The MD4PROD (Manufacturing Digitisation for Production and Resource Optimisation) experiment – under IS4PROD DIH – targets the challenges of managing Production Scheduling and Logistics in a Precision Engineering Manufacturing SME. The existing paper based system is cumbersome and results in delays by operators in reporting production numbers which has a negative impact on Production Scheduling.

The solution combines barcode scanners and industrial grade touch screen tablets with BEinCPPS components to digitise the operator interaction with the factory Enterprise Resource Planning system. Up-to date production numbers are then available to Production Management, the status of machines and jobs are published throughout the factory and externally using mobile visualisation tools.

Benefits resulting from the project include the ability by Management to react in a timely manner to minimise outages of machines or delays in production activity. A considerable increase in machine productivity per shift has resulted in greater customer satisfaction with a marked improvement in on-time delivery.

This experiment has been supported under the second open call of BEinCPPS project.


Industry 4.0 and Digital Transformation are changing the way Manufacturing is developing. The “Fourth Industrial Revolution” takes advantage of multiple new technologies such as; big data & analytics, cyber-physical systems (CPS) and the Internet of Things (IoT) to provide more agile, customised and flexible production systems. In this scenario, integration of information is the key factor and one of the most challenging tasks. Although many hardware and software components are already available as off-the-shelf solutions, there are still innovation gaps to bridge, e.g. in linking shopfloor data, operator engagement and legacy equipment. The challenge is particularly severe for Manufacturing SMEs who are constrained in their access to expert knowledge and financial capital.

The Precision Engineering involved in the consortium is a Manufacturing SME based in Ireland that recognises the need to enhance the Digitisation of its Manufacturing Operations. The development of the proposed CPS-based Factory Logistics Management System will significantly impact on the factory’s internal logistics, specifically for planning, scheduling and monitoring products, materials and machines within their production system. The proposed experiment will build on components developed through the BEinCPPS Project. Additional tools to integrate with Enterprise Resource Planning (ERP) Platforms and to analyse Overall Equipment Effectiveness (OEE) will be developed as commercial products by another partner in the consortion, a Digital Technology Supplier SME in the Mid-West region. Access to data through a Wireless Sensor Network (WSN), Data Repository and Key Performance Indicators for production and resource optimisation will be supported by the Competence Centre (ACORN). Dissemination of the experiment success to a network of Manufacturing SMEs will be carried out by the i4MS Digital Hub (IS4PROD: Intelligent Systems for Production and Resource Optimisation), based in LIT.

Industrial relevance, potential impact and exploitation plans

The current production efficiency system present in the Precision Engineering company is run through an ERP (Enterprise resource planning) system. The machine operator enters information about part cycle time, machine setup time and production time. At the end of each shift, the number of parts manufactured and scrapped by an operator is entered. The ERP system database can then be queried during or after the production run to calculate the efficiency of the job. This system is based on industry standard principles; however it is 100% dependant on the accuracy of the data entered. Incorrect data entries or errors will skew the results and give misleading information to the production team. These inaccuracies lead to problems with future production planning and job costing.
The objectives of this project are to:

  • Develop Cyber Physical systems to gather and use the power profiles of production and other variables from the manufacturing machine to corroborate the information held in the ERP System. The goal is to be able to accurately determine part cycle times, number of parts per shift and production cycle start and finish times. This real empirical information can then be added to the ERP system and used to verify the results from the operator’s input and, once fully implemented, evolve into an automated real-time data input method.
  • Create a new business layer to interface the shop level data and the ERP system, even for Legacy Machines, resulting in a fully digitised and integrated environment which will receive information from machines and sensors from the factory and drive the Production Management System. Enhanced digital information on all production variables will be used to drive materials and components scheduling, digital displays for Operator feedback, workcell optimisation, machine maintenance and Key Performance Indicators (KPIs) for energy efficient operation.

The new business layer will be responsible for gathering all the information and translating it into the ERP protocols. The inclusion of operator interfaces and real-time feedback will also be investigated to identify production delays like maintenance, tooling problems, lack of material, etc. and also to show live OEE results which can increase production rates. It will also be possible to compare the power profile pattern of a part previously made and compare it to the next time the part is made. Differences in the part profiles may highlight either manufacturing errors and/or predictive maintenance. The information to do this need to be readily accessible and easily understood (at-a-glance) to be of benefit to the production management team who are operating in a fast-paced environment. The possibility of having the information from many machines brought together in one central system will be investigated in the experiment. By completing this experiment the Manufacturing SME will gain an in-depth knowledge of its production operations and resource consumption, will be able to develop strategies to enhance their production processes, will reduce costs and provide opportunities for expansion.



Other manufacturing