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The purpose of this article is to introduce a novel cost of quality (COQ) decision support model (DSM), which can help management to track the effect of changing each incorporated value added (VA) and non‐value added (NVA) activity on each other's cost as well as on the quality costs in real time.

System dynamics (SD) is used as the modelling tool due to its dynamic characteristic and its advantages; such as the possibility of integrating qualitative factors and defining learning loops. In order to enhance the performance of the model, activity‐based costing (ABC) cost structure has been integrated.

Lean manufacturing (LM) focuses on the methodologies and approaches that can help an enterprise to reduce the waste factors in its processes. Few studies have proven the capability of ABC in providing valuable cost information for LM implementation due to its activity‐oriented nature. This study is another step towards showing the advantages of ABC in controlling the COQ via using a novel SD modelling methodology.

The model can guide management to establish an LM‐oriented quality policy and control the incorporated costs effectively.

Nowadays companies in the world are selling products by keeping quality as a central value for the customer. Quality-related costs arise from a series of activities performed in order to maintain the quality of product or service. Like other activities of business, quality costs can be programmed, budgeted, measured and analysed to attain the objective of better quality at lower cost. The purpose of this paper is to formulate a decisive methodology to optimize total quality cost (TQC), comprehensively.

The TQC borne by any small- and medium-sized enterprise (SME) is the authentic base, which decides budget and other financial policies for running its ongoing quality programmes. To minimize TQC, one has to make sufficient cut backs in the total failure cost (TFC). Through regression analysis, TFC has been statistically modelled with primary quality costs (like preventive costs (PC) and appraisal costs (AC)). Subsequent graphical analysis has been delineated to elaborate relevant behaviour of different quality costs. At last, potency of suggested methodology has been strategically verified by executing a successful case study in an Indian Auto SME. The requisite optimization has been achieved by using Minitab Statistical Software and its practical validation is checked by conducting a two-sample t-test, exclusively.

It has been found that TFC has a direct positive correlation with TQC and it increases with time. Further, TFC is inversely proportional with PC and AC. PC and AC act as independent costs, while TFC is a dependent or secondary quality cost. If the authors strategically allocate PC and AC in advance by using statistical advanced tools, then internal failure costs and external failure costs will diminish amply.

It inspires practitioner to harvest profits by inculcating quality cost optimization through lodged statistical behavioural investigation. Proposed approach is verified after performing an empirical study only on an automobile manufacturing SME. Further research is indeed required to testify the given methodology for more complex process configurations.

Data related to quality costs is well available with practitioners but generally lying ignored. The case findings will motivate quality practitioners to use the proposed step by step approach for sustainable reduction in overall quality cost.

SMEs find it difficult to lower their TQC due to severe scarcity of resources and funds. Moreover literature provides mainly the theoretical or qualitative cases which remain ineffective to propel SMEs towards the real world savings. But this manuscript will act as a unique road-map to de-code the behaviour of different quality costs quantitatively without ignoring the existing constraints of SMEs, especially in the developing nations.