الفهرس 
Business process improvement using business analytics
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Abstract
Nowadays, manufacturers are shifting from a traditional product-centric business
paradigm to a service-centric one by offering not only products but products accompanied by
services, which is known as Product-Service Systems (PSSs). PSS mass customization entails
configuring products with varying degrees of differentiation to meet the needs of various
customers. This is combined with service customization, in which configured products are
expanded by customers to include smart IoT devices (e.g., sensors) to improve product usage
and facilitate the transition to smart connected products.
A massive amount of data is collected along the PSS customization lifecycle starting from
the early stages of smart product ideation and customization to smart product monitoring and
improvement. This data is useless and invaluable unless it is used to generate more information
and gain insights. Moreover, this massive data overload issue hinders the various stakeholders
involved in the PSS customization lifecycle from making informed decisions. However, PSSs
do not support the analysis of this collected data to enhance data-driven decision-making.
This creates a demand for the adoption of novel techniques/approaches to assist all the
involved stakeholders in making informed decisions and accelerating the different PSS
customization lifecycle processes. We anticipate that data analytics techniques can be utilized
to analyze the massive amounts of data collected during the PSS customization lifecycle. Data
analytics techniques are classified into three categories: descriptive, predictive, and
prescriptive. Recommender Systems (RSs) fall under the bigger class of prescriptive analytics,
which represent software tools that offer better suggestions to customers, taking into account
their requirements/preferences. We anticipate that RSs could play a pivotal role in the different
processes of the PSS customization lifecycle (i.e., smart product ideation, PSS customization,
production planning, production execution, and production monitoring) by assisting various
involved stakeholders in making informed right decisions.
Accordingly, in this thesis, a recommendation framework is proposed to support the
different processes of the PSS customization lifecycle. In this framework, a set of
recommendation capabilities are identified to support and accelerate the different processes of
the PSS customization lifecycle while accommodating different stakeholders’ perspectives.
Then, we concentrated our efforts on addressing the main problems identified for the smart
product ideation and the customization of services as part of the PSS customization lifecycle,
which are as follows, (i) in the smart product ideation process, customers may start their customization process by selecting a PSS variant from a wide range of available previously
customized PSS variants rather than doing customization from scratch. Consequently, finding
a PSS variant that is precisely aligned to the customers’ requirements is a cognitive task that
the customers are unable to manage easily; and (ii) during the service customization process,
customers are interested in expanding existing configured products to include smart sensors or
IoT communication devices in general, to improve product usage and facilitate the transition
to smart connected products. Despite the significant gained value from adding sensors to
products, the selection of the appropriate types of sensors and their adequate locations is a
challenge that customers are unable to manage easily and effectively.
These problems are addressed by proposing recommendation approaches that assist
customers in making informed decisions during the previously mentioned two processes. For
the smart product ideation process, we propose a hybrid knowledge-based recommendation
approach that assists customers in selecting a previously customized PSS variant that is
accurately aligned to their requirements from a wide range of available ones. The proposed
approach models the problem of selecting previously customized PSS variants as a Constraint
Satisfaction Problem (CSP), to filter out PSS variants that do not satisfy customers’ needs.
After that, a weighted utility function is applied to rank the remaining PSS variants based on
their utility to the customer. The utility and applicability of the proposed recommendation
approach for the smart product ideation process is demonstrated through its application on a
real-life case study in the domain of laser machines. Moreover, the proposed approach is
evaluated through feedback from industrial experts. The evaluation results show the
effectiveness, utility, efficiency, and persuasiveness of our proposed approach.
In addition, for the customization of services process, we propose a data warehouse-based
recommendation approach that assists customers in selecting the appropriate types of smart
devices (e.g., sensors) to install on their configured products and their adequate locations. This
approach collects and analyzes usage incident data generated during the usage phase of similar
products to the one that the target customer wishes to expand by adding smart sensors. The
analysis of this data helps in identifying the most critical parts with the highest number of
incidents, the causes of these incidents, and the neighboring influential parts that are
responsible for the occurrence of these incidents that occurred on those critical parts. As a
result, these critical parts are suggested to the target customer as the most important parts to where sensors should be installed in her current product. A real-life case study in the domain of milling machines is used to demonstrate the utility
and applicability of the proposed approach for the customization of services process. Moreover,
the performance of the proposed approach is evaluated in terms of response time. The
evaluation results show that our proposed approach is able to generate recommendations within
the recommended system response time boundaries.