The logistics operations require experienced employees, so the lack of labor is expected to get worse. Therefore, businesses need to strategize their operations. Focusing on just effective hiring isn’t enough, and they should implement proper retention practices driven by data.
Data is crucial for empowering the warehousing service and enhancing productivity.
Maturity cycle of data
Advanced data analytics or machine learning isn’t going to solve all the workforce-related problems. Utilizing data can be pretty complex, as there are various interconnected layers to explore.
Understanding the importance of prescriptive analytics empowered by machine learning is crucial for businesses. However, they need to consider other elements that support them.
The challenges
Businesses need to collect data to make decisions. For general transaction data, there are various options for sourcing data, such as your ERP and WMS. Internet of Things machines, such as sorters and conveyors with sensors, can also collect data. Electric devices like computers and printers can also showcase essential data.
While you already have all that information available, you might not know how to get started. This requires a thoughtful, planned, and strategic approach to collecting the right data for your needs.
Data cleansing
Many companies collect as much data as they can and store it for the future. Once they encounter a problem, they will access the data. However, this isn’t that simple because the business needs to cleanse the data in order for it to be usable.
The process involves scanning for errors, removing them, and identifying consistencies, duplications, and other things that could affect consistency and quality.
For example, one of the errors can be not counting the breaks in the productivity assessment. The data cleansing process also includes structuring everything and coming up with reports and dashboards that are ready to be analyzed.
This serves as a valuable base for descriptive and diagnostic data that can be transformed into predictive and prescriptive data.
The four types of data
These four types of data are crucial for the decision-making process. Descriptive data explains what exactly happened. The diagnostic data answers why that happened. Similar to that, predictive says what to expect in the future. And prescriptive explains the steps we should do next.
When the data is presented in the form of reports and dashboards, it allows you to spot the trends that can aid the decision-making process. But you should also keep in mind that the data might change at the moment when you’re making a decision, resulting in it being less relevant and accurate.
In the logistic process, the descriptive data can help identify how many times the order picking had delays that impacted the shipping.
Also, it provides useful insights for the productivity of individual order pickers. The descriptive analysis can help assess the customer’s behavior and predict the demand.
Machine learning and human input
Once you have the data available, you need human input to come up with the answers. It is almost impossible to submit the data to a computer and have the prompts all generated. At this stage, it is essential to acquire people who understand the problems and can come up with potential inputs and outputs.
Data scientists play a crucial role in understanding the business challenges and helping the machine learning with applying the proper data. The data scientists also need to find the right model for your specific case and learn how to maintain and train it.
AI and machine learning can be used to measure productivity, which is completed with engineered productivity standards. In the warehouse, this means that knowing the productivity of your employees allows you to adjust the process to make it more efficient during periods of high demand.
You can allocate the workers according to their best skills to use their competitive advantage.
Author Bio:
Alex Buzan is the co-founder of the White Hat Link Building Service BeneValue.biz. He possesses vast experience in the automation of outreach processes and helps individuals and SEO agencies with link-building activities.
