Talks upon practical subjects /

Pages 156-160 are advertisements.

Genetic parameters and predicted response to selection for lean growth in an Australian commercial nucleus pig herd

Genetic parameters for performance traits in a pig population were estimated using a multi-trait derivative-free REML algorithm. The 2590 total data included 922 restrictively fed male and 1668 ad libitum fed female records. Estimates of heritability (standard error in parentheses) were 0.25 (0.03), 0.15 (0.03), and 0.30 (0.05) for lifetime daily gain, test daily gain, and P2-fat depth in males, respectively; and 0.27 (0.04) and 0.38 (0.05) for average daily gain and P2-fat depth in females, respectively. The genetic correlation between P2-fat depth and test daily gain in males was -0.17 (0.06) and between P2-fat and lifetime average daily gain in females 0.44 (0.09). Genetic correlations between sexes were 0.71 (0.11) for average daily gain and -0.30 (0.10) for P2-fat depth. Genetic response per standard deviation of selection on an index combining all traits was predicted at $AU120 per sow per year. Responses in daily gain and backfat were expected to be higher when using only male selection than when using only female selection. Selection for growth rate in males will improve growth rate and carcass leanness simultaneously.

The impact of lean practices on inventory turnover

Lean manufacturing (LM) is currently enjoying its second heyday. Companies in several industries are implementing leanpractices to keep pace with the competition and achieve better results. In this article, we will concentrate on how companies can improve their inventoryturnover performance through the use ofleanpractices. According to our main proposition, firms that widely apply leanpractices have higher inventoryturnover than those that do not rely on LM. However, there may be significant differences in inventoryturnover even among lean manufacturers depending on their contingencies. Therefore, we also investigate how various contingency factors (production systems, order types, product types) influence theinventoryturnoveroflean manufacturers. We use cluster and correlation analysis to separate manufacturers based onthe extent of their leanness and to examine the effect of contingencies. We acquired the data from the International Manufacturing Strategy Survey (IMSS) in ISIC sectors 28–35.