The emergence of digital technology has compelled the retail industry to develop innovative and sustainable business models to predict and respond to consumer behavior. However, most enterprises are crippled with doubt, lacking frameworks and methods for moving forward. This study establishes a five-step decision-making framework for digital transformation in the retail industry and verifies it using real data from convenience stores in Taiwan. Data from residential type and cultural and educational type convenience stores, which together account for 75% of all stores, underwent a one-year simulation analysis according to the following three decision models for promotions: the shelf-life extended scrap model (SES), the fixed remaining duration model (FRD), and the dynamic promotion decision model (DPD). The results indicated that the DPD model reduced scrap in residential type stores by 12.88% and increased profit by 15.43%. In cultural and educational stores, the DPD model reduced scrap by 10.78% and increased profit by 7.63%. The implementation of the DPD model in convenience stores can bring additional revenue to operators, and at the same time address the problem of food waste. With the full use of resources, sustainable operation can be turned into a concrete and feasible management decision-making plan.

Most real estate agents develop new objects by visiting unfamiliar clients, distributing leaflets, or browsing other real estate trading website platforms, whereas consumers often rely on websites to search and compare prices when purchasing real property. In addition to being time consuming, this search process renders it difficult for agents and consumers to understand the status changes of objects. In this study, Python is used to write web crawler and image recognition programs to capture object information from the web pages of real estate agents; perform data screening, arranging, and cleaning; compare the text of real estate object information; as well as integrate and use the convolutional neural network of a deep learning algorithm to implement image recognition. In this study, data are acquired from two business-to-consumer real estate agency networks, i.e., the Sinyi real estate agent and the Yungching real estate agent, and one consumer-to-consumer real estate agency platform, i.e., the, FiveNineOne real estate agent. The results indicate that text mining can reveal the similarities and differences between the objects, list the number of days that the object has been available for sale on the website, and provide the price fluctuations and fluctuation times during the sales period. In addition, 213,325 object amplification images are used as a database for training using deep learning algorithms, and the maximum image recognition accuracy achieved is 95%. The dynamic recommendation system for real estate objects constructed by combining text mining and image recognition systems enables developers in the real estate industry to understand the differences between their commodities and other businesses in approximately 2 min, as well as rapidly determine developable objects via comparison results provided by the system. Meanwhile, consumers require less time in searching and comparing prices after they have understood the commodity dynamic information, thereby allowing them to use the most efficient approach to purchase real estate objects of their interest.

Most digital manufacturing projects follow the software-development process. However, the application of all the characteristics and processes specific to a digital manufacturing project cannot be generalized to all other engineering projects. Therefore, digital manufacturing project teams typically design and apply process principles that can be customized to individual use cases by project members. Tailoring these principles, including developing and referencing them, is a knowledge intensive activity that requires the gradual improvement of development processes. This paper proposes a knowledge-oriented digital manufacturing ontology model and a semantic rule-based tailoring system that can derive tailoring strategies from knowledge ontologies via an inference-rule design and engine. The proposed model and system can assist in concrete project implementations based on software-development experience. A practical example is demonstrated through the 3D modeling digital manufacturing of a hospital kitchen.

The recommendation of the optimal selling rules for any product or service is challenging, owing to the complexity of the customer’s behavior and the competitiveness existing in the telecom retail industry. This study proposes a recommendation model for selling rules that utilizes a hybrid decision-making approach based on K-means and the C5.0 decision tree to analyze the historical sales information of telecom retailers. To evaluate the efficacy of the proposed recommendation model, it was used to analyze original data from a case company. The results indicated that the proposed hybrid decision-making approach resulted in sales content with a high gross profit and high agreement rates. The experimental results show each cluster that can be used to identify rules for the combination of good tariff items in different tariff ranges. Rules for the recommendation of special tariffs are also established to assist salespeople.