Trace To Andon

Trace to Andon is a material flow management system developed for Magneti Marelli Suspension System in Melfi (PZ).

CUSTOMER:

Magneti Marelli Suspension Systems di Melfi (PZ)

DATE:

2017 - 2018

SERVICES:

Web platform development, Engine application and Android app

Magneti Marelli operates internationally as a supplier of high-tech solutions and systems products for the automotive world. The head office is in Italy, in Corbetta (Milan).

It supplies all the major car makers in Europe, North and South America and Asia with the main objective of combining quality and competitive offer, technology and flexibility in order to make excellent products available at competitive costs.

In particular, the workplace object of our action is Magneti Marelli Suspension Systems in Melfi. This business area designs and manufactures automotive suspension modules and components. The business line is based in Turin and, in addition to Italy, is present in Poland, Brazil and the USA. Inside, the products available range from the single component (oscillating arms, crosspieces, axles, uprights, brake discs and drums) to the assembled modules (wheel units, semicorners). 

Requirement to satisfy

The production lines of the plant are divided into assembly islands. Based on production orders, the production of a specific product is assigned to each island and is therefore supplied with the necessary raw materials, taken from the warehouse.


Is the operator employee to warehouse picking  to withdraw the materials on the basis of production orders and to take them to the operators of the assembly islands via a manual trolley. The flow of information is not computerized and takes place by means of paper supports.

Our proposal

We have profoundly changed the management of information: the data flow has been computerized and there is a wide screen available to the operator employee to the warehouse picking, which informs him about the state of the materials at the individual assembly islands.


In addition, the transport of materials from the warehouse area to the assembly islands is now carried out through the use of AGV (Automated Guided Vehicles).

Andon, in fact, is a Japanese term originally used to indicate paper lamps placed outside homes. In the context of Lean Production, the term Andon indicates a system to inform operators who deal with management, maintenance and other things of the presence of a quality or process problem.

The implemented system

TRACE TO ANDON consists of a MYSQL database, an engine, a web application and an Android app, developed in .NET technology.

The engine is an always on application that allows to check the stock of materials in stock at the assembly islands by checking through polling on appropriate production tables and supply data, specially sent by operators via the HMI web interface.


The web application allows supervisors to set privileges on the graphical interface for each operator, to change the stocks and call thresholds, the production times of the islands, as well as to manage all the personal data necessary for the system functioning. The wide screen is tuned to a live web page that shows the material calls to be enslaved before the related assembly island stops.

The Android app is used to end the material call by reading the data on the material labels via the camera.

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Results

The user interface is highly user-friendly and allows planning and sending supplies with a few easy interactions. The use of colors in the wide screen lines, positioned on the production lines, makes the understanding of state of the stock at the various assembly islands instant.

The operator will therefore be constantly informed, in real time, on the production trend and on the supply sequence of the assembly islands.

Its implementation has allowed the stabilization of the production rhythm and the leveling of the production and the entire enslavement system. The results achieved were the reduction of inefficiencies such as lack of material, overstock or delivery of wrong materials and drastic decrease in downtime with a consequent reduction in costs related to them. All this supported by an increase in plant productivity.


Furthermore, the system has made the production cycle more stable, linear and, since it can be analyzed, safer: knowing the associated risks and assessing the possible containments in terms of probability or parity of the event means increasing the safety of the work cycle. 

Finally, the correctness of the data is managed at the product level: it is checked that the bill of materials is correct and intact. Furthermore, a series of error-proofing algorithms, coded within the system, reduce the possibility of human errors.

The results obtained allowed the project to be among the candidates of the AI ​​Award 2017.