Abstract:Companies implement lean manufacturing (LM) tools in their production processes to reduce waste; however, it is difficult to quantify the effect on benefits gained after their implementation. This article proposes a structural equations model (SEM) that relates three LM tools associated with quality as total quality management (TQM), waste, and right first time (RFT) as independent variables associated with commercial benefits gained as a dependent variable. Those four variables were related by six hypotheses that were validated with information from 169 responses to a survey applied to the Mexican maquiladora industry. Partial least squared was used to validate the hypotheses as direct effects. The sum of indirect and total effects was also estimated, and a sensitivity analysis was developed for relationships between variables. Findings indicate that TQM directly affects waste reduction, drives doing RFT, and directly and indirectly affects the commercial benefits gained.Keywords: lean manufacturing; quality management; commercial performance; wastes; DIRFT
total quality management by sunder raju zip
Village health workers identified suspected cases of leprosy during house visits and referred them to the primary health centres where Medical Officers confirmed the diagnosis and initiate treatment. The surveillance data flew from the health sub-centres and were compiled at primary health centres. The primary health centres, in turn, sent the data to the District Leprosy Office once a month. The districts sent the data once every quarter to the state, which in turn compiled and shared with the National Leprosy Division once a year. A designated person was responsible for reporting at each level. All ten health sub-centre staff members interviewed found the reporting form simple, and all were aware of the leprosy case definition. The system was flexible in that all staff referred cases using an alternate mode of reporting (telephone/message). Monthly data were captured on forms that include different categories of variables, and data quality was poor. We found 20% to 63% of the variables on the reporting forms to be completely filled. All (30/30) reporting units that we assessed submitted monthly reports, as per guidelines, indicating acceptability. Sensitivity could not be assessed because the total number of leprosy cases is not available. The positive predictive value was poor, as 19 of the 40 reported cases (48%) actually had the disease. Representativeness was mixed in that no case was reported from the private sector, whereas all five government health facilities that were assessed reported cases. Timeliness could not be assessed as the date of monthly report arrival at the district office, and the report dispatch date was not available. The system was stable in that all staff positions related to leprosy surveillance were filled, there were no system breakdowns, and reporting forms were not out of stock. The surveillance system helps to detect leprosy cases with a grade-II disability that require reconstructive surgery (Table 1). During 2017-18, seven cases had reconstructive surgery, illustrating the usefulness of the system.
We did a cross-sectional analysis of maternal deaths reported in Virudhunagar district during the period 2013-2018. Data were extracted from the state Health Management Information System (HMIS) and verbal autopsy reports. Data were abstracted and analysed using MS Excel. We defined the maternal mortality ratio as the number of women who die from any cause related to or aggravated by the pregnancy or its management during pregnancy and childbirth or within 42 days of pregnancy termination, irrespective of the duration and site of the pregnancy, per 100,000 live births. We calculated MMR by year, health unit district (HUD), area of residence and caste. We described the distribution of maternal deaths by age group, the order of pregnancy, level of delay, source of referral and comorbid conditions. We calculated observed cause-specific mortality rate by dividing the number of maternal deaths reported due to a particular cause by the total number of pregnant women observed and multiplying by 100000. We used the cause-specific mortality rate based on systemic analysis by WHO [2], as the expected cause-specific mortality rate.
The median estimated per capita expenditure of the approved public health budget for NCD programs in 2016- 2017 for all states was 0.3 $( 23 INR). Only seven states in India, like Uttarakhand, Nagaland, Goa, Sikkim, Manipur, Arunachal Pradesh, and Mizoram, have planned an adequate NCD budget per capita 1.05$ (above 80 INR). These states meet the norms of WHO's "best buy policy". We observed 70% of the states have inadequately planned their budget with less than half a dollar (below 35 INR) per capita for their NCD programs. Adequate budget expenditure indicates the quality and operational efficiency of health programs. We also calculated the actual expenditure of the approved NHM budget for the NCD program for different states. This expenditure analysis of different states shows that an average of 39% of the NCD budget remained unutilized. Almost twenty states were utilizing less than fifty percent of the total approved budget under NHM. The aggregated data of the whole utilized budget was further analyzed under the key budget heads as per NHM budget documents' financial framework. The maximum unutilized budget was shown under training, untied funds, community interventions & infrastructure development, innovation, and IEC components.
We conducted facility based cross-sectional surveys in all public sector health facilities during December 2017 to March 2018 in Saidapet HUD, Kanchipuram district, Tamil Nadu. We included 20 primary health centres (PHCs) and six community health centres (CHCs) in the survey. We collected data for six quality domains including Service Provision, Patient Rights, Input, Support services, Clinical Care, Infection Control, quality management and outcomes. We used the National Quality Assessment Standard Tool in an android app named Guide for NQAS and kayakalp (GUNAK). We reviewed the records and interviewed nurses on duty. For scoring, two marks were given for full compliance (>= 70%), one mark for partial compliance (
The main reason for low score in quality management was the lack of training about new standard operating procedures (SOPs). In addition, in CHCs, there was no designated person for Quality assurance activities. Facilities require establishment of an organizational framework for quality improvement. The low scores in outcome domain were possibly due to low number of deliveries conducted at night time which was one of the criteria for scoring. Another area which lowered the score was lack of assisted deliveries at CHC level. This may be due to lack of training for Medical officers and non-availability of specialists. Other indicators which contributed to poor scores were lack of maintenance of records for data such as episiotomy site infection rate and culture surveillance sterility rate. 2ff7e9595c
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