Challenges and progress in integrating knowledge: cases from clothing and textiles in South Africa
Bibliographic record
Abstract
Purpose The central purpose of this paper is to explore how implicit knowledge capabilities and sharing helps secure organizational survival and success. This article explores the challenging in better management knowledge in the South African clothing and textile industry. In moving from a closed protected market supported by active industrial policy, South African manufacturing has faced intense competition from abroad. The ending of apartheid removed a major source of workplace tension, facilitating the adoption of higher value-added production paradigms. However, most South African clothing and textile firms have battled to cope, given cutthroat international competition. The authors focus on firms that have accorded particularly detailed attention to two instances characterized by innovative knowledge management. The authors highlight how circumstances may impose constraints and challenges and how they paradoxically also create opportunities, which may enable firms to survive and thrive through the recognition and utilization of informal knowledge, both individual and collective. Design/methodology/approach This study is based on in-depth interviews, primary company and industry association and secondary documents. Findings The study highlights how successful firms implemented systems, policies and practices for the better capturing and utilization of external and internal knowledge. In terms of the former, a move toward fast fashion required and drove far-reaching organizational restructuring and change. This made for a greater integration of knowledge through the value chain, ranging from design to retail. Successful firms also owed their survival to the recognition and usage of internal informal knowledge. At the same time this process was not without tensions and paradoxes, and the findings suggest that many of the solutions followed a process of experimentation. The latter is in sharp contrast to many South African manufacturers, who, with the global articulation of production networks, have lost valuable knowledge on suppliers and their practices. At the same time, both firms have to contend with an increasingly unpredictable international environment. Research limitations/implications At a theoretical level, the study points to the need to see informal knowledge not only in individualistic terms but also as a phenomenon that has collective, and indeed, communitarian features. Again, it highlights the challenges of nurturing and optimizing informal knowledge. It shows how contextual features both constrain and enable this process. It further highlights the extent to which the effective utilization of external knowledge, and rapid responses to external developments, may require a fundamental rethinking of organizational structures and hierarchies. This study focuses on a limited number of dimensions of this in a single national context but could be replicated and extended into other contexts. Practical implications The study highlights the relationship between survival, success and how knowledge is managed. This involved harnessing the informal knowledge and capabilities of workforce to enhance productivity, in conjunction with improvements in machinery and processes, and a much closer integration of design, supply, production and marketing, underpinned by a more effective usage of IT. Paradoxically, other clothing and textile firms have survived doing the exact opposite – reverting to low value-added cut-and-trim assembly operations. At a policy level, the study highlights how specific features of South African regulation (above all, in terms of job protection), which are often held up as barriers to competiveness, may help sustain the knowledge base of firms. Social implications The preservation and creation of jobs in a highly competitive sector was bound up with effective knowledge management. The study also highlighted the mutual interdependence of employers and employees in a context of very high unemployment and how the more effective usage of informal knowledge bound both sides closer. Originality/value There is a fairly diverse body of literature on manufacturing in South Africa, and, indeed across the continent; however, much of it has focused on challenges. This study explores relative success stories from a sector that has faced a structural crisis of competitiveness, and as such, has relevance to understanding how firms and industries may cope in highly adverse circumstances.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".