An Ideation Framework for Service Process Improvement

2012 ◽  
pp. 191-227
Author(s):  
Maya Kaner ◽  
Reuven Karni
Keyword(s):  
Service Process ◽  
Case Based Reasoning ◽  
Process Redesign ◽  
Crucial Step ◽  
Road Map ◽  
Current State ◽  
Successful Design ◽  
New Ideas ◽  
Improvement Methodologies ◽  
Case Based

Service delivery processes play a key role in the competitiveness of modern organizations. Their effectiveness and efficiency are a consequence of successful design of new processes and improvement of existing processes. Improvement methodologies commonly focus on generic steps serving as a road map for moving a process from its current state along a guided path to better performance. However, these methodologies ignore the crucial step of methods for modifying processes, which often necessitate the generation of new improvement alternatives; generally based on “randomized” brainstorming rather than on systematic triggering of new ideas and reusing past improvements. The authors’ framework comprises and integrates 21 goal determinants to be achieved through process redesign, 32 best practices describing possible process modifications, 40 TRIZ inventive principles for generating new improvement ideas, and case-based reasoning (CBR) for retaining and reusing past improvements. This paper illustrates the application of the proposed methodology using an example of an inbound telesales process.

An Ideation Framework for Service Process Improvement

2010 ◽  
Vol 1 (3) ◽  
pp. 17-49 ◽  
Author(s):  
Maya Kaner ◽  
Reuven Karni
Keyword(s):  
Best Practices ◽  
Process Improvement ◽  
Case Based Reasoning ◽  
Process Redesign ◽  
Road Map ◽  
Current State ◽  
Successful Design ◽  
New Ideas ◽  
Effectiveness And Efficiency ◽  
Improvement Methodologies

Service delivery processes play a key role in the competitiveness of modern organizations. Their effectiveness and efficiency are a consequence of successful design of new processes and improvement of existing processes. Improvement methodologies commonly focus on generic steps serving as a road map for moving a process from its current state along a guided path to better performance. However, these methodologies ignore the crucial step of methods for modifying processes, which often necessitate the generation of new improvement alternatives; generally based on “randomized” brainstorming rather than on systematic triggering of new ideas and reusing past improvements. The authors’ framework comprises and integrates 21 goal determinants to be achieved through process redesign, 32 best practices describing possible process modifications, 40 TRIZ inventive principles for generating new improvement ideas, and case-based reasoning (CBR) for retaining and reusing past improvements. This paper illustrates the application of the proposed methodology using an example of an inbound telesales process.

scholarly journals Investigating the Case-Based Reasoning Process During HCI Design

2015 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
José Antonio Gonçalves Motta ◽  
Simone Diniz Junqueira Barbosa
Keyword(s):  
Interaction Design ◽  
Human Interaction ◽  
Design Knowledge ◽  
Design Activity ◽  
Case Based Reasoning ◽  
Access Methods ◽  
Reflective Process ◽  
New Ideas ◽  
Previous Design ◽  
Case Based

Given that the design activity makes use of previous design knowledge, we turned to case-based reasoning (CBR) to help identify opportunities to support the design of human-computer interaction (HCI). Using interviews with professional designers and Semiotic Engineering, we developed a CBR tool called CHIDeK (Computer-Human Interaction Design Knowledge), with which we conducted a study to observe how it influenced the HCI design activity. We found that the cases recorded in CHIDeK supported design by motivating the designers’ reflective process, triggering their memories of experiences with similar systems, and helping to generate new ideas. We have also identified limitations in our case representation and case access methods, which offer opportunities for further research.

scholarly journals The 2016 Computational Analogy Workshop at ICCBR

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 91-92
Author(s):  
Joseph Blass ◽  
Tesca Fitzgerald
Keyword(s):  
Incomplete Information ◽  
Cognitive Processes ◽  
Real World ◽  
Structural Alignment ◽  
Case Based Reasoning ◽  
Related Research ◽  
Research Areas ◽  
New Ideas ◽  
Case Based ◽  
Real World Problems

Computational analogy and case-based reasoning (CBR) are closely related research areas. Both employ prior cases to reason in complex situations with incomplete information. Analogy research often focuses on modeling human cognitive processes, the structural alignment between a base/source and target, and adaptation/abstraction of the analogical source content. While CBR research also deals with alignment and adaptation, the field tends to focus more on retrieval, case-base maintenance, and pragmatic solutions to real-world problems. However, despite their obvious overlap in research goals and approaches, cross communication and collaboration between these areas has been progressively diminishing. CBR and computational analogy researchers stand to benefit greatly from increased exposure to each other's work and greater cross-pollination of ideas. The objective of this workshop is to promote such communication by bringing together researchers from the two areas, to foster new collaborative endeavors, to stimulate new ideas and avoid reinventing old ones.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

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