CFF Business Intelligence Division
The company is involved in the Business and Market Intelligence processes
by studying the supply chain to search for the relevant information about a company’s
markets for the purpose of accurate and confident decision-making in determining
market opportunities by using a variety of Artificial Intelligence (A.I.)
Techniques for Data Analysis.
One of our current projects is aimed at improving the forecasting decision making
of companies. A.I. forecasting tools and data extraction techniques are used
in the collection of data from disparate sources to build up intelligence and improved
accuracy and detail in forecasting environments.
In this project, the A.I. and data search, retrieval, and aggregation work
is highly complex using new and novel modelling approaches for both use on stand
alone software packages that can be incorporated onto the software platform. The
tools and components will need to be embedded onto a software platform for the development
of the forecasting tool set.
The business intelligence division has many years of experience in this field with
the senior management team very much involved in developing new and innovative Artificial
Intelligence (A.I.) techniques primarily with the use of neural networks.
Choose a project from the list below in order to go to its description. You can
also scroll the page.
Development of an Autonomous Systems Development Tool (ASDT) for application within
manufacturing operations-planning
Budget:
Funding Stream: Collaborative Research and Development as part of the Technology
Strategy Board (TSB), previously run by the UK Government’s DTI/BERR department
Time Period:2011-2014
Project Details:
The overall vision of the research is to remove the failings of existing operations
planning systems in managing highly variable manufacturing environments. In such
organisations the high frequency with which process and supply chain disruptions
occur and changes in product design and customer demands happen, form major barriers
to increasing the competitiveness and maintaining the high rate of growth of successful
manufacturing businesses.
The key objectives of the project are to provide a means of adding autonomous decision-making
capability to the existing Finite Capacity Planning (FCP) processes of manufacturing
organisations. The focus is on using such capability to enable organisations to
make significantly faster, more flexible and more cost-effective responses to customers'
demands, and to enable efficient management and provision of higher levels of product
customisation, process innovation and delivery service. In addition, the ability
of such processes to operate efficiently in complex environments will offset the
adverse effects arising from the complex interactions of the frequent disruptions
and changes that take place in processing, supply chain and demand processes.
A step change in the operations planning process is required and will be facilitated
by the highly innovative nature of the autonomous decision-making processes that
can be applied within them. These processes arose from EPSRC-funding research projects,
undertaken by the academic partner, that have successfully proof-of-concept tested
the application, to the operations planning process, of the basic principles of
autonomous gene regulatory control, within complex biological organisms.
Partners: C4FF, Preactor International Ltd, Plessey Semiconductors Ltd, TDK
Lambda UK Ltd, Vision in Print, Tata Steel Europe Ltd, De Montfort University.
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On-the-cloud environment implementing agile management methods for enabling the
set-up, monitoring and follow-up of business innovation processes in industrial
SMEs (ExtremeFactories)
Budget:€3,204,981
Funding Stream:European Union
Time Period:2011-2014
Project Details:
The ExtremeFactories project proposes the conception of a collaborative internetbased
platform with semantic capabilities (by means of ontology modeling) that implements
a new methodology for the adoption of a systematic innovation process in globally
acting networked SMEs. The platform will support SMEs to manage and implement the
complex innovation processes arisen in a networked environment, taking into account
their internal and external links, by enabling an open multi-agent focused innovation
(i.e. a customer/provider/supplier/employee focused innovation). The solution will
be specifically focused on the needs of manufacturing companies and will observe
both product and process innovation.
The project has a strong industrial basis; putting together the efforts of 7 industrial
manufacturing SMEs in the way to become virtual networked organizations by the way
they handle their relationships to third parties, such as customers, suppliers,
distributors, etc. This big effort will result in a methodology and platform that
will be validated and assessed in predefined business scenarios at these organizations.
The project proposes a solid dissemination plan, offering a community management
activity in order to get a wider target, as well as a first version of an exploitation
plan to be detailed during the project.
Partners: C4FF,INNOPOLE, ATB Institute for Applied Systems Technology Bremen
Gmbh, Vaibmu, SafeviewTV, FAMMSA, OAS gmbh, Armbruster, mb air systems ltd, Charles
Robinson (Cutting Tools) ltd., Nikari Oy.
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Improving Demand Forecasting and Cost Forecasting (AI Market Intelligence)
[Project Running]
Budget: £1.3 Million
Funding Stream: Collaborative Research and Development as part of the Technology
Strategy Board (TSB), previously run by the UK Government’s DTI/BERR department
Time Period: 2007-2010
Project Details:
The project is intended to assist those business organisations who make frequent
use of quantitative and/or qualitative models for making a variety of business decisions.
It will achieve this aim by automating the data identification, collection and analysis
tasks involved in the modelling process hence considerably reducing the high levels
of cost, expertise and time resources required. A generic modelling process
will be developed applicable to a wide range of strategic & tactical decision areas,
including:
- Decisions on pricing, design and marketing
- Forecasting future demand for products and services
- Estimating the costs of new products and processes
- Predicting future capacity and inventory requirements
- Identifying and selecting suppliers
- Designing a new component which requires access to historical data on other components
and materials
As well as the economic business advantages, regulatory compliance legislation is
increasingly placing greater imperatives on having good, easy to use and transparent
data retrieval and analysis processes which the proposed project work is intended
to address.
More specifically, the project is aimed at improving the forecasting of inventory
within supply chains. Artificial Intelligence (A.I.) forecasting tools have
been developed as part of this project. This approach will enable improved accuracy
and detail in forecasting environments to be gained.
The A.I. and data search, retrieval, and aggregation work is highly complex
using new and novel modelling approaches, which both use stand alone software packages
that can be incorporated onto the software platform. The tools and components will
need to be embedded onto a software platform for the development of the forecasting
tool set.
The end product will be fully functioning piece of software with “stand-alone” and
“open integration” potential with the embedding of Artificial Intelligence
components for improving the demand forecasting process.
Check out www.dmucfm.co.uk
for more details
Partners: C4FF, De Montfort University, Preactor International, Sustainable
Technology Solutions, Trelleborg Industrial AVS, Unipart Logistics Ltd,.
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Reducing Road Freight Empty Running (REFER)
[Project Running]
Budget: £1.3 Million
Funding Stream: Collaborative Research and Development as part of the Technology
Strategy Board (TSB), previously run by the UK Government’s DTI/BERR department
Time Period: 2010-2012
Project Details:
The REFER project will develop an innovative system for significantly reducing the
levels of empty and part-filled running, ie backloading, of freight road vehicles.
This will lead to reduced freight operating costs, fuel usage and carbon emissions.
It will achieve these aims by developing processes that overcome existing issues
with ‘embedded behaviours’ and enable improved matching of the ‘available empty
and part-filled load journeys’ of freight enterprises with customer’s demands for
goods to be moved.
Current vehicle backloading planning and routing systems are only fully capable
of supporting one-way outbound distribution. The result is that average freight
vehicle loading utilisation factors are less then 40% and empty running of vehicles
accounts for ~29% of total UK freight vehicle kilometres. Some capability for using
software solutions to improve efficiency by integrating demand for consignment movement
to reduce vehicle-miles with empty or minimal vehicle loads exists, however human
intervention is required due to the short-term and highly-variable demand involved
in the movement of goods.
This project will build on the consortium's existing expertise with knowledge management
and Artificial Intelligence (AI) planning and result in significant beneficial effects
on transport networks by reducing the ~8 billion miles currently travelled by empty
and part-filled freight vehicles and the ~1 billion kgCO2e emitted by freight vehicles
during this travel.
It will support the competitiveness of the UK logistics industry by producing a
marketable solution providing realised systems capable of integration with existing
distribution planning software that operators see value in and will want to use
and buy.
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Development of Artificial Novel Neural Network Models for application in Advanced
and High Value Manufacturing
[Project proposal ready for Submission]
Budget: £950,000
Possible Funding Stream: Collaborative Research and Development as part of
the Technology Strategy Board (TSB) previously run by the UK Government’s DTI/BERR
department
Time Period: 2010-2013
Project Details:
The project concerns the development of pull system for application in the advance
and high value manufacturing industry. The model under consideration uses innovative
A.I. forecasting techniques to predict the demand and based on this information
it develops a streamlined manufacturing process for maximum effectiveness and efficiency.
A consortium is formed and the initial research has commenced.
Partners: C4FF and 5 major companies and one university.
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Application of Neural and Expert Systems in Capacity Requirement and Ship Building
[Project under consideration – and – seeking involvement in similar project]
Budget: £1.5 Million
Possible Funding Stream: Collaborative Research and Development as part of
the Technology Strategy Board (TSB) previously run by the UK Government’s DTI/BERR
department in conjunction with the UK Government’s Ministry of Defence / EU Seventh
Framework Programme
Time Period: 2010-2013
Transport by sea is growing rapidly and is fast becoming the safest and most efficient
mode for the transfer of goods and services. Furthermore the emergence of China
and India as economic powers has witnessed the growth of competition amongst the
commercial shipping sector.
An opportunity has arisen to use dynamic new tools to predict capacity requirement
and apply neural and expert systems to build ships at a minimised cost.
An activity based costing system would be adapted and ship construction process
would consider maintenance requirements as well as the dismantling arrangements.
Safety issues would be incorporated in the design phase. The project would involve
importing knowledge, cognitive and learning systems, simulation and visualisation
techniques as well as technology enhanced learning, adaptive and active learning.
Dismantling would be a corner stone of the intended areas for particular attention
and recycling of dismantled components would be a priority area in the knowledge
solicitation of the intended expert system.
Partners: C4FF in collaboration with over 10 other EU based organisations.
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Responsiveness in Ship Building (RiSB) - an investigation into the design, manufacturing
and management processes considering modern lean and total quality principles to
improve demand and capacity forecasting for merchant navy vessels
[Project under consideration – and – seeking involvement in similar project]
Budget: £1.2 Million
Possible Funding Stream: Collaborative Research and Development as part of
the Technology Strategy Board (TSB) previously run by the UK Government’s DTI/BERR
department in conjunction with the UK Government’s Ministry of Defence / EU Seventh
Framework Programme
Time Period: 2010-2013
The initial aim of the investigation in the maritime sector concerned how small
and medium manufacturing enterprises manage their design and manufacture processes.
This would lead to the development of an improved manufacturing management system
using modern lean and total quality principles that are capable of reacting responsively
to changes in the competitive global market place. The end point of the
project involves the development of an improved demand and capacity forecasting
of merchant navy vessels
Partners: C4FF, De Montfort University and six organisations
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MobDiMa, Mobile Direct Marketing
[Project under consideration – and – seeking involvement in similar project]
Budget: 1.3 million Euros
Possible Funding Stream: Eureka
Time Period: 2010 - 2012
The project proposed focuses on the development of Mobile Direct Marketing (MobDiMa),
which will make direct marketing activities more accessible and convenient for market
researchers.
Through the use of mobile communication technology the aim of the project
is to develop an IT solution that will successfully replace paper & pen technology
currently used by market-researchers. At present street based market-researchers
use primarily a pen and paper to record their immediate findings, this project will
seek to replace pen and paper with specially adapted mobile phones, which
will improve data collection and storage ability.
The mobile phones will have a special application installed, which will be used
by researchers collecting survey results. Web-based servers will then be used to
store data over the mobile network and will make the results available on the internet
to assist market research specialists to manage survey data, build demographic structure
definitions, and browse results. Therefore the mobile technology platform will aid
in the accurate attainment of market research and provide the results instantaneously,
and in a cost effective manner.
Because mobile communication technology is new and evolving, it is at the
beginning of being developed for marketing activities. Therefore at present there
is no comparable competition. Our competitors offer different approaches
that are all based on older technologies and equipment, and remain largely unsuccessful.
The technological feasibility of MobDiMa has been proven in the pilot project,
and major problems in the implementation are not expected. The pilot project was
tested on real marketing activity and testing customers were very satisfied. Therefore
the project is ready for a practical execution.
Partners: AdaptA, C4FF and 6 other major companies.
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