Using Deep Learning to Predict Back Orders : A study in the Volvo Group Aftermarket Supply Chain
Abstract: The aftermarket holds a vital role in the Volvo Group value offer. Producing profitability by satisfying the customers needs for important spare parts, ensuring maximum uptime for the entire range of vehicles produced and sold. As the cost for keeping stock exponentially increases with a higher availability, the availability can never be 100%. This in effect means that there will be occasions where an order is placed on a part that is currently not in stock, creating a back order. And while not all of these back orders can be avoided completely, predicting them before they occur will allow for preemptive measures to be taken, potentially reducing lead times and costs. Deep learning is a sub-section of machine learning, the study of methods to make computers find complex patterns in data. Deep learning has had an increase in popularity as the computational power and available data has greatly increased in recent years and is something that Volvo sees potential in. This creates the aim of this study which is to develop a deep learning model to predict the occurrence of back orders. In order to fulfill this aim, two main research questions were formed. The first research question intends to find underlying causes and factors that can explain the occurrence of back orders, in order to create the input features that the model can be trained on. This was initiated with a basis in literature, where a theoretical framework was created from different areas in the field of logistics as well as previous studies that combine logistics and machine learning. After this an empirical study was conducted where four previous initiatives from Volvo were found, that aim to explain the occurrence of back orders. As this was concluded, the findings were combined and synthesized into a list of factors that explain the underlying causes of back orders. In the second research question the factors listed were translated into input features of the model, where all quantifiable factors that could be and located in the Volvo database were included. This created the data set used to train the deep learning model to predict back orders. After the feature creation was completed, the actual design and development of the model could commence. Based on literature concerning deep learning along with directives from Volvo, a deep recurrent neural network was developed. The exact size and shape of the model was varied and evaluated to find the best performance. Evaluating the results showed several interesting findings. After training the model on one year of weekly data for 20 000 part numbers, the model proved to be skillful in predicting the occurrence of back orders. The model was able to predict 73% of back orders one week before they occurred (recall), and 72% of what the model deemed to be back orders were actual back orders (precision). The main challenges with predicting back orders were the imbalance between back order and a non-back order and the limit of one year of data. As the nature of back orders is that on average, only a few weeks per year will there be a back order on a given part, the training of the model becomes difficult. The difficulty with this imbalance is that the model is always less likely to predict a back order if the occurrence of back order itself is rare. The advantage of deep learning can be found with a large amount of data, and not being limited to one year of data is likely to produce better results. Despite these difficulties the model was highly successful in predicting the occurrence of back orders.
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