Now that we covered the major areas in Data Science that can be applied to Logistics & Freight, lets now dig into the top 5 areas above techniques can be applied.
Top 5 areas Data Science techniques can be applied in Freight Logistics
1. Dynamic Routing: Time Series Forecasting and Process Mining
Picture Courtesy of blogs.bing.com[3]
Until the advent of Managed Services like Azure Event Hub/Kafka/AWS Kinesis in the last 12-18 months, it took quite a bit of up front Capital and Engineering expenditure to setup the appropriate infrastructure to process such large volumes of telematic data. However, the ability of enterprises to bring up such services on demand is enabling responding to environmental and accidental disruptions more affordable now than ever.
As mentioned earlier, Process Mining can be extremely effective in quantifying specific bottlenecks based on historic data. In addition to these techniques, Truck Routing APIs available from services like Azure and open sourcing of Combinatorial Optimization tools like Google-OR are bringing the most cutting edge technologies to the masses.
All the above aspects need to work synchronously to make Dynamic Routing viable.
- IoT and Predictive Maintenance
IoT and telematic data is nothing new, but placing multiple sensors on a vehicle system, effectively and securely managing them has been a challenge until the advent and general availability of mesh networking technologies like Thread [4] and Particle.io.
As mentioned earlier, even ingesting and processing such large volume of data in near real time has been a challenge before the advent of Managed services from Cloud providers.
Note that using IoT data and Time/Frequency analysis, we can predict that a disruptive event is about to happen. It is when this event data can be processed and fed into the dynamic routing (or maintenance) algorithms that the real value of Predictive Maintenance can be achieved.
- AI Driven document Processing as a Service
Picture (modified) Courtesy of receipts-templates.com
Sure, you can write an app for everything and give an iPhone to each of your staff and only EDI [5] data is exchanged across systems. But I’m reminded of the story of how NASA spent $3 Billion on a pen that writes in zero gravity while the Russians used a pencil. We see many cases where paper just works and switching over to an app is not without heavy amount of time and capital investment. Even the requirements for what the app should actually look like is not entirely data driven, making the UI design challenging.
At DIVERGENCE.ai, we were able to use Deep Learning augmented Document Processing to automate data entry into back end ERP/Financial systems and we do so without disrupting existing processes. Often, the front end users barely notice any change in their processes. And as we process more documents, we get better understanding of what kind of mobile app can best serve each use case. It has been a painful and memorable lesson for us that getting drawn by the sexiness of AI and automating without profiling is a fast way to burn cash 🙁
- Optimizing Processes using Process Mining (Eg: On-Time-In-Full (OTIF), Order-To-Cash etc)
“Walmart has changed their vendor guidelines and scorecard parameters a few times in recent years. They went from requiring a four-day shipping window in 2016 to a two-day shipping window in 2017 and a no-day shipping window in 2018. They have since relaxed rules to allow carriers to deliver one day early (as of April 2018).” [6]
From the time Walmart started enforcing OTIF guidelines in 2016, it has been extremely challenging for suppliers and logistics companies to comply and very few companies have 90% compliance rates, which probably explains why Walmart had to relax the rules in 2018.
From the time you receive order for shipment to the time you deliver the goods and get paid for it is a fairly complex process within most organizations. While the Business Process experts within your company can point to what the intended process is via Process Flow diagrams, most organizations have a hard time pin pointing what the actual process looks like and where exactly the bottlenecks in the process are. Traditional techniques like Lean-Six-Sigma are valuable at narrowing down and root causing specific bottlenecks, but are unable to get overall understanding of underlying processes.
This is an area where Process Mining is most effective. As the time stamp events from various ERP systems get fed into Process Mining tools (Eg: Disco, Celonis) they reverse engineer and find out what the real underlying processes are and help find where the bottlenecks are as well as cases where the real process deviates from the intended process.
- Driver behavior prediction
In the US alone there are about 2.5 million accidents every year and 64 percent of those accidents are caused by distracted drivers. Forty-seven percent of drivers are comfortable either texting manually or using voice controls while driving according to a survey conducted by the National Safety Council (NSC). But in reality, texting is more dangerous than drunk driving.[7]
These are use cases where Deep Learning based Vision systems are now a viable solution. Note that until recently, Deep Learning on Edge devices has been an engineering challenge. With advent of fully managed AI services like customvision.ai, it has become practical to deploy/maintain and update deep learning machine visions systems on the edge using model containerization.
Note that vision systems are not the only methods to quantify driver behavior. Accelerometer sensors are also pretty good at detecting driver behavior as well, according to Brian Kursar at Toyota Connected [8].
Implications in Packaging and Warehouse management and beyond
Most of above techniques are just as applicable in Packaging and Warehouse management. Also note that most of individual techniques have been around for at least a decade and quite a few techniques even dating back to the World War II. However, most techniques were only available for a privileged few, and even then was an engineering and logistic marvel to connect disparate systems even a few years ago. Getting (near) real time results on Big Data has also been a relatively recent phenomenon.
With cloud adoption increasing rapidly and more services becoming “managed”, companies are now able to focus directly on the problems as opposed to the logistical nightmares of getting licenses for the tools and provisioning them. This also needs a radically different thinking workforce that can learn fast, fall in love with the problems (not tools) and just pick up the right tools and techniques when needed.
We hope you found this article helpful in connecting the various Data Science Techniques and would love to hear your thoughts.
About DIVERGENCE.ai
Divergence.AI is a Full service Management and AI consulting firm. We are based in Dallas, TX.
Our deep capabilities in strategy, process, analytics and technology help our clients improve their performance. We provide expert, objective advice to help solve complex business and technology challenges. We bring our knowledge and experience to develop and integrate AI-driven solutions within the customer’s business environments.
About the Author: Vish Puttagunta
As the CTO and Principal Data Scientists at DIVERGENCE.ai, Vish helps companies incubate Data Driven teams centered around Marketing, Operational Excellence, Fraud Detection and Food Safety.
As the Director of Data Science Programs at Divergence Academy, he teaches and continuously evolves the curriculum for Data Science on Big Data/Cloud based on feedback from various consulting engagements and market research.
References
[1] https://www.foodlogistics.com/cold-chain/article/12332506/logistics-gets-fresh
[2] https://www.fmcsa.dot.gov/regulations/hours-service/summary-hours-service-regulations
[3] https://blogs.bing.com/maps/2017-05/truck-routing-and-more-exciting-news-from-build-2017
[4] https://en.wikipedia.org/wiki/Thread_(network_protocol)
[5] https://en.wikipedia.org/wiki/Electronic_data_interchange
[6] https://ziplinelogistics.com/blog/walmart-on-time-in-full-otif-program/
[7] https://www.linkedin.com/pulse/financial-impact-distracted-driving-tony-summerville/
[8] https://towardsdatascience.com/data-science-at-toyota-connected-69bf50982b09
Additional References
Combinatorial Optimization Tools
https://www.ibm.com/analytics/cplex-optimizer
http://www.gurobi.com/products/gurobi-optimizer
https://developers.google.com/optimization/
Process Mining
http://www.promtools.org/doku.php
AI/ML/Statistics
https://azure.microsoft.com/en-us/free/machine-learning
Real Time “Big” Data Stores
https://azure.microsoft.com/en-us/free/cosmos-db
“Big” Data Stores for Generic processing
https://azure.microsoft.com/en-us/services/sql-data-warehouse/
Real Time “Big” Data Processing/Ingest
https://azure.microsoft.com/en-us/services/event-hubs/
https://azure.microsoft.com/en-us/services/hdinsight/apache-kafka/
https://aws.amazon.com/kinesis/
IoT Mesh Networking