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Kuwait J. Sci. Eng. 34 (2B) pp. 207-224, 2007

Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing

MIRZA JAHANZAIB1 AND KHALID AKHTAR

Mechanical Engineering Department, University of Engineering & Technology, Taxila Email: [email protected], Tel Ph No: 92-51-9047683, Fax No: 92-519047420 and NUST University, Islamabad, Pakistan 3

ABSTRACT

The success of Pakistani manufacturers in meeting the global world challenges will depend on its speed in moving from protected domestic to world class global manufacturing status. To face world challenges, three manufacturing strategies devised in chronological order are: cost cutting, technology driven and value enhancement which uses world class practices tools, and methods and techniques in a systematic and coherent manner with a stepwise re®nement approach. The present work deals with technology driven strategy by analyzing ®ve levels of automation, including engine lathe, turret lathe, automatic machines, numerically controlled machines (NC), and transfer machines. A proposed framework is tested using real life data from discrete parts manufacturing industries processing similar types of parts. Standard mathematical routines already developed are coded in spreadsheets and analyzed by carrying out sensitivity analysis on di€erent process parameters. A comparison of these functions allowed identi®cation of the most, less and least sensitive process parameters. Process parameters were then prioritized based on highest order of sensitivity at each level of technology. A simulation model so developed will help manufacturers to make informed decisions in selecting the most appropriate manufacturing systems based on process parameters.

discrete parts manufacturing; machine automation cost; mathematical simulation; sensitivity analysis; technology driven strategy

Keywords:

INTRODUCTION AND BACKGROUND

Developing countries are facing tremendous pressures and challenges due to more turbulent, dynamic and complex competition in the marketplace. A combination of both external and internal factors like weak infrastructure, foreign debts, increasing inequalities between individuals and geo-political environment have prevented many countries from achieving signi®cant socioeconomic improvement over the last decade. Some developing countries like Pakistan have made economic management their prime agenda and thus are going through the process of restructuring their economy to emphasize

208 Mirza Jahanzaib and Khalid Akhtar competition, integration with global markets and increasing level of privatization. The Pakistani manufacturing industry is striving hard to penetrate into the global marketplace. This can be achieved using state of the art methods, tools and techniques to face current challenges of the world market. Global manufacturers playing in the global market always tend to have world class performance. World class manufacturing is characterized by three core strategies of customer focus, quality and agility supported by six competencies including employees' empowerment, supply chain management, technology, product development, environmental responsibility, employees' safety and corporate citizenship (Kinni 1996). In a new manufacturing environment, time is the primary competitive motive of business. This does not mean that other motives such as cost, quality and service can be ignored. In fact, these are prerequisites to sustain competitiveness. But the winning factor is provided by time and enhancement to the basic product (Stalk & Hout 1991). In a manufacturing environment, time based competition becomes the highest priority to gain responsiveness and ¯exibility (Meyer 1990). There are three pillars to support world class manufacturing: Advanced Manufacturing System (AMS), Total Quality Control (TQC), and Just-In-Time (JIT) (Gunn 1987). This research is therefore, focused on AMS (Gunn 1987, Kinni 1996), as covering all other aspects would have made the study unwieldy and too broad to be meaningful, and addressing this speci®c area is the heart of this research. The objective of this research is to present a Technology Driven Strategy (TDS) framework for selecting an appropriate level of technology using base values taken from di€erent (same product types) companies working for the automobile industry specializing in parts like brake discs, brake drums, case di€erentials, ¯y wheels, planetary carriers and wheel hubs. Although very few ®rms have migrated from traditional systems and upgraded their facilities, a large number of bandwidth is still operating in `island of automation' mode. This stand-alone automation in production systems slows the line pace which forces longer production runs, results in larger inventories, longer cycle times and slower response to the market. As Kinni (1996) proposed six supporting competencies, including technology, this study of the Pakistani manufacturing automobile industry takes a techno-strategic perspective rather than economic one. This paper is organized as follows: the next section brie¯y explains the three manufacturing strategies proposed (Jahanzaib & Akhtar 2005) for the Pakistani manufacturing environment in a normative way, followed by TDS framework and the research methodology. The results, conclusions and recommendations are presented in the last section.

Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing

209

MANUFACTURING STRATEGIES

In order to compete in the world market, Pakistani manufacturers necessarily need to acquire world class performance. This can be achieved by utilizing the available resources more eciently, improving product quality and introducing value added concepts in product design. To cope with this situation, three manufacturing strategies have already been proposed which are: Cost Cutting Strategy (CCS), Technology Driven Strategy (TDS) and Value Enhancement Strategy (VES) (Jahanzaib & Akhtar 2005). The proposed strategies use a bundle of approaches for coping existing manufacturing environments in a systematic manner. These strategies are not mutually exclusive rather a subsequent strategy assumes that the previous strategy has been executed earlier and its bene®ts/ results still exist when the subsequent strategy is implemented. Fig 1 shows the route of three strategies. A brief description of these strategies in normative style follows:

Fig 1: Proposed manufacturing strategy routes

1. Cost Cutting Strategy (CCS)

The object is to obtain increase in production volumes by being more ecient (streamlinning of operations) and overcoming losses (reducing waste times). The tools for Cost Cutting Strategy can be any traditional approach such as line balancing technique, stream lining of operations, minimizing non-productive times, etc., which will help reduce operational loss and allow production to increase thus reducing cost. 2. Technology Driven Strategy (TDS)

The changeover between production runs takes an amount of time called the setup or changeover time, which is the time required to change tooling and to

210 Mirza Jahanzaib and Khalid Akhtar set up and reprogram the machinery. This is lost production time, which is a disadvantage of traditional manufacturing systems. The major role of Technology Driven Strategy (TDS) is to shift from a traditional manufacturing system to a di€erent level of automation (as per requirement) which are more agile, can improve product quality and enhance responsiveness to become competitive in the marketplace. 3. Value Enhancement Strategy (VES)

In the competitive environment the signi®cance of value addition cannot be over-emphasized. In Value Enhancement Strategy, those processes (manufacturing or business) which do not contribute a higher value to the product, i.e. more costly and less important are eliminated. The value deliberately implies looking at cost - importance relationships of the manufacturing processes involved, which allows even higher value added products and are attractive in export due to the additional purchase power of the customer in the developed world. As discussed in the preceding section, we have focused our study on technology driven strategy. In traditional or automated systems for designing and manufacturing a product, geometry plays a vital role for choosing a suitable manufacturing process. Usually ®rms are categorized based on product geometry specialization (prismatic or circular) (Khalid 2003). Processes are normally grouped together on the basis of product geometry (as in Group Technology). Firms specialized in rounded parts are hardly able to manufacture prismatic parts (except mold manufacturers). Product variety (hard or soft) and geometry classi®cation is discussed in details by Grover (2001). The other important considerations are operation cycle time and setup/changeover time. Larger setup times force longer production runs and result in larger inventories, longer cycle times and slower response to the market. This was the realization that led to the development of the Single Minute Exchange (SMED) concept by Shigeo Shingo (1985) in Japan. SMED refers to setup times in `single' minute, i.e. less than 10 min. As discussed earlier the products under consideration are rounded in shape, such as wheel brake drums, wheel hubs, case di€erentials, ¯ywheels, housings etc, so it is relatively easy to calculate the cycle time, labor costs and other related process parameters under the proposed framework of technology driven strategy. Our framework works similarly in concept to automation migration strategy (Grover 2001) (i.e. migration from manual to semi automatic and then fully automated systems) except that we have tested the framework using process parameters and a mathematical simulation-based approach, whereas automation migration strategy is presented in normative

211 style. Secondly, our work is more focused on ®ve levels of technology based on the framework termed Technology Driven Strategy (TDS). The Technology Driven Strategy (TDS) framework is proposed for analyzing the means of improving productivity by improved manufacturing systems and increased automation. The focus is to ®nd out the impact on costs when manufacturing organizations replace manual work with automation. Automation does not allow poorly designed products and inecient processes to exist (Russell & Taylor 1998). There is a lot of pressure on Pakistani manufacturers to increase their product quality and reliability particularly due to increasing competition and likely changes in the world trade structure. It is therefore, imperative for the industry to improve its productivity and competitiveness. One way of achieving this competitiveness is through automation. However, no such studies are available for utilization by the manufacturers in Pakistan. Therefore, there is a need to address this area to aid local manufacturers to use more scienti®c information rather than educated guessing in selecting the appropriate level of technology for the manufacturing system. Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing

Technology Driven Strategy (TDS) framework

The need for Technology Driven Strategy (TDS) has been discussed in the preceding section. TDS consists of three modules: data, expression and output module as shown in Fig 2. The three modules are brie¯y discussed as follows:

Fig 2: Technology Driven Strategy (TDS) framework

212 Mirza Jahanzaib and Khalid Akhtar

Data Module Industrial data from the same product class manufacturing companies are analyzed and validated for the purpose of use. Various costs contribute towards the selling price of a ®nished product including, manufacturing costs, design R&D costs and overheads (typically marketing, sales, customer service and administration costs). The percentage of total cost attributed to manufacturing is about 40%, design R&D costs are about 15% and pro®t about 20%. These ®gures vary in di€erent organizations depending on the product and associated market. However, manufacturing costs are usually the single largest cost element in the selling price and can be broken down further into four main elements with typical percentages: parts and material about 50%, energy, plant and equipment depreciation almost 15%, direct labor nearly 10% and indirect labor about 25% (Mair 1993). It is essential that manufacturing costs be identi®ed and properly classi®ed to allow their appropriate allocation to a particular job (Meigs et al. 1999). The costs involved in producing a product can be classi®ed as variable costs, ®xed costs and sometimes semi-variable costs (Usry et al. 1988). Overhead costs typically include rent and rates, heating, lighting costs, oce sta€ salaries, general laborers, store-men, insurance, equipment costs, depreciation on equipment and servicing costs, i.e. maintenance, etc. The majority of these costs are ®xed because they are incurred irrespective of production rates. In order to determine the total overhead for each department, cost centers are identi®ed within the company. This allows costs to be gathered together according to their incidence (Lucey 1996). The unit cost is the average cost of manufacturing a ®nished product. This is generally found by taking the total cost of production and dividing it by the number of units produced. It is also of utmost importance that the selected materials (both for the job as well as for the cutting tool) have appropriate properties that allow them to perform satisfactorily during service (Edwards & Endean 1990). In terms of cost, the higher the level of performance required, the greater the expense incurred. Therefore, the materials selection decision will generally be a compromise between performance and cost (Dieter 2000). The cost data is taken from di€erent companies used for analyzing level of technology. Expression module This is the main module in which data is entered in routines that are coded in a spreadsheet. These routines work like mathematical simulations o€ering what if scenarios by changing input design parameters (process parameters) one at a

213 time to observe the impact of these changes on output (per unit cost). For this, trial tests are carried out and validated in expression module. The appropriate range is suggested after comprehensive analysis. Sensitivity analysis is carried out by changing the process design parameters like labor, machine cost, etc, as speci®ed in the range. The next step is to obtain the slope of the function from sensitivity results. The mathematical expressions used in simulation process are brie¯y explained as follows: The cost of production on an engine lathe is given by White (1989): Cost=order ˆ …directlabor cos t ‡ machine cos t=unit ‡ cuttingtool cos t=unit† 3 x …1† where x = units per order Turret lathe di€ers from the engine lathe in that the tailstock of the engine lathe is replaced by a hexagon turret, which permits the mounting of multiple tools. The cross slide is made long enough to permit the mounting of another tool post of square turret at the rear of the slide. The turret may be indexed automatically so that each tool can be presented to the work piece in rapid succession (Boothroyd 1977). For a turret lathe Cost=order ˆ setup cos t=order 3 n ‡ …direct labor cos t ‡ machine cos t=unit ‡ cutting tool cos t=unit† 3 x …2† where n = no of setup per order Transfer machines are used exclusively for mass production, the class of machine tool referred to as automatic is used in both mass and large-batch production. For automatic machines, it is assumed that one operator can service `Na' automatic machines, that the operator's rate (including overheads) is Wo and that the rate for one machine (including overheads) is Mt: The cost of production Cprper component will then be given by: Cpr ˆ Cb=Nb ‡ …Wo ‡ Mt † t1 ‡ …Wo =Na ‡ Mt † tm ‡ ‰…Wo ‡ Mt †tct ‡ CtŠ tm=t …3† where, Cb= cost of setting up the machine (including manufacture of cams etc), Nb = batch size, tct= tool-changing time, Ct= cost of providing one new cutting edge, tm = machining time, t= tool life and t1= loading and unloading time. With automatics, programming is expensive and can be justi®ed only for long production runs. However, with machines incorporating feedback control, programs can be provided in the form of punched tapes or punched cards, which are relatively inexpensive to produce compared with disc and drum cams. Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing

0

0

0

0

0

0

0

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214 Mirza Jahanzaib and Khalid Akhtar These machines are known as numerically controlled (NC) machines and can be used economically in small- batch production. For NC machines, the cost of the preparation for machining a batch of components C}b will mostly consist of programming costs. There is a direct relationship between these costs and the machining time `tm' as follows: Cb ˆ Kp 3 tm …4† where Kp}is the cost of programming and tape-preparation per unit machining time. The production cost per component is therefore given by: Cpr ˆ Kp 3 tm=Nb ‡ M …t1 ‡ tm† ‡ tm=t …M 3 tct ‡ Ct† …5† where Nb = batch size, M = total machine and operator rate, Ct= cost of a tool, t = tool life, t1 = loading and unloading and tool-advance and toolwithdrawal time, and tct = tool-changing time. In transfer machines the component is automatically transferred from one machining operation to the next either by a circular indexing table (the rotarytransfer system) or along a conveyor (the in-line-transfer system). It is assumed that on a transfer machine having `Ns' stations the same cutting-tool material is used at each station. Therefore, for any stations s, the tool life relation is Vs=Vrs ˆ …trs=ts† 3 …6† where Vs = cutting speed, ts= tool life, and Vrs = cutting speed for a tool life of trs. The machining time tm for each operation is given by tm ˆ Ks = Vs …7† where Ksis the distance moved by the tool corner relative to the work piece during the machining time tm, and related to …8† …Vrs 3 tm =Ks†1 n The total time to produce Nb components is given by: Nb …tt ‡ tm† Nb X …tmtc 3 tc =ts† …9† where tt= time taken to index the machine and advance and withdraw the tools, tc*ts}= tool changing time at any stations, and 6 = sum of the terms for all the =

215 stations on the machine. Thus, the average production time per component is Tpr ˆ tt ‡ tm ‡ X …tmtc 3 tc = ts† …10† If the rate (costs per unit time) for each station (including the section of the transfer machine associated with that station) is Ms, the total rate for the transfer machine will be 6 Ms, and production cost per component Cpr will be given by: Cpr ˆ X …Ms 3 tpr† ‡ X tm 3 Cts = ts …11† where Cts is the cost of providing a new tool at stations. Substitution of Eqs. (8) and (10) in Eq. (11) and rearrangement gives: Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing

h

h

i h

ii

Cpr ˆ X Ms …ts ‡ tm† ‡ tn1 1 n X Ms X …Ks=Vrs†1 n tcts=trs ‡ X …Ks=Vrs†1 nCts=trs …12† ÿ=

=

=

Equation (12) can now be di€erentiated with respect to `tm' to ®nd the machining time `tmc' giving minimum production costs. Thus, h h i i tmc ˆ …1=n ÿ 1† X ‰Ks=VrsŠ1 n tcts =trs ‡ X ‰Ks=VrsŠ1 n …Cts=trs= X MsŠ n …13† =

=

Output module Based on the slopes of functions, process parameters are prioritized in order of highest level of sensitivity. The recommendations are based on mathematical simulation by carrying out sensitivity to get di€erent responses at each level. Output responses are given in a narrative style so that users can comprehend it, and take right decisions at the right time, just like the case of knowledge based systems, which require intensive knowledge from experts and display results in common sense format. METHODOLOGY

In the preceding sections, the TDS framework has been presented for suggesting a level of technology using process parameters. The mathematical routines relating various manufacturing process parameters with per unit cost have been presented and are taken from standard texts. Fig 3 shows the mathematical simulation ¯ow chart. In data module, real life data from di€erent manufacturers have been collected from industry validated and some trial tests

216 Mirza Jahanzaib and Khalid Akhtar for data usability are carried out. The data is used in expression module as already described in the TDS framework. The relationships expressed in the formulas have been used in spreadsheet to set up corresponding mathematical models. This model behaves like mathematical simulation, as controllable input design factors can be changed one at a time to see the response at the output. We are trying achieving the optimum results from the slopes just like in Response Surface Methodology (RSM) (Law & Kelton 2000). The next step in the expression module is trial testing which was done to ensure the correct setting up of these models. Examples from the literature mentioned in the preceding sections were used. A comparison of the results obtained using spreadsheet models and the standard examples showed that the models have been correctly set up and perform as desired. Having validated the models through these examples, data pertaining to labor rates, machine rates and other related parameters was obtained from automobile part manufacturing industries in Pakistan. Average values of this set of data were used as base values for carrying out a detailed simulation, again using the models developed. The next step was to change one parameter at a time to see the impact on costs per unit for the ®ve di€erent levels of automation. Varying the parameters in small increments and observing the impact on per unit costs gave a greater insight into the economics of each automation level. A comparison of the slopes of these functions allowed us to identify the most sensitive, less sensitive and least sensitive process parameters. The base values were obtained from the Pakistani industries is given in Table 1. Sensitivity analysis was carried out taking 500 iterations for each case by changing the base values according to the plan as given in Table 2 (a) (b) and (c), respectively. Regarding validation process: a) the models have been tested with real life industrial data at data validation and expression module trial test stage; b) sensitivity has been carried out after excluding the initial transient period; c) data from a number of discrete part manufacturers was obtained and average ®gures used in order to reduce organization to organization variability; and d) results from sensitivity are generally consistent with ®ndings mentioned in the literature mentioned above.

Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing

217

Fig 3: Mathematical simulation ¯ow chart

Table 1: Machine and labor costs (a) Machine Costs (Rs)3 Levels Cost Useful Life

Engine lathe Turret lathe Automatic NC Transfer 3

(1US $ = Rs. 60.40)

1,000,000 2,500,000 4,000,000 9,500,000 45,000,000

5 5 10 10 15

(b) Labor Costs (Rs) Levels Cost per Hour

Engine lathe Turret lathe Automatic NC Transfer

30 35 40 55 60

218 Mirza Jahanzaib and Khalid Akhtar Table 2: Range of Sensitivity Analysis Levels

Engine lathe Turret lathe Automatic NC Transfer Levels

Engine lathe Turret lathe Automatic NC Transfer Levels

Automatic NC Turret lathe Automatic Automatic Transfer

(a) Changing Machine Rate Base Value changed from

Rs 0.072216 to 5.062216 (a step change of 0.01) Rs 0.00828 to 7.49328 (a step change of 0.015) Rs. 0.00638 to 0.0313 (a step change of 0.00005) Rs. 0.1709 to 0.4204 (a step change of 0.0005) Rs. 0.23 to 2.725 (a step change of 0.005) (b) Changing Labor Rate Base Value changed from

Rs 14 to 54.32 (a step change of 0.025) Rs 12.95 to 87.8 (a step change of 0.15) Rs. 0.00035 to 0.0253 (a step change of 0.00005) Rs. 30 to 80 (a step change of 0.1) Machine rate (M) inclusive of operator's rate

(c) Changing Tool changing time, Setup, C b , trs , n Tool Changing Time, Base Value changed from

40.24 to 80.16 secs (a step change of 0.08) 40.24 to 80.16 secs (a step change of 0.08)

Number of setups (n) for Turret Lathe Base Value changed from

Setup after every 100 units to setup after every 1000 units

Cb (Cost of setup) for Automatic Machines, Base Value changed from

Rs 0.01966 to 0.51866 (a step change of 0.0001)

(trs ) Tool Life, Base value changed from

2000 to 8000 secs 25 to 105 secs (a step change of 10)

(Cts ) Cost of a tool, Base value changed from

NC Transfer

Rs 5.3 to 55.2 Rs 100 to 900

Transfer

0.111 to 0.25 (a step change of 0.0001) Overhead rates for all ®ve levels, Base value changed from Overhead rate (100%) was changed from 25 to 225% (a step change of 25%)

All ®ve levels

(n) Tool Material Exponent, Base value changed from

Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing RESULTS, CONCLUSIONS AND RECOMMENDATIONS

219

Running of the simulation models as mentioned in the preceding section allowed comparison of the slopes of di€erent parameters so that the most sensitive ones could be di€erentiated from comparatively less and least sensitive ones. This has been done in terms of the ®ve levels, i.e. most sensitive, moderately sensitive, sensitive, less sensitive and least sensitive. The higher the value of slope, the higher is the sensitivity. The slopes are available in terms of impact on per unit cost obtained as a result of making per rupee change in the process parameter. In order to make the result more useful and comparable for the international reader, percentage increase/decrease in cost per unit has been represented by doubling the cost of cutting tool, machine rate, labor rate, overheads etc. Spreadsheet tables and graphs could not be presented here due to lack of space. (They can be obtained from the author if required by any interested reader). However, the results and conclusions are shared in Table 3. It can be concluded that some parameters are more important (sensitive) than others. Sensitivity has been ascertained on the basis of impact on per unit cost and is given in Table 3. The following recommendations based on the conclusions from Table 3 and completed sensitivity results in Table 4 are presented here: 1 - Doubling the machine, labor and overhead rates results in 2.3, 16.6 and 0.51% increases in cost, respectively. The machine rate was found to be the least sensitive so engine lathes should be employed, and labor rate is the most sensitive parameter which can be controlled using the cheapest labor possible. 2 - Doubling the machine, labor, overhead rates and number of setups results in 2.53, 25, 0.7 and 95% increases in cost, respectively. Turret lathe should be used with comparatively large batch size; much smaller number of setups can be obtained. Cost of the labor should be given due priority since it is moderately sensitive in this case. 3 - Doubling the machine, labor, overhead rates and setup cost results in 74, 0.53, 18 and 71.8% increase in cost, respectively. We have adopted an alternate course of action increasing the batch size ten times which signi®cantly decreases (85%) cost per unit and doubling the batch size results in 40% decrease in cost. This suggests that as large volumes as possible should be produced. Machine setup cost, also a sensitive parameter investigated during analysis, can be controlled by employing a single setter engaged for setting up a number of automatic machines. 4 - Machine and overhead rates come out to be the most sensitive indicators. To justify using NC machines, production lines must be properly balanced, and failures and jams should be minimized. Overcoming these problems will lower the unit price and product manufactured from these systems may get more shares in the market at a competitive price. Tool changing time and cost of tool comes out to be less sensitive than machine rate and overheads.

220 Mirza Jahanzaib and Khalid Akhtar 5 - For ultra-large production volumes, transfer machines should be used with particular care taken for the machine origin (machine rate - most sensitive) and tool material exponent (n) being moderately sensitive. Tool life should be given due consideration as it is found to be a sensitive parameter. Cost of tooling turns out to be a comparatively less sensitive parameter, probably because of its small magnitude in comparison with capital cost of the machine. Table 3: Results and conclusions

Engine lathe

Percentage change in Cost per unit

Doubling the machine rate results in 2.3 percent increase in cost Doubling the labor rate results in 16.6 percent increase in cost Doubling the overhead rate results in 0.51 percent increase in cost Turret lathe Doubling the machine rate results in 2.53 percent increase in cost Doubling the labor rate results in 25 percent increase in cost Doubling the overhead rate results in 0.7 percent increase in cost Doubling the number of set ups result in 95 percent increase in cost Automatic machines Doubling the machine rate results in 74 percent increase in cost per unit Doubling the labor rate results in 0.53 percent increase in cost per unit Doubling the overhead results in 18 percent increase in cost per unit Doubling the set up cost results in 71.8 percent increase in cost per unit Doubling the tool changing time increases the cost per unit by 1.5 percent. Doubling the tool life results in 4.75 percent decrease in cost per unit Ten times increase in batch size results in 85 percent decrease in cost per unit. Doubling the batch size results in 40 percent decrease in cost per unit. Numerically Doubling the machine rate results in an increase of 81.56 percent in Controlled (NC) the cost per unit machines Doubling the labor rate results in an increase of 7.69 percent in cost per unit Doubling the overhead rate results in 44.7 percent increase in cost per unit Doubling the tool changing time results in 3.82 percent increase in cost per unit Doubling the cost of tool results in 2.7 percent increase in cost per unit Doubling the batch size results in only 1.55 percent decrease in cost per unit Transfer machine Doubling the machine rate results in an increase of 95.2 percent in the cost per unit There seems to be 86.3 percent reduction in cost per unit by using material with half the value of tool material exponent (n). Doubling the cost of tool results in an increase of 20 percent in the cost per unit Doubling the tool life results in a decrease of 21 percent in the cost per unit

Technology driven strategy (TDS) using machine automation cost in discrete parts manufacturing

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Table 4: Completed sensitivity results

Levels Engine lathe

Final Sensitivity Most Moderately Sensitive Sensitive Sensitive

1. Labor rate 2. Machine rate 3. Overhead rate

X

1. Number of setup (n) 2. Labor rate 3. Machine rate 4. Overhead rate

X

1. Machine rate 2. Setting up cost 3. Overhead rate 4. Tool life 5. Tool changing time 6. Labor rate

X

1. Machine rate 2. Overhead rate 3. Labor rate 4. Tool changing time 5. Cost of a tool

X

1. Machine rate 2. Tool materialexponent (n) 3. Tool life 4. Cost of a tool

X

Turret lathe

Automatic machines

NC machines

Transfer machines

Less Sensitive

X

X

X

X

X

X

X

X

Least Sensitive

X X X

X

X

X

X X

X

222 Mirza Jahanzaib and Khalid Akhtar DISCUSSION

A Technology Driven Strategy (TDS) framework with ®ve levels of technology has been proposed and tested using real life data from industry processing similar parts. Five di€erent levels of technology were analyzed including engine lathe, turret lathe, automatic machines, numerically controlled machines (NC) and transfer machines. A comparison of these functions allowed identi®cation of the most, less and least sensitive process parameters. In general, machine rate turns out to be most important parameter. For automatic and NC machines, machine rates and overheads must be given special consideration. Tool life turns out to be a sensitive parameter for transfer machines. Adequate tool life is, therefore, important in this case. Labor rates should be given due attention when using engine and turret lathes but seem less important in automatics. Machine setting is a moderately sensitive parameter in Automatic machines. Thus, the sequence of operations should be designed and planned in such a way that machine setup cost is minimized. It is evident from the results that certain parameters are important at di€erent levels depending upon the process parameters under investigation. In order to face world challenges, Pakistani manufacturers should pay due attention using proper technology at the right time to become competitive. This might be achieved using a stepwise TDS framework as supporting tool for selecting the appropriate level of technology in the future. ACKNOWLEDGMENT

We are very thankful to Mr. Imtiaz Rastgar [Vice chairman, Engineering Development Board (EDB) Pakistan] for his help and support in collecting data from the manufacturing industry. The grant and support of Mechanical Engineering Department and NUST University is also acknowledged. REFERENCES

Boothroyd, G. 1977. Fundamentals of Metal Machining and Machine Tools, International Student

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Edwards, L. & Endean, M (Eds). 1990. Manufacturing with Materials, Butterworth-Heinemann Butterworth's, England. Grover, M.P. 2001. Automation, Production Systems and Computer Integrated Manufacturing, 2nd edition. Pearson Education. Singapore. Asia.

Manufacturing for Competitive Advantage: Becoming a World Class Manufacturer, Ballinger Publication Company, Cambridge Press, England. Jahanzaib, M., & Akhtar, K. 2005. Coping with low production volumes and very low variety for Pakistani automobile industry - a normative model. Proceedings of 15th International Conference (FAIM- 2005). 18-20 July, University de-Deusto, Bilbao, Spain. Gunn, T. G. 1987.

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Khalid, S. 2003. Manufacturing Resource Planning (MRPII) with Introduction to ERP, SCM and

CRM. McGraw-Hill Professional. India. Kinni, T. B. 1996. America's Best: Industry Week's Guide to World-Class Manufacturing Plants. John Wiley & Sons. New York, NY, USA. Law, A. M. &. Kelton, W. D 2000. Simulation Modeling and Analysis, 3rd edition. McGraw-Hill, New York, NY. USA. Lucey, T. 1996. Costing, 5th edition. Thomson Learning, DP Publications. New York, NY, USA. Mair, G.M. 1993. Mastering Manufacturing, Macmillan, British Printing Press, England. Meigs, R. F., Williams, J. R., Haka, S. F., & Bettner, M. S. 1999. Accounting, the Basis for Business Decisions, 11th edition. McGraw-Hill College, New York, NY, USA. Meyer, W. 1990. Expert Systems in Factory Management: Knowledge-Based CIM. Ellis Horwood, Chichester, England. Russell, R. S. & Taylor, B.W. 1998. Operations Management in Business, Prentice Hall, New York, NY, USA. Shigeo Shingo. 1985. A revolution in manufacturing: The SMED system, Productivity Press, Singapore. Stalk, G. & Hout, T. M. 1991. Competing Against Time: How Time-Based Competition is Reshaping Markets. Free Press, New York, NY, USA. Usry, M. F., Hammer, L. H. & Matz, A.1988. Cost Accounting Planning and Control, 9th Edition.{\small }South Western College Publishing, USA White. J. A., Agee, M H. & Case, K. E. 1989. Principles of Engineering Economic Analysis, 3rd edition. John Wiley Sons, New York, NY, USA. Submitted : Revised : Accepted :

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