Operator agency in process intervention: tampering versus application of tacit knowledge
Tóm tắt
Statistical process control (SPC) theory takes a negative view of adjustment of process settings, which is termed tampering. In contrast, quality and lean programmes actively encourage operators to acts of intervention and personal agency in the improvement of production outcomes. This creates a conflict that requires operator judgement: How does one differentiate between unnecessary tampering and needful intervention? Also, difficult is that operators apply tacit knowledge to such judgements. There is a need to determine where in a given production process the operators are applying tacit knowledge, and whether this is hindering or aiding quality outcomes. The work involved the conjoint application of systems engineering, statistics, and knowledge management principles, in the context of a case study. Systems engineering was used to create a functional model of a real plant. Actual plant data were analysed with the statistical methods of ANOVA, feature selection, and link analysis. This identified the variables to which the output quality was most sensitive. These key variables were mapped back to the functional model. Fieldwork was then directed to those areas to prospect for operator judgement activities. A natural conversational approach was used to determine where and how operators were applying judgement. This contrasts to the interrogative approach of conventional knowledge management. Data are presented for a case study of a meat rendering plant. The results identify specific areas where operators’ tacit knowledge and mental model contribute to quality outcomes and untangles the motivations behind their agency. Also evident is how novice and expert operators apply their knowledge differently. Novices were focussed on meeting throughput objectives, and their incomplete understanding of the plant characteristics led them to inadvertently sacrifice quality in the pursuit of productivity in certain situations. Operators’ responses to the plant are affected by their individual mental models of the plant, which differ between operators and have variable validity. Their behaviour is also affected by differing interpretations of how their personal agency should be applied to the achievement of production objectives. The methodology developed here is an integration of systems engineering, statistical analysis, and knowledge management. It shows how to determine where in a given production process the operator intervention is occurring, how it affects quality outcomes, and what tacit knowledge operators are using. It thereby assists the continuous quality improvement processes in a different way to SPC. A second contribution is the provision of a novel methodology for knowledge management, one that circumvents the usual codification barriers to knowledge management.
Tài liệu tham khảo
Alloway Jr JA (1994) Card drop shop. Quality Progress 27(7):99–104
Coleman DW (1999) Adapting Deming’s funnel experiment to a content-specific area. Simulation Gaming 30(1):8–19. doi:10.1177/104687819903000103
D’Eredita MA, Barreto C (2006) How does tacit knowledge proliferate? An episode-based perspective. Organ Studies 27(12):1821–1841. doi:10.1177/0170840606067666
Davis W (2000) Using corrective action to make matters worse. Quality Progress 33(10):56–61
Deming WE (1986) Out of the Crisis. MIT Center for Advanced Study, Cambridge
Desouza KC (2003) Facilitating tacit knowledge exchange. Commun ACM 46(6):85–88. doi:10.1145/777313.777317
FIPS (1993) Integration definition for function modeling (IDEF0). http://www.itl.nist.gov/fipspubs/idef02.doc. Retrieved 12 Aug 2003
Georgantzas NC, Katsamakas E (2008) Tampering dynamics: SD-SPC insight. Human Syst Manag 27(2):89–108. doi:10.3233/hsm-2008-0672
Goffin K, Koners U (2011) Tacit knowledge, lessons learnt, and new product development. J Prod Innov Manage 28(2):300–318. doi:10.1111/j.1540-5885.2010.00798.x
Hamieza M, Amirreza F (2012) Challenges in Managing Tacit Knowledge: a study on difficulties in diffusion of tacit knowledge in organizations. Int J Bus Soc Sci 3(19)
Hanna MD (2010) Using a spreadsheet version of deming’s funnel experiment in quality management and OM classes. Decis Sci J Innov Educ 8(1):137–142. doi:10.1111/j.1540-4609.2009.00248.x
Jasimuddin SM (2012) Knowledge management: an interdisciplinary perspective, vol 11. World Scientific
Johannessen J-A, Olaisen J, Olsen B (2001) Mismanagement of tacit knowledge: the importance of tacit knowledge, the danger of information technology, and what to do about it. Int J Inf Manage 21(1):3–20
KBSI (2000) IDEF0 Overview. http://www.idef.com/idef0.html. Retrieved 12 Aug 2003
Krehbiel TC (1994) Tampering with a stable process. Teach Stat 16(3):75–79
MacGregor JF (1990) A different view of the funnel experiment. J Quality Technol 22(4):255–259
McNaught K, Chan A (2011) Bayesian networks in manufacturing. J Manuf Technol Manag 22(6):734–747
Nonaka I (1991) The Knowledge-Creating Company. Harvard Bus Rev 69(6):96–104
Nonaka I (1994) A dynamic theory of organizational knowledge creation. Organ Sci 5(1):14–37. doi:10.2307/2635068
Olsen T (2007) Deming’s quality experiments revisited. INFORMS Trans Educ 8(1):6
Pearce A, Pons D (2013) Implementing lean practices: managing the transformation risks. J Ind Eng 790291:1–19. doi:10.1155/2013/790291
Perraton J, Tarrant I (2007) What does tacit knowledge actually explain? J Econ Methodol 14(3):353–370. doi:10.1080/13501780701562559
Polanyi M (1958) Personal knowledge: towards a post-critical phylosophy. University of Chicago Press, Chicago
Rowley J (1999) What is knowledge management? Library Manag 20(8):416–420
Schall SO (2012) Variability reduction: a statistical engineering approach to engage operations teams in process improvement. Quality Eng 24(2):264–279. doi:10.1080/08982112.2012.641143
Schmidt K (2012) The Trouble with ‘Tacit Knowledge’. Comput Support Coop Work 21(2–3):163–225. doi:10.1007/s10606-012-9160-8
Shewhart WA (1931) Economic control of quality of manufactured product. D. Van Nostrand Company Inc, Lancaster
Singer G, Ben Gal I (2007) The funnel experiment: the Markov-based SPC approach. Quality Reliab Eng Int 23(8):899–913. doi:10.1002/qre.852
Sparks RS, Field JBF (2000) Using Deming’s Funnel Experiment to Demonstrate Effects of Violating Assumptions Underlying Shewhart’s Control Charts. Am Stat 54(4):291–302. doi:10.2307/2685781
Stenmark D (2000) Leveraging tacit organizational knowledge. J Manag Inf Syst 17(3):9–24
Xie M, Del Castillo E, Goh TN, Cai DQ (2001) On the monitoring of trended and regularly adjusted processes. Int J Prod Res 39(16):3641–3650. doi:10.1080/00207540110064910
Xie M, Tsui KL, Goh TN, Cai DQ (2002) Process capability indices for a regularly adjusted process. Int J Prod Res 40(10):2367–2377. doi:10.1080/00207540210133543