Exploring lean six sigma-enabled demand management: a case study of an automotive aftermarket parts distributor
Nicola Karen Lawrence et al.
What the paper says
Purpose This study aims to explore how lean six sigma (LSS) may enhance demand management to improve customer value in South Africa’s automotive aftermarket. It addresses the need for structured, customer-centred strategies in volatile, resource-constrained supply chains. Design/methodology/approach A qualitative single-case study in a South African automotive aftermarket parts distributor. Data were collected via semi-structured interviews with staff across supply chain, planning and procurement. Analysis, guided by lean theory and the resource-based view, examined how LSS principles align with demand management practices and value delivery. Findings Customer value was defined by participants as availability, price and service reliability. Demand management was acknowledged as central but hindered by fragmented metrics, reactive practices and limited visibility. While LSS was not formally adopted, it was recognised as a relevant tool for improving consistency, responsiveness and problem-solving. Leadership, internal capabilities and cross-functional collaboration were identified as critical enablers. Research limitations/implications Findings are limited to one organisation and are based on perceived rather than observed LSS use. Practical implications Managers can use LSS to improve demand processes if supported by leadership and capability building. Originality/value This paper contributes practice-grounded evidence from a South African, service-oriented aftermarket context, clarifying the capability conditions, leadership commitment, analytical competence and cross-functional integration, under which LSS-aligned demand management supports value-oriented planning and fulfilment.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.