Using a coach to improve team performance when the team uses a Kanban process methodology

Teams are increasing their use of the Kanban process methodology across a range of information system projects, including software development and data science projects. While the use of Kanban is growing, little has been done to explore how to improve team performance for teams that use Kanban. One possibility is to introduce a Kanban Coach (KC). This work reports on exploring the use of a Kanban Coach, with respect to both how the coach could interact with the team as well as how the use of a coach impacts team results. Specifically, this paper reports on an experiment where teams either had, or did not have, a Kanban Coach. A quantitative and qualitative analysis of the data collected during the experiment found that introducing KC led to significant improvement of team performance. Coordination Theory and Shared Mental Models were then employed to provide an explanation as to why a KC leads to better project results. While this experiment was done within a data science project context, the results are likely applicable across a range of information system projects

Using a coach to improve team performance when the team uses a Kanban process methodology

Teams are increasing their use of the Kanban process methodology across a range of information system projects, including software development and data science projects. While the use of Kanban is growing, little has been done to explore how to improve team performance for teams that use Kanban. One possibility is to introduce a Kanban Coach (KC). This work reports on exploring the use of a Kanban Coach, with respect to both how the coach could interact with the team as well as how the use of a coach impacts team results. Specifically, this paper reports on an experiment where teams either had, or did not have, a Kanban Coach. A quantitative and qualitative analysis of the data collected during the experiment found that introducing KC led to significant improvement of team performance. Coordination Theory and Shared Mental Models were then employed to provide an explanation as to why a KC leads to better project results. While this experiment was done within a data science project context, the results are likely applicable across a range of information system projects

Comparing Data Science Project Management Methodologies via a Controlled Experiment

Data Science is an emerging field with a significant research focus on improving the techniques available to analyze data. However, there has been much less focus on how people should work together on a data science project. In this paper, we report on the results of an experiment comparing four different methodologies to manage and coordinate a data science project. We first introduce a model to compare different project management methodologies and then report on the results of our experiment. The results from our experiment demonstrate that there are significant differences based on the methodology used, with an Agile Kanban methodology being the most effective and surprisingly, an Agile Scrum methodology being the least effective

Beyond lean manufacturing: the productivity, innovator's and proactivity dilemmas resolved

This dissertation provides direction for the management of exploration in an exploitative context by specifying the theory for a universal model of ambidexterity. Research in ambidexterity centres upon how exploration for the future and exploitation of the present can be achieved simultaneously through the management of innovation. Ambidexterity theory strives to resolve the Productivity and Innovator’s Dilemmas, which assert collectively that exploration is inherently antagonistic to exploitation. The Productivity Dilemma asserts that the organisation and routinisation of processes required for efficient exploitation are incompatible with the flexibility required for exploration. The Innovator’s Dilemma asserts that a focus on exploitation through incremental innovation in a stable environment inhibits exploratory innovation, which leaves an enterprise vulnerable to obsolescence from disruptive innovation. Whilst ambidexterity is an issue that dominates in the literature for innovation management and manufacturing systems, the theory for a unifying framework that reconciles competing approaches is not reported. Moreover, the methods and tools for the execution of ambidexterity require significant development. The candidate contends in this dissertation that the ambidexterity issue is epitomised by Toyota’s announcement in 2007 of its intent to implement transformational innovation (kakushin) in a controlled and historically consistent environment. Toyota is known for its system of “Lean Manufacturing”, which is regarded widely for its high productivity and institutionalised continuous improvement (kaizen). This dissertation gives a new perspective on Lean Manufacturing by its critical evaluation through an interdisciplinary framework of innovation, economic and behavioural criteria. Lean Manufacturing is de-constructed and shown to be a systematic evolution from ordered antecedents, which represent an exploration-exploitation continuum that can be used to reconcile the competing approaches towards ambidexterity. Furthermore, a third dilemma is presented by this dissertation, which acts in concert with the Productivity and Innovator’s Dilemmas and is named by the candidate the “Proactivity Dilemma”. The Proactivity Dilemma asserts that exploratory behaviour is perceived increasingly non-proactive as proactivity in exploitation increases. The candidate uses the insights from their new perspective on Lean Manufacturing to specify the theory for a universal model of ambidexterity. The candidate’s model of ambidexterity encompasses nine core organisational processes, which are categorised by Operations Management, Product Development and Strategic Planning. This dissertation provides comprehensive direction for the simultaneous management of productivity and innovation, from “boardroom” strategy to “shopfloor” tactics

Hyperreality as a Concept of the Metanarrative Social

Концепт гиперреальности позволяет задать мета-нарративную структурную соположенность «точек зрения», которые образуют виртуальную цифровую реальность. Гиперреальность состоит из описательных реальностей или субъективных «точек зрения». Метанарратив — это связность, соединяющая их в целое посредством мышления, явленного в языке и смысле. «Точки зрения» промысливают и переписывают имеющееся дискурсивное социальное посредством конструирования новых смыслов, постоянно трансформационно расширяя гиперреальность социального существования.The concept of hyperreality let you to specify a metanarrative structural co-ordination of “points of view” that form a new virtual digital reality. Hyperreality is composed of descriptive realities or subjective “points of view”. The metanarrative is the connectivity that connects them into a whole, through thinking, manifested in language and meaning. “Points of view” contemplate and rewrite the existing discursive social through the construction of new meanings, constantly transforming the expansion of the hyper-reality of social existence

Managing traffic and pedestrian flows at an intersection: simulation model parameters

The article highlights and justifies the input parameters for the simulation model of an intersection. The purpose of building the model is to predict and evaluate the results for reducing traffic and pedestrian downtime. The object of the study is an intersection located in the Pokrovskoe-Streshnevo area of the Northwest District of Moscow (55°49'35.9 "N37°27'08.0 "E). The authors structured and analyzed traffic flows passing through the intersection. The frequency of public transport, the location of nearby stops of such transport, and the phases of traffic lights were determined. Also, the parameters for the simulation scenarios are defined

Improving Data Science Team Performance via the Use of a Kanban Process Framework that has Enhanced Mentoring, Coaching and Metrics Utilization

The amount of data generated by organizations and systems is growing exponentially. At the same time, organizations are increasing their efforts to generate knowledge and useful insights that might be hidden within their data. This is driving tremendous growth in teams doing data science and the related field of big data. However, despite the fact that data science is now widely used in many organizations, most data science teams do not follow any specific methodology of how to organize, collaborate and structure the team’s work. Kanban is one of the potential methodologies that can be used to help a data science team improve their coordination and effectiveness. The use of Kanban is increasing across a range of information system projects, including software development and data science. While the use of Kanban is growing, little has been done to explore how to improve team performance for teams that use Kanban. One possibility is to introduce a process coach. Another possibility is to introduce a process master. Yet another possibility is to collect Kanban team metrics (to be able to understand and predict low team performance). This paper reports on an experiment comparing teams using a Kanban Coach (KC), a Kanban Master (KM) or neither a KC nor a KM. Coordination Theory and Shared Mental Models were employed to provide an explanation as to why a KC or a KM might lead to better project results. It was found that introducing KC or a KM led to significant improvement of team performance in comparison to the baseline case, with the KC being better than the KM with respect to how teams used Kanban. This dissertation also explored, for teams using Kanban, the ability to predict low team performance via an analytical model that uses specific project metrics that can be collected via the team’s visual Kanban board. The models developed, via the analysis of 80 data science teams using Kanban, were significantly better than the baseline situation of thinking that all teams were at risk. Finally, note that while this research was done within a data science project context, the results are likely applicable across a range of information system projects that use Kanban

Core-Periphery Communication and the Success of Free/Libre Open Source Software Projects

Part 1: Full PapersInternational audienceWe examine the relationship between communications by core and peripheral members and Free/Libre Open Source Software project success. The study uses data from 74 projects in the Apache Software Foundation Incubator. We conceptualize project success in terms of success building a community, as assessed by graduation from the Incubator. We compare successful and unsuccessful projects on volume of communication by core (committer) and peripheral community members and on use of inclusive pronouns as an indication of efforts to create intimacy among team members. An innovation of the paper is that use of inclusive pronouns is measured using natural language processing techniques. We find that core and peripheral members differ in their volume of contribution and in their use of inclusive pronouns, and that volume of communication is related to project success