We continue our series on strategic shipping by going into detail of the 10 main areas that shippers can turn to in order to have a strategic logistics and transportation management mindset. Today we will go into detail on using the available data created in the processing of shipments within transportation management and other related logistics management for continuous improvement.
Shipping processes revolve around a million-trucks-worth of data. Overused and misunderstood in blog posts and informative articles, “big data” describes the use of data to improve your operation, which includes shipping, warehousing, and supply chain processes. However, the concept of data gets blown out of proportion when its core functions can be broken down into simple, easy-to-implement procedures. Take a look at how your data can be used for continuous improvement across your organization.
Think about how your operation functions. Do all of your team members understand and fulfill their duties appropriately? Are pallets and shipments picked on time and without errors? Will an inaccurate picking result in one error for the original problem or two errors for sending an incorrect product and retaining the original, correct product in your inventory? Each of these situations represents an opportunity to use data in logistics to improve your processes through performance management.
Performance management is basically how an organization manages potential problems and maintains standards within the workforce. For example, drivers are expected to arrive on time, maintain docking schedules, and avoid dead time. In each of these situations, scorecards can be automatically filled out to reflect the problems for specific employees or groups of employees on a given dock. This information can then be used to determine disciplinary action or require additional in-service requirements for such employees.
Furthermore, such methodology can be applied to vendor relationships. If a vendor fails to deliver product as specified, the vendor may be advised of how future violations will affect contractual obligations. However, you must exercise caution to make sure you do not damage your vendor-shipper relationship. If a vendor consistently appears to run behind schedule, you could change your delivery schedule to reflect the times when the vendor is most likely to arrive as determined by previous action.
Using data in logistics to improve your processes does not necessarily require existing orders and information from KPIs and metrics. Having accurate, efficient data integration into your processes frees up additional space for the entry of new orders. Ultimately, this leads to more orders shipped, which further drives demand for your services within the supply chain. As a result, your business improves, and the amount of data in logistics grows to reveal problems within your existing processes.
Since modern commerce demands exceptional scrutiny and visibility, data in logistics can be used to pass information along to customers. This includes online shipment status options and notifications for customers when shipments become distressed. Furthermore, the same data can be used to identify other routes and solutions to avoid causing an additional distressed shipment.
If your existing shipping processes involve international trade, increased visibility may also be a concern for avoiding penalties, fees, and delays for violations of compliance regulations. Having this data in logistics readily available can help clear up any potential problems at the point of entry or port, which helps promote timely and less expensive shipments across international borders.
Using data in logistics to generate an accurate forecast of shipping processes sounds complex. However, it’s only the application of historical data about given time frames to determine your needs for a given event. For example, holiday forecasts in shipping are often used to add more workers to loading docks, shipping centers, and warehouses. Having more employees in place will alleviate the congestion from high-volume times, which is the overall goal of data applications.
Monitoring shipping processes for inefficiencies rests at the core of data collection and analysis. However, data in logistics can be further simplified by organizing it into KPIs and metrics.
Metrics and KPIs should reflect real-time information about your shipping processes. For example, a loading dock is behind on schedule, a driver has not yet arrived, or a shipment lacks item A. Each of these bits of information can be used to change your processes to address the problem. Basically, metrics and KPIs are a means of preventing and addressing distressed shipments.
Metrics and KPIs may also be applied to performance management concepts to maintain efficiency within your workforce. For example, errors in picking rates and delays in picking procedures may warrant additional training of your employees. Metrics and KPIs give insight into how your operations exist on an on-going basis, which allows you to make immediate decisions to improve efficiency and workflow.
Every transaction, scan, arrival, departure, and order processing method in shipping generates data. However, some of this data in logistics lacks value in the overall view of your transportation processes. Your company is probably collecting data on when employees clock in, move between warehouse zones, and arrive to the dock. Unfortunately, this data does not necessarily provide insight into how to change your operation unless it has been cleaned.
Cleaned data refers to data that has been processed by a computer to eliminate erroneous material. This helps you make decisions that will affect your current processes without risking a negative change in other processes.
For example, uncleaned data may reflect a problem with warehouse zone A picking times. You would be inclined to send additional workers to help with the problem. However, cleaned data will show the problem occurred at a given time, how it was or is currently being resolved, and what type of action will best benefit the situation. If the data were uncleaned, you may have made a decision on the basis of outdated, albeit only a few minutes of old information, data.
The application of data in logistics, transportation, and warehouse management does not have to be complex. Instead, data can be applied to current, ongoing procedures and processes to improve your efficiency and accuracy in your shipping tactics. Furthermore, data provides real-time insight into how to change and address current concerns and problems before they evolve into serious issues for your company. Ultimately, you must accept data’s benefits as well as its flaws when uncleaned. Think of data in logistics for improving transportation as your personal assistant in making business decisions. Data can help you improve your transportation processes when applied correctly.
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