intelligence, Machine Learning and Neuro-linguistic Programming was not so common a few years ago, these technologies are here and available now!
Market leaders in this space have been able to make remarkable leaps in the freight technology space by providing solutions to its customers that has helped maintain their supply chain competitive advantage; to name a few:
AI has helped reduce time of quote by 50% and provided updated ETA information up to 70% earlier than previous notifications.
Dynamic routing has delivered up to 17% savings on transportation cost spent.
Everyone was caught off guard by disruptions caused by the pandemic and the Suez Canal blockage that had cascading effects into other markets such as semiconductors and pharmaceuticals.
These events sent ripple effects through global supply chains, extending across multiple countries, industries, and product lines, with manufacturing and pharmaceuticals hit particularly hard.
Given the extent of these aforementioned disruptions, a surge in container prices as well as increased demand, combined with less than ideal working conditions has resulted in placing the Supply Chain industry in the hot-seat. The White House has now stepped in, to review American supply chains, further pushing supply chains and supplier management into the spotlight.
Turning to technology such as Artificial Intelligence, Freight Forwarders and other stakeholders involved in the Supply Chain have found that forecasting and prediction tools provide greater market clarity reducing uncertainty for businesses. Predicting arrival times, rates, demurrages & detention times as well as assisting in contracts discussions given historical data from respective systems with increasing accuracy and thereby minimizing variability when planning ahead is the name of the game.
Welcome to Supply Chain AI
AI is becoming essential to innovative supply chain transformation. According to IBM’s recent report, “Forty-six percent of supply chain executives anticipate that AI/cognitive computing and cloud applications will be their greatest areas of investment in digital operations over the next three years.”
Companies have been quickly reacting to the pitfalls that the 2020 pandemic has caused to their supply chain, all of which have expedited the desire to automate and better monitor shipments through the use of technology.
Implementation examples of AI in the supply chain can assist in more accurately determining important dates like ETA and ETDs from different data sources and evaluate, given the historic data for a given vessel, carrier or 3PL the likelihood the date is correct which in turn could trigger internal processes or increase visibility for these shipments. Having good empirical data allows stakeholders to have a higher level of discussion when addressing carriers or customers. The use of models will allow companies to find and exploit competitive edges that suit your business’s practices. For example, should a discount for a given customer be kept or removed if the NVOCC wishes to increase rates to a given trade lane. A model could be used to check if it makes financial sense to request the NVOCC to increase rates in low volume trade lanes as opposed to the large volume lanes and what would be the impact of such change to your budget.
It is fairly common in the logistics industry to find shipments with unexpected overhead costs such as waiting times and unforeseen fees. Unexpected fees are an inherent part of the supply chain as there are too many moving parts to accurately and cost effectively predict what happens at a terminal when picking a container. Nonetheless, AI and Machine Learning can utilize the historical data in your system to assist in predicting waiting times and overhead costs, reducing surprises at the end of the month or even assisting trucking companies to direct their trucks to a given terminal. For example, if your model shows, that the terminal you use have high congestion every 3rd week of the month, your business can re-direct their capacity to other terminals – as opposed to going to a congested port.
The adoption of AI / BI technologies provides valuable insights that will raise the level of discussion internally driving businesses to have a better understanding of their operations. This in turn leads to more automation, seeking maximizing returns and reducing human input error where possible.
As the intersection of transportation and numerous governmental regulations — global supply chains have been burdened by enormous amounts of paperwork and bureaucracy making the adoption of new technologies harder.
A common misconception when companies are attempting to implement technologies such as AI and Machine Learning is that these implementations will resolve all issues their current operations have, expecting some sort of black-box solution that provides a remedy to problems without fully understanding the algorithms and thought processes these solutions bring to the table. It is important for companies who desire to make this leap, to fully grasp the added value they present and also the downfalls they might bring as well. Partnering with technology companies who have an understanding of the industry and can help understand where automation is best implemented in addition to the added value these models & predictions bring to the table when discussing business strategies.
IoT Trackers & Real-time cargo monitoring:
One example is IoT Trackers. IoT trackers can provide greater cargo visibility through all stages of transportation. The ability to record data for your specific containers will allow you to compile not only information about your cargo, but also registers what vessel and carrier being used.
Given enough volume and data, the utilization of this stored information can then be processed and bring insights regarding matters such as: does the use of a certain carrier for a trade lane bring your company better returns? Can we better predict the ETA for shipments? Get automated alerts and triggers in your company’s system given a predetermined geo-fence.
The deployment of such solutions generally consists of two basic components, the use and installation and monitoring infra-structure of IoT trackers, followed by the storage and analytics platform which is where models and indicators are developed to suit customer requirements.
The combination of greater visibility and development of bespoke tuned models that take into account stakeholders’ workflow are crucial for meaningful results. Having those involved understand that AI-based solutions are not all about programming and code, but mapping and designing decisions based on workflows is key.
Cloud connected IoT Trackers are particularly useful for first mile & last mile — but also in trans-shipment scenarios as they require cell coverage for timely information reporting. When shipping perishable or temperature controlled cargo monitoring will be paramount. Feeding early warnings into your supply chain analytics will allow for corrective actions before the cargo hits your dock. Increasing capabilities of these devices are making automated AI based reorder processes reality.
Dynamic Global Routing and Execution
Everyone in Logistics and Supply Chain is affected by the convergence of higher than expected demand with the disruptions from the 2020 pandemic all of which culminated to record high shipping rates, severely constrained capacity in all modes and the industry trying to react accordingly. The calls for more Supply Chain resiliency are louder than ever, therefore the ability to evaluate routing and your company’s trade lanes strengths and weaknesses become fundamental in bolstering such resilience. Allowing for a mesh-up of Total Transportation Costs when evaluating sourcing alternatives in light of transit time forecasts, available capacity and transportation costs in a consistent cloud supported fashion will greatly enhance an importer’s ability to deliver on customer promises, avoid lost sales and improve margins.
Reducing costs of shipping as well as expediting the entire process through route optimization with the use of AI. An example could be having predetermined requirements for a given shipment where AI can evaluate all these variables in seconds and determine the optimized route that meets those demands. As these types of optimizations become more available, other products and awareness of their impact become a common place. Having the shipping industry work on their environmental impact is gaining more importance by the day.
The current global awareness of climate change has resulted in customers being more conscious about their carbon footprint, for many it is a marketing strategy to attract this type of demographic, others are being forced by investment funds and stakeholders who have climate change as a drive. AI powered solutions can help you compare routing options across modes as well as leg combinations with ease using a single API call or interactive user query in the cloud platform. By capturing CO emissions per leg and if possible reloading events, one can begin to measure their carbon-offset, demonstrating the commitment to the cause to regulators and stakeholders.
Uses of Natural Language Processing (NLP)
A common issue in the Supply Chain and many businesses is how communication is done. The shipping industry is notorious for the vast amount of emails exchanged on a daily basis requesting updates, document deadlines, quotes amongst others. Natural language processing is a field in AI and Data Science which addresses processing and understanding text or voice data to respond accordingly, mimicking a human interaction with the machine.
The use of NLP could be applied to a company’s workflow to address for example the large influx of emails by detecting requesting quotes, an update about a Master Bill of Lading or requesting a document. The ability to determine correctly what was requested and then trigger the response accordingly allows staff to focus on other matters, such as how to streamline the communication through other tools and developing better workflows, as opposed to wasting time responding to email requests that could have been automated.
NLP has also been a big support to service desks and customer relationship management tools with automated web-chats that assist on redirecting questions to the correct departments expediting the experience and response to customers requests.
Strategic RFP Process
A least obvious application for AI is the ability to model Carbon Emissions over different transportation options. As Carbon Emissions and sustainability are regaining momentum for global supply chain networks and becoming a sales differentiator for many — collecting relevant information during an upcoming RFP is a great starting point. Being able to collect and model based on CO2 impact has become important both on the buying and selling side.
As companies are either forced by legislation to meet CO2 emissions or truly adopt the ethos of going green, the need to measure and report on their impact becomes an important aspect of RFP processes as well as the difference between securing or not the RFP. Customers are more aware of their environmental impact which in turn drives companies to choose to have “greener” solutions even if at a slightly higher price, as those products usually can charge a premium for being green.
As the freight industry evolves, innovative companies all around the world are coming up with creative ways to improve the impact on the environment. Re-engineering the way we transport goods internationally and domestically to advance the health, safety and our future.
The entire Supply Chain industry revolves around understanding demands, be it from customers or carriers. As opposed to being reactive and only responding to the market’s demand, companies have more and more relied on large data to predict and therefore become proactive as opposed to reactive.
Understanding what has changed that has allowed the large adoption of AI has to do with how cheap storage has become as well as the processing power of modern computers. The statistical methods used in machine learning have migrated from pure academia to real world applications thankfully to open-source tools and easy integration with cloud solutions.
It is believed that to be able to implement AI solutions, large amounts of data is required. To some extent that is correct, the amount of data a carrier stores about their operation is vastly larger than what is expected from a small freight forwarding company. Predicting demand for larger data sets can be easier but statistical methods like bootstrapping and bagging can be used on small data sets to replicate and mimic the data set to generate more data points that follow the general trend of the original data set. All of which makes the use of AI and Data Science more accessible to smaller and mid-size companies alike.
The ability to predict demand and demand forecasting allows companies to maximise their efficiency for different departments, allowing management to make better business decisions based on empirical data. We foresee that shippers that are able to accurately predict their shipping volumes will be able to get better space commitments and rates from carriers as predictable, plannable capacity utilization also helps carriers maximize asset utilization.
The Supply Chain industry is at the cusp of a great transformation, the leverage and convergence of technology to support and inform the industry are here to stay and the companies that adopt these practices the best are the ones that will survive, simply due to their competitive advantage.
AI helps forecasting demand, expediting the communication through automation, provides optimized routes to customers, models CO2 consumption for greener decision making among many other solutions.
Finding the correct partner with experience and ability to understand your company’s requirements becomes paramount to the successful use of AI to leverage your business into the Industry 4.0.
ABOUT THE AUTHOR
Martin Hubert was born in Germany, He sold his first program at age 16, then went on and studied computer science. While at the university, He started a software company selling custom software solutions. After graduating with a Master and MBA he did a short engagement in New York, NY at a freight company whose president afterward offered me a job as Director IT. In 1994, his independent Internet quest began by founding a company that focused on providing track and trace capability over the internet through establishing EDI capabilities with carriers. We spun-off Freightgate in 2000 with a vision to provide a focused platform that improves the life for logistics companies and shippers.
Driven by a passion for innovation and never-ending improvements. Leading a team that strives to provide innovative logistics solutions, fostering global collaboration and productivity, while building a highly configurable platform with a global-by-design philosophy. Martin will continue to deploy technology to find smarter ways to solve today’s and tomorrow’s supply chain challenges.