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Context. Soil-tool interaction modelling and optimisation reduce manufacturing costs and energy requirements for precision tillage equipment design. Diverse tillage tools have been designed to reduce draft requirements and desirable soil disturbance, but this is not fully understood. Aims. The current study investigated the effects of tool width, cone index, depth, and forward speed on draft with corresponding rupture width in order to develop response surface methodology (RSM) and artificial neural network (ANN) models and compared them to other models in order to predict draft and rupture width. Methods. Experiments were carried out in a soil bin with a vertisol, and rupture width was measured using an image processing technique. Key results. Using RSM, the optimum values for minimum draft with maximum rupture width within a range of independent variables were found to be l00 mm tool width, 600 kPa cone index, 141.63 mm tillage depth, and 3 km/h forward speed. For predicting the draft, the coefficients of determination ([R.sup.2]) for ANN and RSM models were 0.997 and 0.987, respectively; for rupture width prediction, [R.sup.2] were 0.921 and 0.976. Conclusions. Developed ANN and RSM models of draft and rupture width were better than other analytical or numerical models, and both models' predictions were in good agreement with experiment values within the range of [+ or -] 5% uncertainty. Implications. The developed models can be used to predict the draft and soil disturbance requirements of tillage tools and design precision tillage tools. Keywords: artificial neural network, cone index, draft, image processing, modelling, response surface methodology, rupture width, vertisols., Introduction Tillage operations are an unavoidable step in agricultural production systems, and consume energy and time. For a proper tillage operation, extensive knowledge of soil characteristics before, during, and after [...]

Costing is important for manufacturing industries. Large methods of costing have evolved over time. Recently, the time-driven activity- based costing (TDABC) system has gained importance and application. This article describes the procedure that allows companies to implement TDABC using the Maynard operation sequence technique (MOST) for improving productivity and profitability. Two parameters are required for TDABC: (1) the unit cost of supplying capacity, and (2) the time required to perform a transaction or an activity. MOST is employed to estimate the time required for each activity. Based on this, time equations are formulated and the practical capacity of activities is determined. The procedure is explained with the help of a case study from a manufacturing industry. The results of the case study are discussed from the perspective of the overall company and also at the product level. This approach provides the capacity analysis and the cost analysis together with its hierarchical decomposition. This paper also discusses the different information obtained from TDABC, and its usefulness for managers and decision-makers.