Experiment 1 – Digital Style

The Digital Style experiment is expected to provide the instruments to support the management of Piacenza to find a very rapid and highly optimized approach to the problem of fabric production optimization.

MOTIVATION

Piacenza is a SME manufacturer of fine woolen fabrics, for the high-end luxury market. The production strategy of Piacenza is based on strict and integrated control of production. Piacenza keeps internal those production phases which give an added value perceived by the customer (for ex. Raw material acquisition, finishing, inspection) or a production flexibility and cost advantage (weaving, yarn dyeing). Piacenza competitive strategy is focused on maximum differentiation of the product, in terms of raw material choice, style, and colour. Each season more than 2.000 designs are introduced into the market. This key competitive advantage is enforced by design know-how and quick flexibility to customer requests, where Piacenza offers new, customized and/or exclusive fabrics in close cooperation with fashion stylists. Piacenza’s strategic target is not to increase quantities but average price, enforcing market barriers based on design, know how, personalized service and sharp delivery.

PURPOSE OF THE EXPERIMENT

The goal of the experiment is to create a digital copy of the production process (digital twin) that shows all the parameters necessary for its elaboration and analysis. This digital model has the goal of optimizing the production planning in the weaving department thus facilitating, taking into consideration all the existing constraints (machines, article compatibility, technical lead-times, customer delivery dates), improving the accuracy and effectiveness of this planning (at the same time reducing the use of intermediate production batches, reducing the stock of semi-finished and finished products produced in larger than needed quantities in order to accommodate technical constraints). The solution aims to use cloud-based software and services (Machine Learning, AI, Advanced Analytics) and with these tools to build optimization algorithms that can provide alternatives and what-if simulations to evaluate the impact of certain choices on the production mix and on the machines allocation and on the respect or not of the delivery term (dates and quantities).

TECHNICAL IMPACT

The combination of the peculiar production and competitive approaches has led to a very challenging problem of production optimization management, which must satisfy rigid and high demanding service and design requests by clothing fashion customers on one side, and a complex and fully integrated production process on the other. With around 1 million meters produced per year, 200 meters of average lot and a continuous refreshing of  the planning due to the overlapping of regular production, exclusive one, prototyping and sampling, the repetition of the same lot with the complexity of the management of Piacenza production planning, requires to be supported and updated with new and innovative approaches to remain up to date to the needs of the market. The above-mentioned production scenarios make the data analysis a huge challenge. A big effort must be placed in gathering the sufficient amount of data for a good output without overloading the system of un-necessary data.

Project Partners

  • Piacenza is the end-user in the experiment, providing the use case and the data.
  • Porini is an Independent Software Vendor, who will mainly run the experiment for providing the optimization algorithm.
  • Domina is the services provider of Piacenza and is in charge of gathering and providing the relevant dataset.
  • START4.0 is the Digital Innovation Hub with the role of experiment supervisor.

Who else is in the project?

Contact our team directly.

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