The Treviso-based company Stesi Srl, specialized in the development of software for the Supply Chain, will present at the Richmond Logistics Forum IT Solutions, scheduled from 5 to 7 July in Gubbio, the new solution designed for even more effective warehouse management.
Stesi boasts a proprietary software platform, known and used by important companies such as Alce Nero, Samo, Kasanova which have obtained significant advantages since it has allowed them to optimize production (MES) and warehouse management (WMS) activities.
Now Silwa has evolved and offers a very important new solution, silwaSLOT, destined to become a pillar of efficient logistics: able to better manage the activity of the loading and unloading docks, i.e. the entry and exit of goods, this new system allows time slots to be booked quickly and intuitively, planning and synchronizing all the preparatory activities of the warehouse staff and therefore optimizing space, time and resources. It is a native cloud system hosted by Microsoft Azure and equipped with a native Android App.
The silwaSLOT application module brings companies tangible benefits such as, for example, the reduction of waiting times for trucks in the yards, the reduction of the costs of logistic activities, the elimination of penalties for haulage stops and the maximization of productivity. In fact, the application allows you to organize and plan the activities of the loading/unloading bays by communicating with the drivers and effectively synchronizing the work of the operators.
In this way, any bottlenecks are eliminated, processes are always kept under control and excessive expectations by carriers and therefore penalties are reduced. The system is also able to communicate with the workers and carriers via an Android App with an intuitive interface which allows you to obtain real-time information on the status of operations and of the entire load.
Data analytics will also enable managers to evaluate the performance of carriers, carriers, suppliers and customers. Finally, the module makes it possible to detect inefficiencies and anomalies and to adopt the most appropriate productivity recovery strategies.