Browsing by Author "Droomer, Marli"

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    Development of a low cost machine vision based quality control system for a learning factory
    (Elsevier, 2019) Louw, Louis; Droomer, Marli
    ENGLISH ABSTRACT: Learning Factories provide a promising environment for developing the competencies required from a future workforce to apply and integrate technologies associated with digitalised production environments and cyber-physical systems. This paper describes a student project for the development and implementation of a low cost machine vision based quality control system within a Learning Factory. A prototype system was developed using low cost hardware and open source software freely available. The system will be used towards further research and development of more intelligent manufacturing systems within the Learning Factory, based on machine vision. A second benefit was student competency development through self-learning and experimentation. It serves to illustrate how the education as well as research goals of a Learning Factory can be addressed simultaneously through student projects.
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    Predicting the next purchase date for an individual customer using machine learning
    (Stellenbosch : Stellenbosch University, 2020-12) Droomer, Marli; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: We live in a world that is rapidly changing when it comes to technology. Gatheringa customer’s information becomes easier as companies have loyalty programs thattrack the customer’s purchasing behaviour. We live in an era where search enginessuggest your next word, online shopping is no longer scary, and people order aride by means of an application. The fact is that technology is evolving, andgathering information from customers is becoming easier. Given this change,the questions, however, are: How do companies use this information to gain acompetitive advantage? Do they use this information to benefit the customer?How can a company use customer information to give each individual a uniqueexperience?A research study was conducted to determine if an individual customer’s nextpurchase date for specific products can be predicted by means of machine learning.The focus was on fast-moving consumer goods in retail. This next purchase date canthen be used to individualise marketing to customers, which benefits the companyand the customer. In this study, the customer’s purchase history is used to train AbstractWe live in a world that is rapidly changing when it comes to technology. Gatheringa customer’s information becomes easier as companies have loyalty programs thattrack the customer’s purchasing behaviour. We live in an era where search enginessuggest your next word, online shopping is no longer scary, and people order aride by means of an application. The fact is that technology is evolving, andgathering information from customers is becoming easier. Given this change,the questions, however, are: How do companies use this information to gain acompetitive advantage? Do they use this information to benefit the customer?How can a company use customer information to give each individual a uniqueexperience?A research study was conducted to determine if an individual customer’s nextpurchase date for specific products can be predicted by means of machine learning.The focus was on fast-moving consumer goods in retail. This next purchase date canthen be used to individualise marketing to customers, which benefits the companyand the customer. In this study, the customer’s purchase history is used to trainmachine learning models. These models are then used to predict the next purchasedate for a customer-product pair. The different machine learning models that areused are recurrent neural networks, linear regression, extreme gradient boostingand an artificial neural network. Combination approaches are also investigated, andthe models are compared by the absolute error, in days, that the model predictsfrom the target variable.The artificial neural network model performed the best, predicting 31.8% of thedataset with an absolute error of less than one day, and 55% of the dataset withan absolute error of less than three days. The application of the artificial neuralnetwork as the Next Purchase Date Predictor is also demonstrated and shows howindividualised marketing can be done using the Next Purchase Date Predictor.The encouraging results of the Next Purchase Date Predictor showed that machinelearning could be used to predict the next purchase date for an individual customer.
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    Using machine learning to predict the next purchase date for an individual retail customer
    (Southern African Institute for Industrial Engineering, 2020-11-11) Droomer, Marli; Bekker, James
    ENGLISH ABSTRACT: Targeted marketing has become more popular over the last few years, and knowing when a customer will require a product can be of enormous value to a company. However, predicting this is a difficult task. This paper reports on a study that investigates predicting when a customer will buy fast-moving retail products, by using machine learning techniques. This is done by analysing the purchase history of a customer at participating retailers. These predictions will be used to personalise discount offers to customers when they are about to purchase items. Such offers will be delivered on the mobile devices of participating customers and, ultimately, physical, general paper-based marketing will be reduced.