Today we continue our long series and look at teaching shippers how to remain strategic when it comes to logistics and their transportation management program. This is the 7th post in this series that first started by giving an overview of the 10 areas shippers can turn to stay strategic. We then are going in depth on each of the 10 listed. The first 5 were as follows:
- 6 Strategic and Proactive Tips to Stay Ahead of Distressed Shipments
- Strategic Logistics: 8 Steps to Controlling Inventory Flow & Driving Warehouse Efficiency
- 5 Areas of Technology Application to Aid in Logistics Visibility and Communication for en Route Disruptions
- 6 Benefits of Applying Useable Data in Logistics For Continuous Improvement
- Strategic Logistics Management: The Importance of Flexibility
In this post we will talk about how you can improve your transportation management program by focusing on using data, extracted from the use of technology, such as a transportation management system, that then people can use to continuously improve.
The Use of Data to Create a Successful Transportation Management Program
Business practices have become synonymous with the application and collection of data for continuous improvement. However, most of today’s data goes unused and represents a lost opportunity to the company. Unfortunately, data lacks value if not properly cleansed, transformed, and applied. Furthermore, some data may be of minimal use without comparison to and identification of trends and collaborations between data from other transactions within a given warehouse or transportation system.
To survive in an increasingly complex, data-driven world, businesses must be ready to implement new “Big Data” solutions to ensure all data is aggregated, analyzed, and stored appropriately. Businesses are often left with questions about the origination of such data. Take a look at how a business can use technology, metrics, and services to improve transportation processes, which range from supplier shipping to route optimization.
Origination of Data
Data originates from one of two primary sources: individual sensors gathering data or manual entry of data. Both entries of data represent critical opportunities for errors, especially when considering the human element of manual data entry. In automated data gathering, which is driven by the Internet of Things through radio frequency identification chips or other automated data capture, data is aggregated from thousands of sources. However, this data is nothing more than a conglomeration of information without a purpose. It’s up to the use of metrics and algorithms to begin the process of breaking down the data for use in being able to improve your transportation management program.
Manual entry of data carries an inherent risk of incorrect data transcription. Unfortunately, the error may not be the result of the technician. If the data was passed along incorrectly, or if data did not have any value in the company, the data entry becomes useless. As a result, uncleansed, worthless data begins to take space within the system, which could negatively impact the overall operation. For any instances of manual data entry, the data should be double-checked for accuracy and importance. In today’s data-driven transportation systems, data entry should primarily be an automated process. However, exceptions to the rule of automated data entry will always exist. This is where metrics becomes necessary for the analysis and monitoring of data.
In a sense, parameters may reflect the individual data capture points. However, metrics may also be applied to the algorithmic analysis of data to identify what data needs to be removed, clarified, or analyzed in further detail. Additionally, metrics reflects decision capability by real-time data information.
An Example of Data Collection and Metrics in Logistics and Transportation Management Program Processes
For example, incoming deliveries may be delayed due to inclement weather. Data originates from within the truck’s GPS to relay information about the delay to the corresponding destination. The warehouse or distribution center may then alter services to reflect the change in loading or unloading schedules. This data is then stored in a specified location to begin the analytics’ process. Afterward, the data may reveal problems at the originating distribution center, which caused a minimal delay. Unfortunately, this minimal delay resulted in the truck’s encounter with the inclement weather. As a result, the destination center may implement corrective actions at the originating center to prevent this issue from occurring in the future. Ultimately, the entire process becomes more efficient with the collection, analysis, and actionable qualities of data.
In this example, a company may feel the aspect of metrics were left out. However, metrics is the information given to the company by data analysis. In the example, the metric showed a consistent delay at the originating center, which was corrected. However, metrics may be used as key performance indicators to monitor a given location’s activities. For example, the originating center may have order automated tracking and processing sensors in place to immediately detect a delay.
Data Management and Transportation Services
Regardless of technology implementation, the human element of improving your transportation management program must exist. Customers may want to speak with customer service representatives, and some orders will come in person. However, this does not mean automated processes, metrics, and real-time data collection, analysis, and use in decisions will be eliminated for these transactions.
Today’s transportation providers must understand the value in big data analytics’ services. These services act as the initial point of improving efficiency across the entire transportation network. Although we mentioned using historical data in making some decisions, real-time feedback enables decisions to be made by current actions. Essentially, this eliminates the “what if this happens like it did in…” hypotheses in transportation.
When an organization or transportation company uses these services, the organization will see an increase in profitability through reduced overhead costs and increases in the efficiency of its workforce. As a result, more customers will be pleased with the organization’s services, which will further drive demand for the transport company’s service. This becomes a self-fulfilling circle of order fulfillment at lower prices.
The world of transportation management is constantly evolving, and today’s transportation management systems must adapt to the chaotic events of daily life and business practices. By using data efficiently and accurately, a company can grow beyond expectations while providing superior service at more competitive rates. Through the use of metrics, people, and services, today’s shipper will reap the rewards of an investment into technology and “Big Data” analytics to continually improve their transportation management program.