Because of changing customer requirements and increasing introduction of new products, the retailer¡¦s inventory has to be reduced. In addition, the supplier is facing the problem of more frequent delivery in small batch. All these give rise to the demand for Distribution center¡]DC¡^. In addition to the study of logistics facilities, logistics information system¡]LIS¡^ plays an important role to improve the efficiency of physical distribution. Various market environments require different kinds of DC. The services and operational processes provide in each DC will depend on the orientation of the DC. The differences are to be reflected in the LIS requirements. The findings of this research indicated that both scale and organizational form have contributed to the variation in LIS. The DC that is large in size is often the logistics department of a big company; furthermore, the DC that is small in size is often a third party logistics company. Hence small DCs, while concentrating on the logistics function, do no need the function of purchasing. Large DCs have better performance record in using database, electronic bulletin board system, telephone conference, and video conference. By comparison, small DCs have better use of common distribution network and common information system. In general, problematic internal and external integration, and lack of flexibility are prevalent phenomena in the implementation of LIS. Suggestions could be made that integration with other type of information system is to be emphasized for large DCs. Small DCs, while falling short of resources, will need to form alliance with their partners, which can provide better information resources for daily operations. The user interface of LIS had a significant impact on user satisfaction, and user satisfaction in turn affected the competitive advantage of DC. This indicated that both user interface and user satisfaction should be highlighted in designing LIS. A LIS should be directed to meet user friendly purpose and strategic position in the market, which will exert the system function fully and achieve competitive advantage for the company.

<div>There has been a great interest in applying Data Science, Machine Learning, and AI-related technologies in recent years. Industries are adopting these technologies very rapidly, which has enabled them to gather valuable data about their businesses. One such industry that can leverage this data to improve their business's output and quality is the logistics and transport industry. This phenomenon provides an excellent opportunity for companies who rely heavily on air transportation to leverage this data to gain valuable insights and improve their business operations. This thesis is aimed to leverage this data to develop techniques to model complex business processes and design a machine learning-based predictive analytical approach to predict process violations.</div><div>This thesis focused on solving delays in shipment delivery by modeling a prediction technique to predict these delays. The approach presented here was based on real airfreight shipping data, which follows the International Air and Transport Association industry standard for airfreight transportation, to identify shipments at risk of being delayed. By leveraging the shipment process structure, this research presented a new approach that solved the complex event-driven structure of airfreight data that made it difficult to model for predictive analytics.</div><div>By applying different data mining and machine learning techniques, prediction techniques were developed to predict delays in delivering airfreight shipments. The prediction techniques were based on random forest and gradient boosting algorithms. To compare and select the best model, the prediction results were interpreted in the form of six confusion matrix-based performance metrics. The results showed that all the predictors had a high specificity of over 90%, but the sensitivity was low, under 44%. Accuracy was observed to be over 75%, and a geometric mean was between 58% – 64%.</div><div>The performance metrics results provided evidence that our approach could be implemented to develop a prediction technique to model complex business processes. Additionally, an early prediction method was designed to test predictors' performance if complete process information was not available. This proposed method delivered compelling evidence suggesting that early prediction can be achieved without compromising the predictor’s performance.</div>

This diploma work analyses order to cash process in the company which core business lies within steel construction zinc dipping, preparation of technical proposals and providing of services in transportation and manual works. This thesis includes proposals for process optimization with aim to select new IT system which leads to cost reduction and eventually to the enhancement of company’s effectiveness.