Fraunhofer Chalmers Research Centre for Industrial Mathematics
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What We Lost - Searching through History after a Lost Connection to the Natural Environment
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Reaching Social Sustainability in Residential Architecture - An Investigation of Coliving Communities, the Housing Sector, and their Contributions to Social Sustainability
No full textPredicting Deviation in Supplier Lead Time and Truck Arrival Time Using Machine Learning - A Data Mining Project at Volvo Group
No full textThe deviation in delivery performance from a company’s suppliers directly affects
the company’s performance, causing availability loss for the customer orders and
large costs for the rush transportation. If the deviation can be predicted in advance
and used as deviation alerts, actions can be taken in advance either to prevent the
deviation or decrease the impact of the deviation.
To predict the deviation in the supplier delivery performance from a buying company’s
point of view, this thesis work specifically focuses on the first two phases of
a supply chain, namely supplier lead time from material suppliers and truck arrival
time from logistics service providers (LSP). In order to examine the possible implementation
of machine learning, a data mining project has been conducted at Volvo
Group Service Market Logistics. The factors associated with deviation of supplier
lead time and truck arrival time are identified, while the corresponding features
are prepared under the constraint of the case company’s data availability. For predicting
deviation in the two phases, two machine learning models are constructed
accordingly based on the characteristics of output and input features. The opportunities
and obstacles along the data mining process in the case company are identified.
The results show currently in the case company, both generated machine learning
models do not have enough predictive power in lead time deviation. This could
be caused by the absence of some key features that have strong associations with
deviation. However, the performance of the prediction model for truck arrival time
is regarded to be improved to a deployable level when the desired features are constructed
into the model by the case company. Future recommendations regarding
constructing the desired features and improving the model performance are proposed.
In comparison, predicting deviation in material suppliers’ lead time could
be practical when the buying company get more information sharing from material
suppliers