MIDIH has an experiment in the cutting tool sector (NECO) focusing on smart management of product orders and their integration with the production, in particular to optimize data interoperability between logistic data, raw material costs and manufacturing critical paths with the goal to optimize the capability of the PSS (Production Service Systems) providing an optimal prediction of delivery time and associated costs and lifetime of the cutting tool.

Clienrs will benefit from speed-up of the design process and faster offers --> improvement of brand image.

Increases the commitment to the client: KPI: 30% reduction of broken promises

Standardization of tool changes. Warehouse inventory reduction: KPI: 30% decrease on the average tap stock level in the facilities compared with the AS IS situation prior to MIDIH

Repeatability of the process KPI: 50% decrease on the number of tools reworked compared with the "As-Is" situation prior to MIDIH. Improving the manufacturing process (KPI 10% defective product reduction) Lead time reduction (KPI 15% time reduction in the delivery).

The most important business indicator will be the time per offer. The time of the team to prepare and launch an offer will decrease by 10% (KPI)

In MIDIH we have two experiments in the area of Smart Factory, one in the automotive sector and one in the cutting tools sector.
The scenario in the automotive sector is focused on the quality of the welding process in truck manufacturing. The experiments to be performed aim at testing a continuous monitoring of the welding cell and robots and Data analysis algorithm to detect unexpected behaviour or to plan special maintenance intervention based on historical data.                                                                           
The scenario in the cutting tools sector is focused on the management of the cutting tools production process. The experiments aim at testing the integration of digital twins to improve quality control and optimize performance and maintenance of the manufacturing machines, monitor and keep track of the manufacturing costs.

Predictive maintenance service implemented in the welding cell focuses on following KPIs:
– 20–30% decrease in costs for the management of unpredicted maintenance events reduction
– 5% increase in Mean Operating Time Between Failures (MTBF)
– 0.5–1% OEE increase
– 5–10% decrease in low quality bodies

The experiment in the automotive sector in the area of Smart Factory (already described for the previous objective can also contribute to this objective:
The scenario is focused on the quality of the welding process in truck manufacturing. The experiments to be performed aim at testing a continuous monitoring of the welding cell and robots and Data analysis algorithm to detect unexpected behaviour or to plan special maintenance intervention based on historical data.

Investments, savings, efficiency growth expected etc. in 3 years (KPIs):
– 20–30% decrease in costs for the management of unpredicted maintenance events reduction
– 5% increase in Mean Operating Time Between Failures (MTBF)
– 0.5–1% OEE increase
– 5–10% decrease in low quality bodies

The second Business Process for the Smart Factory scenario in the automotive sector consists in the application of the MIDIH solution in the Campus Melfi, involving a welding cell, equipped with the same sensors
and SCADA system as in the Suzzara plant, other robots and AGVs.

The KPIs related to this Business Process are the maintenance costs and the number of low quality products and its respective decrease.

In MIDIH we have also experimentation in the Steel Sector covering the Smart Supply Chain scenario.
This scenario focuses on the ecosystem of 3D printing services. There is a rising number of 3D Printing Hubs in the market, but there is sophisticated engineering know how needed to create a printable file to maximize the benefits of 3D printing. Beside the optimization of the part itself, there is the choice of the fitting production technologies and finally the availability of logistic services. All these aspects lead to complex decision-making process in optimizing the delivery of the part. The experiment aims to establish a self-organized distribution network as part of a digital self-organized supply chain. The dynamic forming of the supply chain should be realised by a service application. The application plans an order-related distribution by exploring an open market for logistic services, based on a customer designed, preferred distribution network.

The targeted value of the experiment is to create Logistics vocabulary for smart supply chain, the creation of a distributed analytics service for the calculation of the estimated time of arrival and vocabulary for digital twin as foundation for smart product.