The problems experienced by the supply chain highlight the need to invest more in innovation and technologies of Deep learning for smarter and more efficient management.
The innovation and use of new technologies advances unstoppably in all sectors. In supply chains and large organizations is no exception and investment in process automation and intelligent management of supplies, as well as in business risk is where these advances are being seen the most.
However, there is still a long way to go, especially given the large number of manual processes that still exist and could be automated. This opens an opportunity for him Deep learning, a kind of Machine learning, or machine learning that, with the use of computational architectures, trains teams to perform tasks automatically as humans do.
With the incorporation of new technologies, such as Deep learning, a better efficiency of internal processes is achieved, as well as communication and collaboration with suppliers, increasing the competitiveness of organizations. This is what Antonio Fernández, Director of Technology of Fullstep, firm specialized in digitization end-to-end of the purchasing, procurement and supply chain process, who argues that “There are many procedures involved in the process, and the needs in each case are very different.”
For example, supplier approval is one of the areas that is still largely done manually and that could benefit from the improvements brought about by the incorporation of new technologies. According to Fullstep estimates, a large company has about 10,000 suppliers that it needs to approve for the purchase of materials and services. If each of them must provide an average of seven certificates, such as Social Security, Finance or ISO standards, among others, this represents an investment of up to 2,500 hours a year in their management.
According to Fernández, although the approval process is digital, since it can be sent and archived digitally, “There are procedures for viewing, validating documents and extracting key data that require heavy human intervention, which makes them slow and less than optimal from the point of view of time invested.”
This process, along with risk management which involves ordering new orders from a supplier, is where digitization processes require more human management. Although it is something that a risk analyst normally handles, “All this ‘artisan’ and specialized work has a procedure and cost that conditions the companies”, says the person in charge.
How to get the most out of Deep learning
To respond to all needs, Fullstep works on the development of tools based on Deep learning that allow the automatic validation of certificates, as well as the extraction of data, to obtain a smart supplier homologation as a cloud service.
Likewise, the company is also working on an intelligent system based on Deep Learning techniques combined with sentiment analysis for the automatic elaboration of risk profiles for critical suppliers.
The objective with this is that organizations can minimize the risk of hiring critical suppliers. For this, it has a platform based on APIs that facilitates integration with any ERP and that, thanks to the system of modules with which it is designed, facilitates the Spending Cycle according to the needs of each company.
The digitization of the entire purchasing and provisioning process allows organizations to save an average of 10% in purchasing management, automation and collaboration of the process. This also advances when it comes to avoid fraud or possible defaults by suppliers for greater transparency.
The possibilities of this platform allowed last year to successfully manage more than 12,000 million euros in purchase negotiations.
