A Comparison of Digital and Analogue Visual Management Communication Boards
Bibliographic record
Abstract
The use of Visual Management is well established in Operations Management(Galsworth 2017 and Greif1991)and most current practice utilises communications boards based on analogue media,such aspaper charts or white boards (Bateman and Lethbridge 2013).These types of boards are used for team briefings and can managecapacity and flow in the form of kanbans (or scrum boards)or a range of performance measures.There is a strong drive to move over to digital versionsof these boards,particularly in some sectors such as constructionwith initiatives such BIM 2 (Building Information Modelling Gov.uk 2020). This digital impetus can be driven by vendors of equipment and software,but also by sector innovators wishing to modernise, such as construction. The drivers to move to digital systems are likely to increase given the focus on AI and big data. Current visual management literature in operations provides support for either an analogue (Galsworth 1997and Greif 1991)or a digital approach (Few 2006),but crucially,a critical analysis comparing both approaches has not been published. This paper sets out to lay the foundations to analyse and test the benefits of each approach byexploringfindings from observations of the use of digital tools and discussion with practitioners. In addition,three pilot trialswere conductedwhich weredesigned to compare a digital approach to visual managementwith analogue tools.The academic literature for Visual Management is fairly limited -notable papers are Bateman, Philp, and Warrender, (2016) and Beynon-Davies and Lederman, (2017). Bateman et al.(2016) proposes four visual management principles and documents their use in an action research paper. These principles go on to be tested in Gardner and Bateman (2017). Beynon-Davies and Lederman, (2017)frame an analysis of visual tools in terms of affordance theory, considering first and second order affordances of communication and purpose. Usefully for this research,Beynon-Davies and Lederman also considersthe limitations and benefits conferred by the physicality of the communication medium such as whiteboards. Bresciani, Blackwell and Eppler (2008) providea useful framework for analysing team interactions with visual toolsand Sibbet, (2008) a thoughtful synthesis of visual tools in practice.In terms of analysing the use of technology and its role played in decision-making,the technology adoption model (TAM) used Brandon-Jones and Kauppi, (2018) provides a useful framework. [Intoroduction]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".