Feb 13

New Industry Projects on Inventory Forecasting

Logistics Systems Dynamics Group (LSDG), and
Panalpina Centre for Maufacturing and Logistics Research at Cardiff University

Cardiff Business School has partnered with Yeo Valley, the largest organic dairy producer in the UK, on a project to streamline the company’s forecasting, planning and replenishment systems. The £220,000 two-year collaborative project is being led by Dr Laura Purvis, Professor Stephen Disney and Professor Aris Syntetos, from the School’s Logistics and Operations Management section. This new project and partnership enables the School and Yeo Valley to conduct research into flexible and resilient operations and supply chain management strategies. The project runs with the contribution of one postdoctoral research assistant, Xia Meng.

The School has also partnered with Hilti on a project co-funded by the Economic and Social Research Council (ESRC, UK) to elevate the company’s inventory forecasting performance. Hilti is a multinational company that develops, manufactures, and markets products for the construction, building maintenance, and mining industries. Dr Qinyun Li will be working for the first half of 2018 with Hilti’s global statistical forecasting team, after having been awarded an early career research fellowship grant from ESRC.



Feb 13

TU Darmstadt Department of Law and Economics – Research Assistant position

The Department of Law and Economics invites applications for a vacant position of a

Research Assistant – 75 %

at the Institute for Production and Supply Chain Management, initially limited to 3 years.

An option for increasing the coverage of the position is available – for more information please contact the head of the institute.

Applicants should have a Master or Diploma degree, comparable to a German University degree, with a focus on quantitative studies, preferably in the areas of business mathematics, business informatics, industrial and business engineering with majors in operations research/management science, logistics or industrial management/supply chain management.

Applicants with experiences in the use of mathematical optimization software, such as Mathematica, MatLab, CPlex or LINGO, will be given preference. Applicants should further be fluent in German or English; proficiency in both languages is desirable. The prospective job holder is expected to contribute to research and teaching at the institute, in particular:

•Contribute to the various research projects of the institute in the area of production and logistics
•Contribute to the preparation of (and conduct) lectures and seminars
•Supervise student theses
•Take over administrative duties

Opportunity for further qualification (doctoral dissertation) is given. The fulfillment of the research and service requirements attached to this position serve at the same time as fulfillment of the academic requirements for a candidate’s doctoral degree.

The Technische Universität Darmstadt intends to increase the number of female employees and encourages female candidates to apply. In case of equal qualifications applicants with a degree of disability of at least 50 or equal will be given preference.
Wages and salaries are according to the collective agreements on salary scales, which apply to the Technische Universität Darmstadt (TV-TU Darmstadt).

Please send your application including a CV and certificates preferably in electronic form to: glock@pscm.tu-darmstadt.de or by mail to: TU Darmstadt, Prof. Dr. Christoph Glock, Fachgebiet Produktion und Supply Chain Management, Fachbereich Rechts- und Wirtschaftswissenschaften, Hochschulstr. 1, 64289 Darmstadt, Germany.

For questions and further information please do not hesitate to contact us.

Code. No. 24

Application deadline: February 15, 2018


Feb 13

Call for Papers – Machine Learning for Big Data in Industrial Processes

Special Issue on Machine Learning for Big Data Analytics in Manufacturing and Logistics Processes

Applied Mathematical Modelling invites submissions of original contributions to machine learning research for Big Data Analytics for Optimization of Manufacturing and Logistics Processes.

1. Summary and Scope

Machine learning is continuously enhancing its power in a wide range of applications and has been pushed to the forefront in recent years partly owing to the advent of big data. Thus, machine learning techniques have generated a huge societal impact in a wide range of applications such as computer vision, speech processing, natural language understanding, neuroscience, health, and Internet of Things and business process improvement. Moreover, in the context of big data, machine learning algorithms enable to uncover more fine-grained or complex patterns and make more timely and accurate predictions than ever before, e.g. for sales, marketing and tailor-made advertising applications for customers.

The data comes from different sources and in different forms and formats (i.e. structured or unstructured) such as consisting of a complex mixture of cross-media data content. For example, text, images, videos, audio, graphics, process signals, and time series sequences in logistics and manufacturing processes. The complexity, size, variety, and uncertainty (noise) in the data make it challenging to analyze the data and build models with it using traditional approaches. Machine learning methods have extensively been used in many industrial application areas such as pattern recognition, object and product identification and steering, predictive maintenance, scheduling and material flow control, predictive analytics in supply chains for logistics planning purposes using industry 4.0 environment, and statistical process control. Machine Learning is programming computers to optimize a performance criterion using example data or past experience. They are most useful when learning is needed in the absence of human expertise, or humans are unable to explain their expertise, or solution changes over time, or solution needs to be adopted in particular cases[1]

This special issue will focus on brand-new research results and shared recent advances in this research area. We solicit original contributions that have a strong emphasis on data analytics using machine learning techniques.

The list of possible topics includes, but is not limited to:

Machine learning methods for

  • Business process improvement and optimization
  • Analysis of real-time business process data
  • Real-time data analysis in a statistical process and quality control
  • Predictive analytics in supply chains
  • Machine learning methods in process optimization and quality control
  • Predictive maintenance
  • Logistics and manufacturing process optimization
  • Data analytics in manufacturing and logistics processes
  • Industrial analysis and mining applications via machine learning methods


Read the full Call for Papers >>>

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