The application of the 5S methodology to warehouse management represents an important
step for all manufacturing companies, especially for managing products that consist of
a large number of components. Moreover, from a lean production point of view, inventory
management requires a reduction in inventory wastes in terms of costs, quantities and time
of non-added value tasks. Moving towards an Industry 4.0 environment, a deeper understanding
of data provided by production processes and supply chain operations is needed:
the application of Data Mining techniques can provide valuable support in such an objective.
In this context, a procedure aiming at reducing the number and the duration of picking
processes in an Automated Storage and Retrieval System. Association Rule Mining is applied
for reducing time wasted during the storage and retrieval activities of components
and finished products, pursuing the space and material management philosophy expressed
by the 5S methodology. The first step of the proposed procedure requires the evaluation
of the picking frequency for each component. Historical data are analyzed to extract the
association rules describing the sets of components frequently belonging to the same order.
Then, the allocation of items in the Automated Storage and Retrieval System is performed
considering (a) the association degree, i.e., the confidence of the rule, between the components
under analysis and (b) the spatial availability. The main contribution of this work is
the development of a versatile procedure for eliminating time waste in the picking processes
from an AS/RS. A real-life example of a manufacturing company is also presented to explain
the proposed procedure, as well as further research development worthy of investigation.
Power big data contains a lot of information related to equipment fault. The analysis and processing of power big data can realize fault diagnosis. This study mainly analyzed the application of association rules in power big data processing. Firstly, the association rules and the Apriori algorithm were introduced. Then, aiming at the shortage of the Apriori algorithm, an IM-Apriori algorithm was designed, and a simulation experiment was carried out. The results showed that the IM-Apriori algorithm had a significant advantage over the Apriori algorithm in the running time. When the number of transactions was 100 000, the running of the IM-Apriori algorithm was 38.42% faster than that of the Apriori algorithm. The IM-Apriori algorithm was little affected by the value of supportmin. Compared with the Extreme Learning Machine (ELM), the IM-Apriori algorithm had better accuracy. The experimental results show the effectiveness of the IM-Apriori algorithm in fault diagnosis, and it can be further promoted and applied in power grid equipment.
As the delivery of good quality software in time is a very important part of the software development process, it's a very important task to organize this process very accurately. For this, a new method of the searching associative rules were proposed. It is based on the classification of all tasks on three different groups, depending on their difficulty, and after this, searching associative rules among them, which will help to define the time necessary to perform a specific task by the specific developer.