Control charts applied to simulated sow herd datasets

Krieter, Joachim; Engler, J.; Tölle, K.-H.; Timm, H. H.; Hohls, E.

doi:10.1016/j.livsci.2008.06.025

Cited by 15 publications

(21 citation statements)

References 18 publications

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“…Low k-values make the chart more sensitive to changes (Hawkins and Olwell, 1998;Krieter et al, 2009). In the present study k was tested for the values of 0.1, 0.2 and 0.5 and then set to 0.2 for mastitis as well as for lameness detection at cow level.…”

Section: Classic Cusum Chartmentioning

confidence: 99%

Detection of mastitis and lameness in dairy cows using wavelet analysis

Miekley¹,

Traulsen²,

Krieter³

2012

Livestock Science

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Section: Classic Cusum Chartmentioning

confidence: 99%

Detection of mastitis and lameness in dairy cows using wavelet analysis

Miekley¹,

Traulsen²,

Krieter³

2012

Livestock Science

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“…The majority of the papers handle economically relevant parameters related to animal fertility (Wrathall 1977;Wilson et al 1980;Wrathall & Hebert 1982;Dial et al 1996Dial et al , 1994de Vries et al 1997a, b;Deen 1997;Marsh et al 1997;Morrison et al 1997;Polson 1998;de Vries 2001;de Vries & Conlin 2003, 2005Engler et al 2005Engler et al , 2009Niehoff et al 2007;Krieter et al 2009) and to production performance or product quality (Akachi 1971;de Vries et al 1997a;Pleasants et al 1998;Grennstam 2005;Mertens et al 2008Mertens et al , 2009Lukas et al 2009). Parameters that reflect collective production or quality performance, such as hen-day egg production in laying hens, are less sensitive to process changes as they pool the inter-individual variability.…”

Section: Practical Relevance Of the Process Parametermentioning

confidence: 99%

Statistical control charts as a support tool for the management of livestock production

et al. 2010

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SUMMARYThe concepts of control charts, an important tool in statistical process control, are commonly used for monitoring industrial production processes. In the context of precision livestock farming, their use has been demonstrated by many, although the statistical properties of livestock process data often do not comply with the basic assumptions of such control charts. The focus of the current review is on the most important aspects, recommendations, pitfalls and opportunities for the development and performance of control charts on livestock process data. An important hurdle to tackle is the statistical characteristics of the raw livestock process data which are mostly violating the control charts’ assumptions. An integrated approach, like synergistic control, appears to be promising in handling this issue. The availability of real-time on-farm validation of proposed systems will be crucial for lifting them from the potential level to direct practical relevance.

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“…To test the performance of the EWMA and the CUSUM control charts, the calculated ATS as well as the setting of the constants were chosen according to the results of the foregoing simulation study (KRIETER et al 2009). …”

Section: Performancementioning

confidence: 99%

“…For the RSR, PWM and the NPW a time period of 52 weeks to calculate the charts was chosen. This was done to be able to monitor the production process over a distinct time period and to make a comparison of the results of the simulation study (KRIETER et al 2009) with the actual data possible by choosing the same length of investigation time.…”

Section: Traitsmentioning

confidence: 99%

“…In sow production processes on the other hand, the number of observations per time unit compared to industrial processes may be smaller. KRIETER et al (2009) presented a simulation study to test the performance of the CUSUM chart as well as the EWMA chart applied to sow farm datasets. Both charts were able to detect differential shifts in a timely manner depending on the setting of the charts.…”

Section: Introductionmentioning

confidence: 99%

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Control charts applied to pig farming data

Engler

Tölle

Timm³

et al. 2009

Arch. Anim. Breed.

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Statistical control charts are effective tools to reveal changes in a production process. The CUSUM (cumulative sum) and the EWMA (exponentially weighted moving average) control chart are used to detect small deviations in a process. Data from two sow herds, herd A and herd B, were collected from 1999 to 2004. Farm A had an average number of 530 breeding sows, Farm B had an average of 370 breeding sows. Both herds were diagnosed with Porcine Reproductive and Respiratory Syndrome (PRRS). The weekly means of the number of piglets weaned (NPW), the pre-weaning mortality (PWM) and return to service rate (RSR) were analysed with different settings of the CUSUM as well as the EWMA control chart to reveal a shift in the production process. For the pre-weaning mortality and the number of piglets weaned, the two charts detected a change in the process 4 weeks (Farm A) and 2 weeks before (Farm B) PRRS was diagnosed. The CUSUM and the EWMA chart revealed a shift in the return to service rate on Farm A 3.5 months before PRRS was detected. On Farm B, the signal occurred 6 weeks before the infection was detected. The CUSUM and the EWMA control charts were effective tools for detecting small deviations in sow herd data. Compared with EWMA, the use of the CUSUM chart is more straightforward and the settings are more easily handled. The CUSUM chart is therefore the preferred option for use in practice.

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Control charts applied to simulated sow herd datasets

Cited by 15 publications

References 18 publications

Detection of mastitis and lameness in dairy cows using wavelet analysis

Detection of mastitis and lameness in dairy cows using wavelet analysis

Statistical control charts as a support tool for the management of livestock production

Control charts applied to pig farming data

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