The use of advanced techniques applied to operations planning, from inventory management and procurement, production planning and resource allocation to distribution logistics, makes it possible to optimize all these processes, gaining efficiency and quality of service to customers.
These advanced techniques include the possibility of building digital twins of the processes as well as artificial intelligence techniques that allow automating tasks and obtaining new insights derived from the analysis of process data.
Applications include those related to layout design, improvement (and design) of operational processes and material movements, virtual commissioning, improvement of warehouse operations, etc.
We work to achieve a more competitive and sustainable industry by optimizing production and logistics processes supported by simulation tools from the logic of the manufacturing process, demand, supplies and production capacity.
For this purpose, we use Kajal®, ITA’s platform, which allows us to optimize logistics, production and transportation processes in an integrated manner, using the necessary modules for each case to address the specific problems of each company. It allows to handle different demand profiles, manage stocks in a multichannel environment and configure and optimize different replenishment and production target functions adapted to the needs of each customer.
In order to analyze and optimize the design of an industrial plant or the movement of materials within a factory or distribution center, we use tools that allow us to create digital twins. A digital twin allows us to capture the precise state of the current process and simulate hundreds or thousands of possible scenarios (starting from the current situation) and select the best decision (or the best three) for the decision maker to make the most appropriate decision. to this particular moment, secure in the knowledge that all available options have been explored.
The digital twin can help us explore new situations, new layout designs, new supply chain configurations, etc. that are difficult to evaluate a priori in the real process (economic constraints, risks). They help us to innovate and explore new combinations that can improve the current process performance (for example: new automations, modification of storage areas location, different task distributions…).
