The effectiveness of intralogistics systems is crucial for successful warehousing operations. Those systems can vary from tangible automated technologies and machines to advanced software and analytics, and they provide essential benefits to the warehousing business entities in terms of improvement in several areas:
- Transparency of operations
- Real-time control
- Reduced costs, etc.
Huge volumes of data in Industry 4.0 are generated through the number of machines, sensors, vehicles, cranes, and conveyors that are used in the different processes.
And those data can be effectively handled and used in an accurate and relevant manner through one particular intralogistics innovation: Augmented Analytics.
Augmented analytics is the new era of machine learning. This new trend combines ML (machine learning) with NLP (natural language processing) in order to improve the quality of the analytics, make them more detailed, consistent, understandable, clear, and meaningful to the business entities. In the practice of Augmented analytics, data is being treated just as humans would, but automatically, on a larger scale, and with increased pace.
And in a more scientific formulation, according to WonderFlow, “AA is the use of statistical and linguistic technologies to enhance data management performance.”
This innovative tool has become key in intralogistics operations. Even though it is intangible, it has the core value of using technology to assist and effectively improve intralogistics operations, and support human work in terms of decision-making and planning the smart freight handling process.
3 reasons why you need AA in intralogistics
1. Help employees deal with complex data
AA helps the businesses to transform their big, voluminous sets of collected data into smaller “more digestible” pieces, which are more understandable, insightful, and valuable to the working units of the organization. This allows a data-driven decision-making process throughout all of the hierarchy levels in the company with the purpose of improving productivity, scalability, speed, and flow of operations.
Besides, as AA uses NLP to present data-based feasible freight handling recommendations, the analytics are easily understandable by every level of working units in the organization. This stimulates the usage of data in planning and decision-making, to achieve economies of scale through increased output and lowered costs.
2. Take the best out of the data and save time
The Augmented Analytics prepare the data in order to bring Business Intelligence and facilitate the managers and workers to make efficient use of their data resources, thus, to interpret them in the best way and make the most out of them for the most efficient material flow in the logistics plant.
What is more, until now, analytics have usually been prepared by a data scientist, who would spend 80% of his time extracting and preparing data, and only 20% interpreting the data sets to derive business insights and develop useful and feasible operational recommendations.
With Augmented analytics, we can receive an automatic and quick action plan for intralogistics improvement and success, supported with charts and graphs, which are derived instantly from the latest available business data resources. This way, we don‘t use old, irrelevant data and don’t waste time manually extracting, processing, integrating, and interpreting data. As a result, the warehouses are provided with a great competitive advantage in quickly meeting the changing and evolving intralogistics technologies, trends, and processes, and responding adequately to those industry changes.
3. Enjoy errorless data analytics
Another benefit of AA comes in terms of data veracity. When it is manually manipulated, the chance of making a mistake in the interpretation of data sets is significantly higher than in the case when we use an automated system for the same purpose.
What is more, the AI used diminishes the risk of situational bias. Any data scientist is biased by his own way of processing data, the way he makes decisions, his routine tasks and habits in terms of the process. Because after all, every human has his own way of understanding and conducting tasks.
But what companies neglect is the fact that this cognitive bias can result in outliers, incomplete, or “short value” data analytics reports. With the implementation of augmented analytics tools, organizations benefit from transparent and non-biased data processing, high-quality results in terms of trustworthy analytics, closely related operational recommendations, and aligned action plan to achieve high-level productivity and cost reductions.
An example: AA and Shuttle systems
The so-called “shuttle systems” are the innovative, better, improved version of Automated storage and retrieval systems. They outscore the mainstream AS/RS by a number of factors:
- Flexibility and scalability
- Efficiency in materials handling
- Reduced freight and manufacturing costs
- Fast movement of handling goods
- Productivity (average 1000 picks per hour)
- Advanced control
- Lighter and smaller, but with an increased equipment capacity
- Use multifunctional rails
- Enhanced energy efficiency and ability to use innovative energy sources
This innovative intralogistics technology is very compatible with the usage of Augmented Analytics. As the shutter systems are composed of autonomous vehicles, those analytics can be effectively used to improve the positioning, area of movement, and warehousing motion intelligence.
What is more, they enable the logistics management to have better control over the real-time condition of the shuttle systems, recognize potential operational and technical issues, and intervene to avoid quick amortization of assets, due to malfunction or glitch.
In Industry 4.0, the intralogistics processes and systems have become a key area for improvement through innovative tangible and intangible technologies.
The Augmented analytics help the warehousing businesses to make the best out of the data and capital resources they have available and facilitate sustainable organizational development in terms of:
- Cost reductions