Rajeev Malhotra

Modern weaving installations employ high speed automatic weaving machines with air-jet and other shuttleless weft insertion systems. The improved productivity of these machines is accompanied by increased complexity and cost, when compared with the traditional shuttle looms. Although increased mechanization has resulted in reduced labor requirement per machine and higher yardage per man-hour, it has made the competence and technical skill level of the human operator more critical [1 – H. Niedhardt, Textiltechnik, 1973, 23, 560]. Such machines are expensive to install and operate, and, because they represent a very significant capitalization cost, it behooves management to maximize their utilization. The downtime becomes critical with such machines, and the need to avoid all unnecessary stoppages and deep the downtime per stoppage as low as possible assumes great importance. Operating such installation s profitably requires extensive control over productivity and performance.

Since the 1970s, electronic performance monitoring has become an almost standard feature in weaving installations. Electronic monitoring provides a quick feedback on the performance of machines, materials and the operators. Manual data evaluation is not useful in these setups because it is a slow and labor intensive process. The results obtained are of historic value only. Immediate intervention in the production process cannot be done on the basis of these results [2 – G. Brockel and W. Primosch, Melliand Textilber, 1972, 53, 869]. However, the improvement in productivity with the electronic monitoring systems does not come about by itself and is largely dependent on how effectively the monitored information is put to use. If the plant management is not organized well to make use of these systems, the data generated may end up being used for wage calculation and similar purposes. The monitored data is generated much faster than can be used in real time. The full potential of monitoring systems is yet to be realized by many of the plants. The reason for this is that the weave room functions, such as maintenance and machine supervision, have not been reorganized to operate under the new setup.

The aim of this study is to explore the possibility of utilizing the feedback from the monitoring systems more effectively for machine supervision purposes with the ultimate goal of reducing machine downtime and improving the operator utilization. The downtime of machines represents a cost in terms of lost production. It comprises of the time taken to actually service the stopped machines and the time the machines have to wait for service while the operators are busy repairing the other machines. This waiting time component of the downtime is also referred to as machine interference and it constitutes a significant portion of the downtime. One way to reduce interference is to have a large number of operators but that increases the production cost in terms of increased wages (Figure 1).

The number of machines that should be allocated to each operator is based on work study data and is determined using interference models or waiting line models. Traditionally, an individual operator is made responsible for a set of machines and the wages are calculated on the basis of number of picks inserted during the shift. However, studies have indicated that if work is allocated on a group basis, i.e. a group of operators for a set of machines, higher productivity is obtained due to reduced machine interference. Nikitina and Kopylev [3 – L. N. Nikitina and S. N. Kopylev, Teknol. Tekstil. Prom., 1973, No. 6, 9] report a productivity increase of 1.5% on the Russian STB weaving machines at no extra cost when 12 machines were assigned to a group of two operators instead of the usual allocation of six machines to each operator. The concept of group work allocation has not received much attention from the industry because of the administrative problems associated with such as system. These include how to compute the wages of the operator, and more importantly, how to identify and record each individual’s performance in the group. However, with the machines communicating with a central computer under the electronic monitoring system, ways can be devised to overcome these problems and make group work allocation a feasible proposition. A major portion of this thesis is devoted to a detailed investigation of the effects of group work allocation on the various performance parameters, using data obtained from a modern air-jet weaving installation.

Polyanichko and Gres [4 – A. L. Polyanichko and B. V. Gres, Teknol. Tekstil. Prom., 1975, 35, No. 1, 44] note that, when the machine complements per operator are large, as in certain types of jet weaving systems, improved productivity can be achieved if the operators are provided with some means of transport. To obtained quantitative data on this aspect, the effect of walking speed of the operators on performance parameters is also investigated.

A man-machine system that uses the electronically monitored data from the weaving machines for controlling the group of operators supervising the machines is modeled. A cost-benefit analysis of such a system is carried out.

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