A methodology for direct exploitation of available information in the online model-based redesign of experiments

Online model-based design of experiments techniques were proposed to exploit the progressive increase of the information resulting from the running experiment, but they currently exhibit some limitations: the redesign time points are chosen “a-priori” and the first design may be heavily affected by the initial parametric mismatch. In order to face such issues an information driven redesign optimisation (IDRO) strategy is here proposed: a robust approach is adopted and a new design criterion based on the maximisation of a target profile of dynamic information is introduced. The methodology allows determining when to redesign the experiment in an automatic way, thus guaranteeing that an acceptable increase in the information content has been achieved before proceeding with the intermediate estimation of the parameters and the subsequent redesign of the experiment. The effectiveness of the new experiment design technique is demonstrated through two simulated case studies

Informatisation of transport process using GPS navigation system

Nowadays, internal business processes are one of the crucial factors of firms’ competitive advantage achievement, which is becoming increasingly important, not only on local but also on global markets. Firms’ performance improvement depends to a large extent on its flexibility, adjustability and especially ability to manage internal business processes. Hence, use of information technology is among the fundamental elements of business process change, which not only improve competitiveness but also assure long term development and growth of the firm. For all these reasons, the main purpose of this article is firstly to establish that use of information technology is feasible in all industries, explain what possible obstacles for successful informatisation are, and which are major advantages after its implementation. Secondly, within theoretical aspect we present broaden view of informatisation in logistics, whilst practical example illustrates successful informatisation of transport process with adoption of GPS (Global Positioning System) in a transport firm. Furthermore, in this article we are also introducing a case study of three transport firms describing process flow before and after implementation identifying enhanced information quality, service quality and communication as a foremost advantages and achievements after informatisatio

Toward a process theory of entrepreneurship: revisiting opportunity identification and entrepreneurial actions

This dissertation studies the early development of new ventures and small business and the entrepreneurship process from initial ideas to viable ventures. I unpack the micro-foundations of entrepreneurial actions and new ventures’ investor communications through quality signals to finance their growth path. This dissertation includes two qualitative papers and one quantitative study. The qualitative papers employ an inductive multiple-case approach and include seven medical equipment manufacturers (new ventures) in a nascent market context (the mobile health industry) across six U.S. states and a secondary data analysis to understand the emergence of opportunities and the early development of new ventures. The quantitative research chapter includes 770 IPOs in the manufacturing industries in the U.S. and investigates the legitimation strategies of young ventures to gain resources from targeted resource-holders.Open Acces

Six Sigma vs. Design for Six Sigma: selection of the requisite Six Sigma approach using multi-criteria decision analysis: innovation report

The literature suggests that organisations which have adopted Six Sigma have realised that upon achieving a Five Sigma level the only way to surpass this is to redesign the process(es) by means of Design for Six Sigma (DFSS). For others, the selection of Six Sigma over the DFSS approach is not a definitive question and just a guideline can be provided. A major objective of this research was to extend the selection of the requisite Six Sigma approach beyond the sigma level case and the general guidelines, towards a multi-criteria decision using established techniques. Thus, two research questions were defined: what influences the selection of the requisite Six Sigma approach, i. e. Six Sigma versus DFSS? and, how effective is the use of Multi-Criteria Decision Analysis (MCDA) techniques in the selection of the requisite Six Sigma approach? An action research methodology was applied where one Six Sigma project, one DFSS project and one Six Sigma project applied in a non-manufacturing process were implemented and analysed in collaboration with 3M Corporation, General Domestic Appliances (GDA) and Land Rover. From the action research spiral it was concluded that the sigma level has a positive association with the selection of redesign or improvement efforts within Six Sigma, however the Five Sigma level cannot necessarily dictate the use of one approach over the other. Besides the sigma level the selection of the requisite Six Sigma approach is influenced by multiple and conflicting criteria. In addition, the selection can occur at different stages of the methodologies. To assist decision-makers in organising, synthesising and optimising the criteria affecting this decision, the Stochastic Analytic Hierarchy Process (SAHP) was developed and applied to the problem at hand. The SAHP was developed on the basis of Analytic Hierarchy Process (AHP) and disparate sources of relevant literature. SAHP provides a mechanism for achieving a more effective selection of the requisite Six Sigma approach in the form of considering multiple and conflicting criteria using quantitative and qualitative information under uncertainty. In contrast to the traditional AHP, SAHP incorporates probabilistic distributions to incorporate uncertainty that people have in converging into a Likert scale their judgments of preferences. The vector of priorities is calculated using Monte Carlo simulation and the final rankings analysed for rank reversal using statistical analysis with managerial aspects introduced systematically. The concept and implementation of SAHP is new to the selection of the requisite Six Sigma approach and as such it constitutes the main innovation to result from this research. It extends the selection of the requisite Six Sigma approach towards a systematic multi-criteria decision considering multiple and conflicting criteria under uncertainty. Furthermore, while SAHP was originally conceived as a specific aid to the improve or redesign issue within Six Sigma, this research indicates that it is potentially much more widely applicable. This research also provides evidence of how different factors affecting the selection of requisite Six Sigma approach were considered. Further areas of research include the use of a positivist method in order to increase the sample size of the research and identify different factors affecting the decision improve or redesign. The development of SAHP software and extending the SAHP practice to different multi-criteria decisions are also potential areas for further research

Business process improvement with the AB-BPM methodology

A fundamental assumption of Business Process Management (BPM) is that redesign delivers refined and improved versions of business processes. This assumption, however, does not necessarily hold, and any required compensatory action may be delayed until a new round in the BPM life-cycle completes. Current approaches to process redesign face this problem in one way or another, which makes rapid process improvement a central research problem of BPM today. In this paper, we address this problem by integrating concepts from process execution with ideas from DevOps. More specifically, we develop a methodology called AB-BPM that offers process improvement validation in two phases: simulation and AB tests. Our simulation technique extracts decision probabilities and metrics from the event log of an existing process version and generates traces for the new process version based on this knowledge. The results of simulation guide us towards AB testing where two versions (A and B) are operational in parallel and any new process instance is routed to one of them. The routing decision is made at runtime on the basis of the achieved results for the registered performance metrics of each version. Our routing algorithm provides for ultimate convergence towards the best performing version, no matter if it is the old or the new version. We demonstrate the efficacy of our methodology and techniques by conducting an extensive evaluation based on both synthetic and real-life data

Ranking of Distribution System's Redesign Scenarios Using Stochastic MCDM/A Procedure

AbstractThe paper presents the original procedure of solving a multiple criteria stochastic ranking problem consisting in the evaluation of different variants of the distribution system. The problem originates from the analysis and construction of the redesign scenarios of the existing distribution system. The authors develop a computational procedure being a combination of a traditional – deterministic multiple criteria ranking method (e.g. Electre III/IV) and a classification algorithm (e.g. Bayes classifier). The proposed method is composed of six steps, including: stochastic data collection, random selection of deterministic numbers using simulation technique, solving a multiple criteria ranking problem with an application of a deterministic multiple criteria decision aiding/making (MCDM/A) method, the classification of deterministic relations between redesign scenarios (variants) to predefined classes using classification algorithm, the construction of a final ranking of redesign scenarios with an application of a spreadsheet, the recommendation of the compromise solution based on stochastic final ranking of redesign scenarios. The proposed approach is verified on the real-world analysis of the distribution system of goods which operates at the Polish electro-technical market. The results of computational experiments, including: ranking generation, classification and sensitivity analysis are demonstrated. The analysts’ final recommendation of the compromise solution selection is presented. It is based on a comprehensive analysis of the current state of the system, the perspectives of its development, decision maker's preferences, results of the computational experiments and sensitivity analysis

Transit Route Planning to Improve Accessibility: A Reinforcement Learning Approach

Public transport has a key role in social sustainability by reducing social isolation and improving access to essential opportunities. The conventional approaches to transit network and service planning do not consider the social role of public transport, and attempt to maximise the cost savings of users and the operational efficiency of operators. Cost-based network design tends to allocate more transit routes and/or services to high population density areas. This can create transit service gaps between high and low population density areas and leave some people poorly served with low accessibility. This study proposed a new algorithm for transit network design by incorporating a transit accessibility measure in the routing optimisation. Transit accessibility refers to the ease of reaching opportunities from a location by public transport. The proposed approach aims to improve the transit connectivity between residential zones and major activity centres, which contain a range of opportunities. This study also developed a transit need index and integrated it into route planning to account for the socio-demographic characteristics of potential transit users in the network design to benefit those who need the transit service to reach essential opportunities. This index is formulated by combining selected socio-economic and demographic variables and then integrating them with the transit accessibility measure as the optimisation objective. This study developed a reinforcement learning method for the purpose of the bus network and service planning. This study used reinforcement learning to address the complicated interactions of associated features in bus network and service planning. Reinforcement learning methods achieve a complex goal or optimise long-term performance over many experiences. As reinforcement learning can work in changing environments, reinforcement learning methods effectively solve sequential decision-making problems such as bus route and service planning. The reinforcement learning method was examined to validate the effectiveness of adapting the transit accessibility measure in transit route planning, with and without the transit need index, on the existing bus network in a case study area. The existing bus network in the case study area of Penrith local government area in western Sydney, Australia, has 56 bus routes and 1,940 stops, which provides a realistic and challenging test environment. The performance results indicate that the proposed algorithm, which adopts the measure of transit accessibility combined with the transit need index as an optimisation objective, can both improve overall transit accessibility and provide an appropriate level of service across areas

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field