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Modelos avanzados de extracción de conocimiento para mejorar procesos logísticos y optimizar cadenas de suministro

Resumen

Tipo:
Oferta Tecnológica
Referencia:
TOES20150327001
Publicado:
16/06/2015
Caducidad:
15/06/2016
Resumen:
Un centro tecnológico español ha desarrollado una nueva tecnología que aplica modelos de descubrimiento de conocimiento (KDM) para descubrir patrones ocultos en registros de sistemas de gestión e información. Estos modelos pueden utilizarse para analizar diferentes procesos industriales y logísticos, reduciendo costes y tiempo de almacenamiento. El modelo utiliza técnicas avanzadas como k-means, dendrogramas, algoritmos de clasificación, análisis de componentes principales, estimación, DOE (diseño de experimentos) y ANOVA (análisis de la varianza). Se buscan empresas de cualquier sector implicadas en procesos complejos de logística para establecer acuerdos de cooperación técnica, servicio y comercialización con asistencia técnica.

Details

Tittle:
Advanced knowledge extraction models to improve logistics processes and optimize supply chains
Summary:
A Spanish technology Centre has developed a new technology that applies knowledge discovery models (KDM) to uncover hidden patterns in information and management system records. These models can be used to analyze different industrial and logistics processes, reducing storage time and costs. They are looking for agreements technical cooperation agreement, services agreements and commercial agreement with technical assistance with companies of any sector involving complex logistic processes.
Description:
Process management systems generate large amounts of data containing historical information that is typically stored in management systems without any analysis. These large data sets contain useful information that can be used to improve efficiency. In this way, a Spanish technology centre with a large expertise in ICT and logistics fields has developed a new technology that applies a knowledge discovery model (KDM) to uncover hidden patterns in information and management system records (e.g. Enterprise Resource Planning, Warehouse Management System and Transportation Management System).

The model uses advanced techniques such as k-means, dendrograms, classification algorithms, principal component analysis as well as estimation, DOE (design of experiments), and ANOVA (analysis of variance) techniques. Such a model is applied to data sets taken from different systems, where the relationships between the main variables are analyzed and the most relevant information for understanding system behaviour is selected.

These models might be used for the analysis of different industrial and logistics processes. For example, it is common in manufacturing processes to characterize manufactured product quality from the tests carried out on samples of product. Applying this technology makes it possible to estimate these parameters from the manufacturing process data extracted; thus reducing the cost and time required to perform the tests. In addition, it also provides a manufacturing process control system to detect early changes in the final product quality, depending on the main variable values.

It is known that the logistic cost of a product is more or less the 11% of the selling costs and with the help of this technology this cost might be considerably reduced. Currently this technology is being tested in several industrial processes, which has allowed their logistic costs to be reduced up to 15% (even up to 20% in some cases) by decreasing the manufacturing, logistics and transport costs but always maintaining the quality of service.

Other potential uses of this technology are the configuration of a storage location map, the classification of customer behaviour using a set of references, the identification of the demand for certain products, the characterisation of energy consumption with respect to certain product manufacturing parameters and packaging portfolio selection. The technology uses information from company management systems (e.g. sales files, merchandise movements, stock levels, production master data), to which can be added other external information sources (e.g. sensors, data from other companies and macroeconomic indicators).

The technology centre looks for industry partners in order to sign technical or services agreements. They would also like to get commercial agreement with technical assistance. Under any of these agreements, the technology centre might use the technology to help the partner to improve its supply chain management.

The sought companies could work in any sector involving complex logistic processes, such as manufacturing, transport, storage or retailers.
Advantages and Innovations:
Using this technology, the technology centre may:

- Implement new knowledge extraction models to improve the company's logistics processes
- Analyze potential logistic improvements in the companies
- Apply these models to any sector
- Integrate this model with the company systems
- Detect the best opportunities to improve logistic processes such as forecasting, inventory, scheduling or transport

As main advantage, this technology reduces the logistics costs, maximizing the usage of resources (both staff and material).
Stage of Development:
Already on the market
IPs:
Secret know-how

Partner sought

Type and Role of Partner Sought:
- Type of partner sought:
Industry

- Specific area of activity of the partner:
The sought partner should use logistics processes or should be related in some way to the logistics sector. This may include retailers, services providers, manufacturers, transport and storage companies, etc.

- Task to be performed:
The partner should be looking for improvement in the supply chain management and he should be willing to contribute to the technology development from the industry's point of view.

Client

Type and Size of Client:
R&D Institution
Already Engaged in Trans-National Cooperation:
Si
Languages Spoken:
English
Spanish

Keywords

Technology Keywords:
02003004 Cadena de suministro
02008003 Logística
02003002 Redes de plantas de fabricación
02003005 Sistemas y procesado de información, flujo de trabajo
02008002 Transporte intermodal