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Dr Ross Clement

Job: Senior Lecturer

Faculty: Computing, Engineering and Media

School/department: Leicester Media School

Research group(s): Advanced Manufacturing Processes and Mechatronics Centre (AMPMC)

Address: 51Âþ»­, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 250 6675

E: rclement@dmu.ac.uk

 

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Research group affiliations

Interactive and Media Technologies

Publications and outputs


  • dc.title: Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers dc.contributor.author: Raju, Y.; Kang, Parminder Singh; Moroz, Adam; Clement, Ross; Hopwell, Ashley; Duffy, A. P. dc.description.abstract: Accurate forecasting of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. This paper is an attempt to understand the cause for the high level of variability such as weather, holidays etc., in demand of SME wholesalers. Therefore, understanding the significance of unidentified factors may improve the forecasting accuracy. This paper presents the current literature on the factors used to predict demand and the existing forecasting techniques of short shelf life products. It then investigates a variety of internal and external possible factors, some of which is not used by other researchers in the demand prediction process. The results presented in this paper are further analysed using a number of techniques to minimize noise in the data. For the analysis past sales data (January 2009 to May 2014) from a UK based SME wholesaler is used and the results presented are limited to product ‘Milk’ focused on café’s in derby. The correlation analysis is done to check the dependencies of variability factor on the actual demand. Further PCA analysis is done to understand the significance of factors identified using correlation. The PCA results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The correlation of the above three factors increased relative to monthly and becomes more stable compared to the weekly and daily demand.

  • dc.title: Using Agent-Based Simulation to Investigate Daily Order Variation of a B2B Fresh Food Supplier dc.contributor.author: Clement, Ross; Kang, Parminder Singh; Hopewell, Ashley; Duffy, A. P. dc.description.abstract: Agent-based simulation has been used to simulate customers of a B2B fresh food supplier, in order to examine why total orders vary considerably on a day by day basis. Different types of virtual customers can be included in the simulation, ordering products using different strategies including their own demand prediction. This simulation suggests that customers changing the day of their order is the largest cause of daily order variance.

  • dc.title: Classification and Clustering Approaches to Understanding Customer Ordering by Customers of a Fresh Food Supplier dc.contributor.author: Clement, Ross; Kang, Parminder Singh; Duffy, A. P.; Hopewell, Ashley dc.description.abstract: Purpose: This paper looks at characterization of B2B customers of a fresh food wholesale company supplying SME clients in terms of their weekly orders of a variety of fresh products. Customers whose orders can be predicted (days of the week order is placed, size of order) can easily be supplied without risk of waste due to the wholesaler ordering stock that is not sold to customers before it must be disposed of. Greater understanding of customer order patterns is necessary to improve demand prediction and reduce waste. Research Approach: Extensive real-world data from a fresh food wholesaler has been analysed in bulk. Customers’ weekly orders have been classified into one of nine classes depending on how each week’s order compares to the previous week. Equal order amounts on the same day (or days) of the week as the previous week are the most predictable class. Varying order amounts for orders placed on different days of the week are a much less predictable class. Other classes represent customers who either cease ordering after having made previous orders, or who place an order after not ordering in previous weeks. K-means clustering has also been used to extract clusters of customers showing similar ordering patterns from the customer base. These functions have been integrated into a data visualization tool which displays the clusters in terms of the frequency of occurrence of order classes, and their standard deviation within the clusters.

  • dc.title: Knowledge Engineering Based Forecasting to Improve Daily Demand Prediction for Refrigerated and Short Shelf-Life Food Supply Chains dc.contributor.author: Kang, Parminder Singh; Clement, Ross; Hopewell, Ashley; Duffy, A. P.; Garicia-Taylor, Marilu dc.description.abstract: The accuracy of demand forecasting for companies in the food industry is highly important, especially for those that deal with products that require refrigeration or that have short shelf-life, given the fact that the freshness and overall quality of the products offered can affect the profit margins for business and the health of the consumers (Doganis et al., 2006). Furthermore, Agrawal and Schorling (1996) as cited by Chen and Ou (2008) highlighted that having easy access to accurate and up-to-date information about demand forecasting is vital for any company aiming to maintain high levels of competitiveness in their market sector. This is even more important for fresh foods wholesalers, whose profit is directly affected by wasted or unsold products and unsatisfied customers (unfulfilled demand), especially when storage facilities are limited.

  • dc.title: Making connections between final year students and potential project supervisors dc.contributor.author: Clement, Ross; Bounds, Peter dc.description.abstract: A web-based final year project management system, ProMS, has been created and deployed to help coordinate undergraduate final year projects including automating practical tasks such as the submission of documents. ProMS helps introduce students to potential supervisors, through both student access to staff information, and staff access to draft copies of students’ project proposals. Many students who used the system found that it helped them become aware of potential supervisors whom they had never met, and a sizeable proportion of these students listed staff they were previously unaware of as preferred supervisors. The system helps greatly in expanding students’ knowledge of potential project supervisors. Following the deadline for student project proposals, ProMS made it possible to generate a draft allocation of students to supervisors in only a few hours.

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Research interests/expertise

The application of Computational, including Artificial Intelligence techniques to Media. In particular, Machine Learning techniques for automatic musical synthesiser programming.

Areas of teaching

  • Audio Recording and Production
  • Computer Programming for Music
  • Internet Radio Media and Radio Delivery
  • Multimedia Systems

Qualifications

Doctor of Engineering. MSc in Computer Science (1st class hons), BSc in Computer Science.

Courses taught

  • BSc in Music Technology
  • BSc Audio Recording Technology
  • BSc in Media Production
  • BSc in Radio Production
  • MSc in Media Production.