pith. sign in

arxiv: 1907.01875 · v1 · pith:NFOT6GACnew · submitted 2019-07-03 · 💻 cs.SE

Industrial DevOps

Wilhelm Hasselbring , S\"oren Henning , Bj\"orn Latte , Armin M\"obius , Thomas Richter , Stefan Schalk , Maik Wojcieszak This is my paper

Pith reviewed 2026-05-25 10:09 UTC · model grok-4.3

classification 💻 cs.SE
keywords Industrial DevOpsIndustry 4.0DevOpspredictive maintenanceenergy managementTitan platformcontinuous developmentproduction environments
0
0 comments X

The pith

Industrial DevOps transfers software DevOps practices to production environments via continuous operation, observation, and development.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper proposes Industrial DevOps to help small and medium-sized enterprises adopt Industry 4.0 solutions despite software-related obstacles like high costs and uncertainty. It defines the approach around a continuous cycle of operating, observing, and developing the full production environment so that stakeholders, systems, and data integrate through small incremental steps. This setup supports rapid adjustments without large initial investments. The authors accompany the concept with the Titan software platform and a role model, then apply both to energy management and predictive maintenance scenarios.

Core claim

We propose Industrial DevOps as an approach to introduce methods and culture of DevOps into industrial production environments. The fundamental concept of this approach is a continuous process of operation, observation, and development of the entire production environment. This way, all stakeholders, systems, and data can thus be integrated via incremental steps and adjustments can be made quickly. Furthermore, we present the Titan software platform accompanied by a role model for integrating production environments with Industrial DevOps. In two initial industrial application scenarios, we address the challenges of energy management and predictive maintenance with the methods, organization,

What carries the argument

Industrial DevOps, defined as the continuous process of operation, observation, and development applied to the entire production environment to enable incremental integration of stakeholders, systems, and data.

Load-bearing premise

That DevOps methods and culture developed for software can transfer directly to industrial production environments with physical machines and plants while still enabling effective integration and quick adjustments.

What would settle it

An industrial deployment where the continuous operation-observation-development cycle produces no measurable reduction in adjustment time or fails to integrate physical plant data without extended downtime.

Figures

Figures reproduced from arXiv: 1907.01875 by Armin M\"obius, Bj\"orn Latte, Maik Wojcieszak, S\"oren Henning, Stefan Schalk, Thomas Richter, Wilhelm Hasselbring.

Figure 1
Figure 1. Figure 1: The continuous adaption and improvement process of Industrial DevOps [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Titan role model are created. These components, in Titan called bricks, serve for integrating exactly one such system. This allows on the one hand to exchange the underlying tool or infrastructure easily, but on the other hand also to replace the integrating software implementation. To connect bricks (and thus the underlying systems of the production), Titan applies the principles of flow-based program… view at source ↗
read the original abstract

The visions and ideas of Industry 4.0 require a profound interconnection of machines, plants, and IT systems in industrial production environments. This significantly increases the importance of software, which is coincidentally one of the main obstacles to the introduction of Industry 4.0. Lack of experience and knowledge, high investment and maintenance costs, as well as uncertainty about future developments cause many small and medium-sized enterprises hesitating to adopt Industry 4.0 solutions. We propose Industrial DevOps as an approach to introduce methods and culture of DevOps into industrial production environments. The fundamental concept of this approach is a continuous process of operation, observation, and development of the entire production environment. This way, all stakeholders, systems, and data can thus be integrated via incremental steps and adjustments can be made quickly. Furthermore, we present the Titan software platform accompanied by a role model for integrating production environments with Industrial DevOps. In two initial industrial application scenarios, we address the challenges of energy management and predictive maintenance with the methods, organizational structures, and tools of Industrial DevOps.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes 'Industrial DevOps' as an approach to transfer DevOps methods and culture to industrial production environments for Industry 4.0 adoption. Its central concept is a continuous process of operation, observation, and development to enable incremental integration of stakeholders, systems, and data with quick adjustments. It introduces the Titan software platform and an accompanying role model, illustrated via two initial scenarios addressing energy management and predictive maintenance.

Significance. If the transferability of DevOps practices to physical production environments holds, the proposal could reduce barriers for SMEs by lowering experience requirements and enabling incremental changes, complementing existing Industry 4.0 visions. The introduction of a named platform and role model provides a concrete artifact that could serve as a basis for subsequent implementation studies.

major comments (2)
  1. [Abstract] Abstract: the claim that 'adjustments can be made quickly' via the continuous operation-observation-development process and incremental steps is load-bearing for the entire proposal, yet the text provides no technical discussion of adaptations required for physical-domain constraints (real-time control latency, safety certification, hardware variability, sensor/actuator reliability).
  2. [Application scenarios] Application scenarios section: the two scenarios are described only at high-level conceptual terms with no quantitative evaluation, error analysis, baseline comparison, or metrics of integration speed or effectiveness, leaving the central effectiveness claim unsupported.
minor comments (1)
  1. The abstract introduces the Titan platform and role model but does not indicate where in the manuscript their architecture, interfaces, or mapping to the continuous process are detailed, which affects readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review of our proposal on Industrial DevOps. Below we respond point by point to the major comments, clarifying the conceptual scope of the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'adjustments can be made quickly' via the continuous operation-observation-development process and incremental steps is load-bearing for the entire proposal, yet the text provides no technical discussion of adaptations required for physical-domain constraints (real-time control latency, safety certification, hardware variability, sensor/actuator reliability).

    Authors: The manuscript is a conceptual proposal introducing Industrial DevOps as a transfer of DevOps methods and culture to industrial environments, centered on the continuous operation-observation-development process and the Titan platform with its role model. The claim of quick adjustments follows directly from the incremental integration enabled by this process, which reduces the need for large-scale upfront changes. The paper does not include detailed technical analysis of physical-domain constraints because its focus is on the organizational, process, and platform-level integration rather than low-level hardware or certification engineering; such adaptations would be addressed in subsequent implementation work using the proposed approach. revision: no

  2. Referee: [Application scenarios] Application scenarios section: the two scenarios are described only at high-level conceptual terms with no quantitative evaluation, error analysis, baseline comparison, or metrics of integration speed or effectiveness, leaving the central effectiveness claim unsupported.

    Authors: The two scenarios are presented as initial conceptual illustrations of how the Industrial DevOps methods, organizational structures, and Titan platform can address energy management and predictive maintenance. As this is a proposal paper rather than an empirical evaluation study, the scenarios demonstrate applicability at a high level without quantitative metrics, which would require full system deployments and longitudinal data collection outside the current scope. The central claim is supported by the alignment of the proposed process with established DevOps benefits and the concrete artifacts (platform and role model) provided. revision: no

Circularity Check

0 steps flagged

No circularity: purely conceptual proposal without derivations or fitted inputs

full rationale

The paper is a conceptual proposal for Industrial DevOps, defining the approach directly via description of a continuous operation-observation-development process, the Titan platform, and a role model. No equations, predictions, fitted parameters, or derivation chains exist. The two application scenarios are presented as initial uses rather than reductions of prior results. No self-citation load-bearing steps or ansatz smuggling occur; the central claim stands as an independent proposal without reduction to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The paper introduces new conceptual entities and relies on domain assumptions about the transferability of DevOps without empirical validation beyond initial scenarios described at high level.

axioms (2)
  • domain assumption DevOps methods and culture from software development are transferable to industrial production environments
    Core premise of the proposal without detailed justification of transferability.
  • ad hoc to paper A continuous process of operation, observation, and development will enable incremental integration and quick adjustments in production environments
    Fundamental concept stated as enabling the benefits for SMEs.
invented entities (2)
  • Industrial DevOps no independent evidence
    purpose: Approach to introduce DevOps methods and culture into industrial production
    Newly defined term and framework in the paper
  • Titan software platform no independent evidence
    purpose: Platform for integrating production environments with Industrial DevOps
    Introduced as accompanying tool for the approach

pith-pipeline@v0.9.0 · 5730 in / 1388 out tokens · 34889 ms · 2026-05-25T10:09:52.078017+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages

  1. [1]

    Jeschke, C

    S. Jeschke, C. Brecher, H. Song, and D. B. Rawat, Industrial Internet of Things: Cybermanufacturing Systems . Springer, 2017

    work page 2017

  2. [2]

    Industry 4.0: A survey on technologies, applications and open research issues,

    Y . Lu, “Industry 4.0: A survey on technologies, applications and open research issues,” Journal of Industrial Information Integration , vol. 6, pp. 1 – 10, 2017. doi: 10.1016/j.jii.2017.04.005

    work page doi:10.1016/j.jii.2017.04.005 2017

  3. [3]

    Software-defined industrial internet of things in the context of Industry 4.0,

    J. Wan, S. Tang, Z. Shu, D. Li, S. Wang, M. Imran, and A. V . Vasilakos, “Software-defined industrial internet of things in the context of Industry 4.0,” IEEE Sensors Journal , vol. 16, no. 20, pp. 7373–7380, Oct 2016. doi: 10.1109/JSEN.2016.2565621

    work page doi:10.1109/jsen.2016.2565621 2016

  4. [4]

    Evolution of software in automated production systems: Challenges and research directions,

    B. V ogel-Heuser, A. Fay, I. Schaefer, and M. Tichy, “Evolution of software in automated production systems: Challenges and research directions,” Journal of Systems and Software , vol. 110, pp. 54 – 84,

    work page

  5. [5]

    doi: 10.1016/j.jss.2015.08.026

    work page doi:10.1016/j.jss.2015.08.026 2015

  6. [6]

    Microservice architectures for scala- bility, agility and reliability in e-commerce,

    W. Hasselbring and G. Steinacker, “Microservice architectures for scala- bility, agility and reliability in e-commerce,” in Proceedings 2017 IEEE International Conference on Software Architecture Workshops , 2017. doi: 10.1109/ICSAW.2017.11 pp. 243–246

    work page doi:10.1109/icsaw.2017.11 2017

  7. [7]

    Drivers and barriers for microservice adoption – a survey among professionals in Germany,

    H. Knoche and W. Hasselbring, “Drivers and barriers for microservice adoption - a survey among professionals in germany,” Enterprise Mod- elling and Information Systems Architectures (EMISAJ) - International Journal of Conceptual Modeling , vol. 14, no. 1, pp. 1–35, Januar 2019. doi: 10.18417/emisa.14.1

    work page doi:10.18417/emisa.14.1 2019

  8. [8]

    L. Bass, I. Weber, and L. Zhu, DevOps: A Software Architect’s Perspec- tive. Addison-Wesley, 2015

    work page 2015

  9. [9]

    N., Abdrasheva, G

    V . Gruhn and C. Sch¨afer, “BizDevOps: Because DevOps is not the end of the story,” in Intelligent Software Methodologies, Tools and Techniques, H. Fujita and G. Guizzi, Eds. Springer, 2015. doi: 10.1007/978-3-319- 22689-7 30 pp. 388–398

    work page doi:10.1007/978-3-319- 2015

  10. [10]

    Benchmarking the lean enterprise: Organizational learning at work,

    J. Knuf, “Benchmarking the lean enterprise: Organizational learning at work,” Journal of Management in Engineering, vol. 16, no. 4, pp. 58–71,

    work page

  11. [11]

    doi: 10.1061/(ASCE)0742-597X(2000)16:4(58)

    work page doi:10.1061/(asce)0742-597x(2000)16:4(58 2000

  12. [12]

    Denning, The Age of Agile: How Smart Companies Are Transforming the Way Work Gets Done

    S. Denning, The Age of Agile: How Smart Companies Are Transforming the Way Work Gets Done . AMACOM, 2018

    work page 2018

  13. [13]

    J. P. Morrison, Flow-Based Programming, 2nd Edition: A New Approach to Application Development . Paramount, CA: CreateSpace, 2010

    work page 2010

  14. [14]

    A scalable architecture for power consumption monitoring in industrial production environments,

    S. Henning, W. Hasselbring, and A. M ¨obius, “A scalable architecture for power consumption monitoring in industrial production environments,” in IEEE International Conference on Fog Computing , 2019, in press

    work page 2019

  15. [15]

    Clean code: On the use of practices and tools to produce maintainable code for long-living software,

    B. Latte, S. Henning, and M. Wojcieszak, “Clean code: On the use of practices and tools to produce maintainable code for long-living software,” in Proceedings of the Workshops of the Software Engineering Conference 2019. Stuttgart, Germany: CEUR Workshop Proceedings, Februar 2019

    work page 2019

  16. [16]

    Including performance benchmarks into continuous integration to enable DevOps,

    J. Waller, N. C. Ehmke, and W. Hasselbring, “Including performance benchmarks into continuous integration to enable DevOps,” SIGSOFT Software Engineering Notes , vol. 40, no. 2, pp. 1–4, Mar. 2015. doi: 10.1145/2735399.2735416

    work page doi:10.1145/2735399.2735416 2015

  17. [17]

    Design for future: managed software evolution,

    U. Goltz, R. Reussner, M. Goedicke, W. Hasselbring, L. M ¨artin, and B. V ogel-Heuser, “Design for future: managed software evolution,” Computer Science – Research and Development , vol. 30, no. 3, pp. 321–331, Aug. 2015. doi: 10.1007/s00450-014-0273-9

    work page doi:10.1007/s00450-014-0273-9 2015