Robustness assessment in production systems

Abstract

PurposeThe concept of robustness in manufacturing is not easy to capture and even harder to quantify. This paper elaborates an approach to assess robustness in production systems from a holistic input-throughput-output perspective using a pragmatic robustness indicator.Design/methodology/approachFirst, in order to have a precise understanding of what needs to be measured, a concept of robustness in production systems is defined based on a literature overview. Three different aspects are considered to be essential to comprehensively describe robustness in production: the deviations of input resources, of performance and of output. These aspects are translated into an aggregated indicator based on developments of production costs, order delays and output volumes. The indicator-based assessment approach is eventually applied to a flow-shop scheduling case study in the chipboard industry.FindingsThe study shows that an assessment of robustness should not solely focus on a single aspect of a production system. Instead, a holistic view is required addressing the tradeoffs that robustness must balance, such as the one between the realized performance, the corresponding resource requirements and the resulting output. Furthermore, the study emphasizes that robustness can be interpreted as a superior system capability that builds upon flexibility, agility, resilience and resistance.Research limitations/implicationsFirst, the paper is a call to further test and validate the proposed approach in industry case studies. Second, the paper suggests a modified understanding of robustness in production systems in which not only the deviation of one single variable is of interest but also the behavior of the whole system.Practical implicationsThe approach allows practitioners to pragmatically evaluate a production system’s robustness level while quickly identifying drivers, barriers and tradeoffs.Originality/valueCompared to existing assessment approaches the proposed methodology is one of the first that evaluates robustness in production systems from a holistic input-throughput-output perspective highlighting the different tradeoffs that have to be balanced. It is based upon a comprehensive concept of robustness which also links robustness to adjacent capabilities that were otherwise only treated separately.

Exploring challenges in dimensioning safety buffers: an empirical study

“…This is closely related to CR6 that appeared in several cases, namely that safety buffers are decoupled by top management's responsibilities. Different objectives receive attention from top management over time and prioritisation naturally occurs, for no method for dimensioning safety buffers can provide optimal results in every dimension, as Stockmann et al . (2021) also found while investigating robustness assessments in production systems.…”

“…That outcome contradicts what several cases revealed, in which performance metrics were discussed as being comparable, although the underlying definitions for those metrics differed in details. As a consequence, CI10 can be underestimated in such cases because definitions can vary widely (Stockmann et al. , 2021).…”

“…Although performance measures are essential input data both for judgemental and calculation methods, such measures rely upon definitions that differ widely in type (Stockmann et al, 2021) and can be defined and measured in multiple ways (e.g. Forslund and Jonsson, 2010).…”

“…This is closely related to CR6 that appeared in several cases, namely that safety buffers are decoupled by top management's responsibilities. Different objectives receive attention from top management over time and prioritisation naturally occurs, for no method for dimensioning safety buffers can provide optimal results in every dimension, as Stockmann et al (2021) also found while investigating robustness assessments in production systems. Guidance and support from top management is thus essential for succeeding in dimensioning safety buffers, not least as a means of cultivating the motivation to learn and adaptability to change, as mentioned by Sun et al (2022).…”

“…Keskin et al (2022) have additionally highlighted the importance of not only the quality but also the quantity of input data for improvements, especially in shifting conditions. Input data can be defined in multiple ways and each definition has ramifications (Stockmann et al, 2021), which is not clearly described in several cases (see CI10). Different measurements were faulty compared and that mismatch has implications as difficulties to communicate input data (e.g.…”