Manufacturing Management Practices

The set of manufacturing management tasks listed in the Figure 6.6 was evaluated with respect to:

• Importance to top management in the short-term
• Importance to top management in the long-term
• Perceived usefulness of computers for the task

Again, five-point scales were used for rating importance and usefulness. A score of I indicated ‘not important’ or ‘not useful’, while a score of 5 indicated ‘very important’ or ‘very useful’. The results should be interesting for IT managers who want to priorities their spending due to resource constraints. An interesting finding was that the following tasks have scores of more than 4 on all three criteria:

• Working capital management
• Quality assurance
• Production planning
• Materials planning
• Purchasing
• Production
• Finished goods distribution

Product design is one task in which the strategic use of IT has had a major impact globally (e.g. in the case of Boeing). In India, this task is not viewed as important because very few companies have indigenous product development capabilities (Figure 6.7). 51 per cent of the respondents said that less than 25 percent of their product range was designed in-house. Only 8 per cent of the companies went for in-house design for 51 to 75 per cent of their product range. More than one-fourth of respondents stated that no effort was being made to boost interaction between their design department and production engineering. This might indicate a poor practice of design for manufacturability (i.e. designing products that can be manufactured more easily).

Indian production planners seem to bear the brunt of the dictum that a forecast is always wrong!

The consensus seems to be that varying sales forecasts ‘frequently’ make it difficult to make feasible plans (Figure 6.8). Some respondents said that this happened ‘very often’, and there are also cases where it happened ‘almost always’. The problems of forecasting were probably compounded by the longer manufacturing cycle times, which make it necessary to forecast for longer time horizons. The other factors that are commonly perceived as causes for concern for production planners-invalid standards (e.g. BOMs) and inventory data-were not viewed as serious problems. The biggest obstacles in achieving production targets were lack of timely supplies of materials from vendors and absenteeism (Figure 6.9). The other factors-equipment breakdowns and power cuts-were lesser problems. Power cuts were external variables, which can only be controlled through captive power generation facilities. The problems with supplies reflect on Indian industry’s weakness in purchasing management. Less than half of the responding companies (about 40 per cent) had automated shop scheduling and loading systems. Such systems, whether in-house or purchased, can greatly increase productivity and effectiveness on the shop floor.

Figure 6.6: Importance of Management Tasks and the Perceived Usefulness of IT for them

Figure 6.9: Factors Preventing Achievement of Production Plan

Figure 6.10 shows the number of companies that used various planning mechanisms. Many companies reported that they use Material Requirements Planning (MRP) in conjunction with shortage lists. This implies that the use of MRP had not been completely effective because MRP use should ideally eliminate the need for shortage lists. We also found that many managers called their material planning process MRP even if it was not the standard MRP procedure. In summary, MRP was found to be the most common planning mechanism but shortage lists continued to be used, while pull systems were at an early stage of use.

The inventory figures shown in Figures 6.11, 6.12 and 6.13* are representative averages for the respondents. High capital costs notwithstanding, Indian industry continues to operate in high inventory mode. Though more than 15 companies had achieved more than 100 finished goods inventory turns per year, many of them were dedicated ancillary units or companies that produced heavy equipment to order. Most of the respondents carried more than three days of finished goods inventory, more than one week of WIP inventory and more than two weeks of raw material inventory. Many respondents did not have figures available on their WIP inventory. The pipeline inventory level varied across industries and respondents. It also depended on the size of the market serviced.

Most of the respondents (83 per cent) stated that their staff helped vendors to improve their processes. Similarly, formal vendor rating systems were used by four-fifths of the respondents whereas only one-fifth replied in affirmative that components/materials were supplied directly to the shop floor without any incoming inspection.

*The horizontal axes in Figures 6.11 to 6.20 and 6.22 to 6.26 show the number of responding companies.

However, it is interesting to note that 69 per cent of the companies had a breakdown of their vendor’s cost for important items. Having cost data implies a level of trust and it is a requisite for joint cost-reduction efforts, the benefits of which are shared by both the company and vendor.

The ultimate goal of vendor development is realized when the vendor reaches a stage of zero defects in quality and delivery. This enables the vendor to ship materials or components directly to the buyer’s shop floor, cutting out a lot of waste from the system. This stage had been reached by less than one-fourth of the responding companies. Though product quality improvement is stated to be the most strategic objective in manufacturing (Figure 6.4) and TQM is the most widely used management tool (Figure 6.5), it is ironic to see that the results of the quality movement are only beginning to show on the shop floor.

About 50 per cent of the responding companies stated that they had defect rates, before rework, of up to I per cent. A major chunk of the responding companies stated defect rates of 1-3 per cent, which was far above the global standards of parts per million (PPM) or defect rates on the order of 0.0001 per cent. Companies also reported defect rates of more than 5 per cent. The redeeming factor here was that the profile of defect rates would probably have been much worse had a similar survey been carried out ‘five years ago. Also on the positive side, 10 companies reported defect rates of less than 0.1 per cent.

The use of TPM (Figure 6.5) has not resulted in World-Class maintenance practices. Perhaps, as in the case of quality, the need for significant improvement in this area has been felt only in the last five years. Only one third of the respondents have reported downtime percentages of less than 2 per cent. Unlike zero defects, which many experts agree is a long-tern goal, near-zero downtime should definitely be achievable.

Figure 6.14: Average Pipeline Inventory in the Finished Goods Distribution System

The disquieting finding was that none of the respondents had switched to predominantly cellular layouts, which are generally considered one of the foundations for World-Class manufacturing in the discrete manufacturing sector. The remaining types of layouts were in keeping with the conventional trends. Generally speaking, product layouts are used when the scale of production favors dedicated production facilities, process layouts in the case of batch production and fixed position layouts rue used in heavy industries where the ‘job’ does not move and equipment is moved around it.

Figure 6.15: Average Percentage Defect Rate (Before Rework)

Set-up/changeover time is the key to ‘lean’ production in any plant that processes multiple materials on the same equipment. Large set up times force longer production runs and result in larger inventories, longer cycle and slower response to the market. This was the realization that led development to the development of the SMED concept by Shigeo Shingo. SMED refers to set up times in ‘single’ minutes, i.e. less than 10 minutes. The concept is not restricted to press shops. Only two responding companies had reached this stage, while 11 companies were close to it. They constituted about 15 per cent of the survey sample.

Figure 6.16: Average Downtime Percentage