- Email: [email protected]
Performance and partnership in global manufacturing-modelling frameworks and techniques
Int. J. Production Economics 60—61 (1999) 261—269
Performance and partnership in global manufacturing-modelling frameworks and techniques Edmond K. Lo*, Chamli Pushpakumara School of Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK
Abstract Manufacturing globalization warrants researches in the two important related areas. First, the evaluation and comparison of performance based on the structure and future requirements of an industry. Second, the evaluation of essential synergistic effects resulted from sharing of resources in different forms of partnerships. This paper presents two related analytical models to quantify manufacturing performance and partnership synergy in a global context. It will analyse the related literature, outline model frameworks and evaluation methodologies, and illustrate the applicability. 1999 Elsevier Science B.V. All rights reserved. Keywords: Global manufacturing; Capability; Performance; Partnership; Synergy
1. Introduction Many companies perceive global manufacturing as integral to their strategy for improved business performance and growth. Encompassed within this strategy is clearly the opportunity to access new markets, but of equal importance are the opportunities to explore and pursue joint venture and co-production activities. In the domain of corporate strategy, the study of corporate diversification has been a central theme and the relationship between performance and diversification has received the most research attention, and some studies have touched on the issues of alliance synergy in diversification processes [1]. Two interrelated areas of
* Corresponding author. Tel.: 00 44 114 225 3419; fax: 00 44 114 225 3433; e-mail: [email protected].
work are of particular importance in the study of Global Manufacturing. First, the evaluation and comparison of manufacturing competitiveness based on the structure and future requirements of an industry. Second, the identification and evaluation of the synergy of partnership between comanufacturers and vendor/suppliers co-operating in a global supply chain. With the growing interest in manufacturing as a competitive weapon, literature is abound on the subject of manufacturing strategy and manufacturing competitiveness, addressing both process and content issues [2]. At the early stages most of them were of conceptual nature. But now there is evidence of an increase in the number of empirical studies as well. Most studies associated with the evaluation of global manufacturing performance were focused on macroeconomic level using a country as the unit of study. The results are somewhat
0925-5273/99/$ - see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 5 2 7 3 ( 9 8 ) 0 0 1 5 0 - 9
262
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
inadequate for companies which require specific and structured information that would enable them to analyse their competitive position in a changing industry within the overall context of a combination of industrial, national and global economic trends. Furthermore, improving internal efficiency alone is no longer sufficient in ensuring global competitiveness. As activities performed by suppliers and users within a supply chain often account for 60—80% of the total manufacturing cost [3,4], companies have to work together beyond the traditional boundary of transactional, short-term business arrangements and into more durable partnering relationships. Partnering promises greater productivity, reduced costs and new marketplace value [5]. Its importance in manufacturing today warrants explicit evaluation of the performance improvements arisen from the synergy of the global manufacturing partnership [6]. Two related analytical models which quantify global manufacturing performance and partnership synergy are currently developed at Sheffield Hallam University. The first model aims to measure, compare and project manufacturing performances of a company if it supplies to a particular market using products manufactured in different locations, taking into account of global development in related industrial sub-sectors. The second model attempts to evaluate quantitatively and qualitatively the manufacturing synergy between vendors and customers, contractors and suppliers, licensers and licensees, and co-manufacturers. It is based on the scenario that a company has identified a number of potential collaborating companies and is in the process of selecting among them a suitable partner for an appropriate operation mode. The model aims to develop an analytical tool to measure, on a relative basis, essential synergy factors identified for a particular partnership, and to compare these factors with corresponding measures calculated for an alternative partnership combination. 2. Manufacturing capability evaluation 2.1. Modelling framework Fig. 1 shows the generic framework based on which the Global Manufacturing Capability Model
Fig. 1. Manufacturing capability model framework.
is developed. The model is conceptually represented by a cuboid whose three axes relate respectively to performance, process and potential. The overall manufacturing performance of a company studied in terms of its abilities to perform major steps of production can be used as an indicator for the measurement and comparison of its manufacturing capabilities. Therefore manufacturing performance measures and the production process are the two key elements in the study of manufacturing capabilities in general. The ‘process’ axis is industry-specific. It represents major process routes which have significant effects on the global manufacturing performance of an industry or a company. “Potential” axis represents emerging changes resulted from industrial factors such as technological advances, product life cycle and/or environmental restriction. Potentials can be positive or negative, and may lead to future changes in performance and hence competitiveness. Performance measures can be analysed in a hierarchical manner. Industrial factors become increasingly process-specific as they extend down the hierarchy. The relative importance of individual performance measures varies according to the structure of the industry in a particular region. Within an organization, the effect of different processes on each performance measure also varies
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
depending on factors such as cost structure and process capability.
2.2. Performance analysis Initial studies on performance measures emphasised on accounting factors extracted from financial reports [7]. However, they represent past cost performance and do not account for other intangible and qualitative information which are essential in today’s global market. Partial measurement may lead to incorrect conclusions [8]. Consequently, it is essential to evaluate global manufacturing capability based on a comprehensive performance measurement system that encompasses both financial and non-financial measures in a manner appropriate to a particular industry [9]. In this study, manufacturing performance is analysed under the four competitive priorities of cost, quality, delivery and flexibility. Better performing companies generally compete on multiple capabilities rather than focusing on one or two [10]. Manufacturing cost and transportation cost are considered to be the primary elements for cost evaluation. Manufacturing cost composes of materials cost, energy cost, labour cost and plant consumable cost. It can be expressed in non-financial productivity parameters. Compared with monetary figures, productivity parameters are processoriented and less commercially sensitive. They are converted to monetary data specific to a location using published data. Considering transportation costs is vital in globalised operations because any reduction in production costs achieved by the global shifting of manufacturing activities could substantially be offset by transportation costs involved. Quality measures can be classified according to in-bound, in-process and out-bound qualities to account respectively for the incoming materials, in-process parts and finished products [11]. Potentially, the inbound quality can be measured by a consistency factor which describes the variance in quality of different lots of materials purchased for a particular application. In-process quality can be represented by parameters such as percentage of products acceptable first time without re-work.
263
Product range, specification, frequency of customer complaints and the nature of the customer base provide a base to measure outbound quality. Delivery speed and delivery reliability are two major aspects to consider in evaluating the delivery capability dimension. Reliable on-time delivery is particularly important in global manufacturing operations because it involves movement of materials between plants for processing. For long-haul transportation, on-time delivery should imply on-time arrival and receipt of goods by the customers rather than ex-factory. It depends on production and delivery lead times. Flexibility measurement accounts for both process and product flexibility, with emphasis on new product introduction. Table 1 shows the relationship between performance measures and the production process using the steel industry as an example. It includes all the major processes associated with steel-making to maintain the generality. However, any steps not relevant to a particular situation can be omitted during the implementation of the model.
2.3. Evaluation technique The inclusion of several multidimensional variables is advocated because single item indicators limit the generalisability and the fail to capture the complexity of manufacturing competitive priorities [10]. However, there is no one right way to combine these multidimensional variables to produce a meaningful single indicator to represent a complex system or to make comparisons. The evaluation of manufacturing capabilities requires the development of a multi-attribute analytical method which quantitatively accounts and relates various interdependent performance and process factors to a single score. Analytic hierarchy process (AHP) [12] offers a technique that is consistent with our requirements. AHP attempts to decompose a complex multiple-factor problem in a hierarchical manner. At the highest top level of the hierarchy is the overall objective, with next levels representing the criteria and sub-criteria upon which the outcome of the objective depends. The relative importance of these criteria are calculated using pair-wise comparison where the relative
; ; ; ;
;
;
;
; ; ; ;
;
;
;
;
;
; ;
; ; ;
;
;
;
;
; ;
;
;
;
;
; ; ; ; ; ;
units/t
;
t/man
units/t
units/t
; ;
; ; ; ;
; ;
; ;
; ; ;
%
; ;
; ; ; ;
; ;
; ;
; ; ;
%
CU/PU FTC
Dist.to/ VQR from core mkts. km %
Energy
Materials
Plant cons.
In-bound In-process
Transp.
Manufacturing Labour
Quality
Cost
VQR"Vendor Quality Rating CU/PU"Conforming Units/Produced Units FTC"First Time Correct ‘;’"denotes postions where indicators could be assigned.
Coil build up Annealing Cold rolling Final anne. Bright anne. Coiling Slitting Packaging
Steelmelting Ladle Casting Reheating Slabbing mill Roughing mill Shear Finishing mill Coiling
Process
Table 1 Performance measures in steel-making
;
;
;
;
;
;
;
;
Complaints Vendor on time del. perf. no. %
Out-bound Incoming
Delivery
; ; ; ; ; ; ;
; ;
; ; ; ; ; ;
min.
Prod. time
Prod
;
;
;
;
;
;
;
;
days %
Lead On time time delivery
Delivery
; ;
;
; ;
no.
Products
Product
Flexibility
; ;
;
;
New prod. per year no.
;
;
; ;
;
;
min.
Set-up time
Volume
264 E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
importance of one criteria or sub-criteria with respect to the next higher level is compared with another based on a relative scale of 1 to 9. The lowest level is composed of decision alternatives. AHP can be successfully applied in a variety of production and operations management problems from product design and supplier selection to bench marking [13]. Rangone [14] has developed a framework to compare the performance of manufacturing departments using this technique while Lee et al. [15] have developed a business performance evaluation system.
2.4. Modelling process This model differs from other work in the area in that it attempts to measure and compare manufacturing performance at each major stage of the production process route as well as taking the entire production route as a whole in a global context. This is to enable a company to compare the manufacturing performances of different production route combinations encompassing different plants in different countries. In order to demonstrate the modelling process we use a simplified hypothetical example. Take the case of the steel industry. Steel is an industry with marginal growth prospects in the developed world but with vast opportunities globally, particularly in the Far East [16]. Consider the scenario where a company has the following optional routes for producing and supplying products to a particular country in the Far East (Fig. 2). We take the example where the company wants to compare the performance of these two routes at the final stage of the production, i.e. cold rolling and customising.
Fig. 2. Example — alternative production routes.
265
We use the hypothetical manufacturing data (i.e. c , q1 , c , q1 ,2 etc.) for the analysis as shown in Fig. 3. These data represent the cumulative effect of the outputs of preceding production steps. For example, the cost parameter at this stage includes all the manufacturing costs, transportation costs as well as tariffs and taxes incurred in the preceding production steps. 2.4.1. Prioritisation Once the hierarchy is developed it is necessary to decide the relative importance of each of these competitive priorities, namely cost, quality, delivery and flexibility based of on the requirements of the market. Under this, the user is expected to provide as input the relative importance of each competitive priority with respect to others using his knowledge about the market based on the abovementioned scale. For example, if the user assumes that cost is more strongly important than the flexibility, a score of 5 is given for this comparison. An example using notations as relative importance scores (i.e. a, b, c2etc.) is shown in Fig. 4. The AHP provides the technique to translate these paired comparison data into absolute weights to represent the overall relative importance of each of the priorities with respect to the market. Similarly, with respect to the sub-criteria coming under the competitive priorities, relative importance comparisons are made based on industrial requirements. 2.4.2. Ranking the process routes based on performance measures The next step in this technique is to assess the performance of each route with respect to each performance measure. The AHP technique facilitates analysis of both qualitative and quantitative data. Qualitative data is analysed using the same method described above. However, in our model we mostly use quantitative data and they are analysed in a slightly different way. This is demonstrated here using one measurement, on-time delivery performance. Table 2 analyses the percentage on-time deliveries made by the specific plant in each route. These data are then normalised and the results can be used for the comparison of two routes. The normalisation procedure is adopted
266
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
Fig. 3. Manufacturing data.
Fig. 4. Prioritisation procedure.
Table 2 Normalisation of data
Route 1 Route 2
% on time Normalised value deliveries
Notation used in the example
d4 d4
nd4 nd4
d4 /(d4 #d4 #d4 ) d4 /(d4 #d4 )
here because of the necessity to allow different types to be integrated together in order to arrive at an overall score for the system. This procedure is repeated for the other measures as well except for the ones where the measures are inversely proportional to better performance, for example cost. In such cases the
reciprocal of the value is normalised in order to maintain the consistency of the comparison. The complete picture of the hierarchy after the prioritisation and normalisation procedures is shown in Fig. 5. The final ratings are calculated by taking the summation of each normalised value of performance multiplied by the weight of the corresponding criteria or sub-criteria in the next highest level, starting from the lowest level (Fig. 6). This model provides the facility to compare the performances at the highest level competitive priorities as well as at individual components of them. The major feature of the model is the ability to make comparisons at each stage of the production process taking to the cumulative effect of the preceding production stages. This will assist companies in assigning specific productions steps to plants taking into account the effect to the overall performance of the entire system.
3. Manufacturing partnership 3.1. Partnership and synergy Over the last three decades, research issues associated with global manufacturing alliance and
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
267
Fig. 5. Data after prioritisation and normalisation.
Fig. 6. Overall ratings.
partnership have been dispersedly addressed. They have been included in the studies of plant location decisions, diversification strategy, acquisitions, vertical integration and the configuration and coordination decisions in global manufacturing, particularly in the studies of international joint ventures [17]. Most of them examine partnering issues within a broad environmental context taking account of locational or macrovariables. These research interests have paralleled a main concern about economic explanations for foreign direct in-
vestment. However, many of the works were concentrated on the formulation of strategy. There is a lack of efforts dedicated to implementation [18,19]. Furthermore, the fundamental conditions for global manufacturing alliances have significantly changed over the last two decades [6]. Thus, we would need to develop a full and rich understanding of the partnering process and an implemental methodology to explore for the external economies of interdependent activities in global manufacturing.
268
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
Partnership is an inter-business relationship which involves sharing and pooling of resources in order to: E reduce co-ordination and transaction costs, E concentrate on critical competency, E seek for additional assets and/or markets. Depending on the nature of the shared resources and the extent of sharing, partnership can be built on different levels of co-operation (e.g., vendor/customer, licenser/licensee, contractor/supplier and co-manufacturers). Very often, sharing a resource may provide positive contribution to one partner but negative to another. A successful partnership must lead to an overall win/win situation for all the partners. Synergy is the potential ability to synthesise individual resources so that the aggregate effect of sharing resources could be greater than the sum of individual contributions. The aggregate effect depends on the magnitude, relative significance and the complementary/supplementary, conflicting or counteracting characteristics of the contributions to its owners and to the partnership.
3.2. Modelling synergy The synergy evaluation model is designed to measure and compare, on a relative basis, essential synergistic effects resulted from sharing resources in different forms of partnership. It would assist partnership selection, and would provide a common understanding and means of communication in the formulation of partnership. Fig. 7 shows the modelling framework for partnership synergy evaluation. A and B are the two companies which are seeking for a partnership in the form of C by sharing/pooling resources together. Each of them has identified their strategies. They recognise their individual likes and dislikes. The flow of resources and their complementary/conflicting nature would provide a measure of the synergy of partnership. Two types of complementary evaluation methods developed by the major rating agencies can be used to ensure consistency in the partner rating process. One of them is from multi-variate
Fig. 7. Partnership synergy modelling framework.
statistical classification models typically used to assess economic and political risk, and the other is a judgmental method which assigns country risk ratings.
4. Conclusion The above two models represent generic frameworks that can be used by companies in different industries. Even though the models are of a quantitative nature in general, qualitative parameters can also be accommodated. The manufacturing capabilities model will be validated using industrial data from the Chinese steel industry. In addition to the general business and performance related factors discussed above, a company involved in globalised operations is directly or indirectly affected by other external factors such as political stability in the host countries, exchange rate fluctuations and environmental regulations, etc. Due consideration should be given to these factors also in order to arrive at a complete analysis. We expect to develop general guide lines in this respect also.
Acknowledgements This project is funded by EPSRC Grant No. GR/K 82512 and is supported by four major steel
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
companies, namely, Avesta Steel, Sheffield Forgemasters, British Steel Engineering Steels and Davy International. We gratefully acknowledge their support. References [1] G.G. Dess, A. Gupta, J.F. Hennart, C.W.L. Hill, Conducting and integrating strategy research at the international, corporate, and business levels: Issues and directions, Journal of Management 21 (3) (1995) 357—394. [2] E.D. Minor III, R.L. Hensley, D.R. Wood Jr., A review of empirical manufacturing strategy studies, International Journal of Operations and Production Management 14 (1) (1994) 2—25. [3] T. Minahan, What drives the supply chain, Purchasing 120 (11) (1996) 54—58. [4] P. Cocks, Partnership in pursuit of lean supply, Purchasing and Supply Management (2) (1996) 32—33. [5] N. Rackham, L. Friedman, R. Ruff, Getting Partnering Right, McGraw-Hill, New York, 1996. [6] J.H. Dunning, Re-appearing the eclectic paradigm in an age of alliance capitalism, Journal of International Business Studies, third quarter (1995) 461—491. [7] A.M. Ghalayini, J.S. Noble, The changing basis of performance measurement, International Journal of Operations and Production Management 16 (8) (1996) 63—80. [8] R.W. Schmenner, T.E. Vollmann, Performance measuresgaps, false alarms and the ‘usual suspects’, International Journal of Operations and Production Management 14 (12) (1994) 283—295.
269
[9] B. Maskell, Performance measurement for world class manufacturing. Articles 1-4, Management Accounting 67 (5) (1989). [10] M.A. Noble, Manufacturing strategy: Testing the cumulative model in a multiple country context, Decision Sciences 26 (5) (1995) 693—721. [11] A. De Toni, G. Nassimbeni, S. Tonchia, An Instrument for Quality Performance Measurement, International Journal of Production Economics 38 (1995) 199—207. [12] T.L. Saaty, The Analytic Hierarchy Process, McGrawHill, New York, 1989. [13] F.Y. Partovi, J. Burton, A. Benerjee, Application of Analytical Hierarchy Process in Operations Management, International Journal of Operations and Production Management 10 (3) (1990) 5—19. [14] A. Rangone, An analytical hierarchy process for comparing the overall performance of manufacturing departments, International Journal of Operations and Production Management 16 (8) (1996) 104—119. [15] H. Lee, W. Kwak, I. Han, Developing a business performance evaluation system: An analytic hierarchical model, The Engineering Economist 40 (4) (1995) 343—357. [16] C. Pushpakumara, E.K. Lo, Capabilities evaluation in global steel-making, in: Proceedings of the 32nd International MATADOR Conference, UMIST, Manchester, 1997. [17] D.A. Ricks, B. Toyne, Z. Martinez, Recent developments in international management research, Journal of Management 16 (2) (1990) 219—244. [18] A. Rock, Strategy vs. tactics from a venture capitalist, Harvard Business Review, Nov. —Dec. (1987) 63. [19] B. Taylor, Corporate planning for the 1990s: The new frontiers, Long Range Planning 19 (12) (1986) 13—18.
Performance and partnership in global manufacturing-modelling frameworks and techniques Edmond K. Lo*, Chamli Pushpakumara School of Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK
Abstract Manufacturing globalization warrants researches in the two important related areas. First, the evaluation and comparison of performance based on the structure and future requirements of an industry. Second, the evaluation of essential synergistic effects resulted from sharing of resources in different forms of partnerships. This paper presents two related analytical models to quantify manufacturing performance and partnership synergy in a global context. It will analyse the related literature, outline model frameworks and evaluation methodologies, and illustrate the applicability. 1999 Elsevier Science B.V. All rights reserved. Keywords: Global manufacturing; Capability; Performance; Partnership; Synergy
1. Introduction Many companies perceive global manufacturing as integral to their strategy for improved business performance and growth. Encompassed within this strategy is clearly the opportunity to access new markets, but of equal importance are the opportunities to explore and pursue joint venture and co-production activities. In the domain of corporate strategy, the study of corporate diversification has been a central theme and the relationship between performance and diversification has received the most research attention, and some studies have touched on the issues of alliance synergy in diversification processes [1]. Two interrelated areas of
* Corresponding author. Tel.: 00 44 114 225 3419; fax: 00 44 114 225 3433; e-mail: [email protected].
work are of particular importance in the study of Global Manufacturing. First, the evaluation and comparison of manufacturing competitiveness based on the structure and future requirements of an industry. Second, the identification and evaluation of the synergy of partnership between comanufacturers and vendor/suppliers co-operating in a global supply chain. With the growing interest in manufacturing as a competitive weapon, literature is abound on the subject of manufacturing strategy and manufacturing competitiveness, addressing both process and content issues [2]. At the early stages most of them were of conceptual nature. But now there is evidence of an increase in the number of empirical studies as well. Most studies associated with the evaluation of global manufacturing performance were focused on macroeconomic level using a country as the unit of study. The results are somewhat
0925-5273/99/$ - see front matter 1999 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 5 2 7 3 ( 9 8 ) 0 0 1 5 0 - 9
262
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
inadequate for companies which require specific and structured information that would enable them to analyse their competitive position in a changing industry within the overall context of a combination of industrial, national and global economic trends. Furthermore, improving internal efficiency alone is no longer sufficient in ensuring global competitiveness. As activities performed by suppliers and users within a supply chain often account for 60—80% of the total manufacturing cost [3,4], companies have to work together beyond the traditional boundary of transactional, short-term business arrangements and into more durable partnering relationships. Partnering promises greater productivity, reduced costs and new marketplace value [5]. Its importance in manufacturing today warrants explicit evaluation of the performance improvements arisen from the synergy of the global manufacturing partnership [6]. Two related analytical models which quantify global manufacturing performance and partnership synergy are currently developed at Sheffield Hallam University. The first model aims to measure, compare and project manufacturing performances of a company if it supplies to a particular market using products manufactured in different locations, taking into account of global development in related industrial sub-sectors. The second model attempts to evaluate quantitatively and qualitatively the manufacturing synergy between vendors and customers, contractors and suppliers, licensers and licensees, and co-manufacturers. It is based on the scenario that a company has identified a number of potential collaborating companies and is in the process of selecting among them a suitable partner for an appropriate operation mode. The model aims to develop an analytical tool to measure, on a relative basis, essential synergy factors identified for a particular partnership, and to compare these factors with corresponding measures calculated for an alternative partnership combination. 2. Manufacturing capability evaluation 2.1. Modelling framework Fig. 1 shows the generic framework based on which the Global Manufacturing Capability Model
Fig. 1. Manufacturing capability model framework.
is developed. The model is conceptually represented by a cuboid whose three axes relate respectively to performance, process and potential. The overall manufacturing performance of a company studied in terms of its abilities to perform major steps of production can be used as an indicator for the measurement and comparison of its manufacturing capabilities. Therefore manufacturing performance measures and the production process are the two key elements in the study of manufacturing capabilities in general. The ‘process’ axis is industry-specific. It represents major process routes which have significant effects on the global manufacturing performance of an industry or a company. “Potential” axis represents emerging changes resulted from industrial factors such as technological advances, product life cycle and/or environmental restriction. Potentials can be positive or negative, and may lead to future changes in performance and hence competitiveness. Performance measures can be analysed in a hierarchical manner. Industrial factors become increasingly process-specific as they extend down the hierarchy. The relative importance of individual performance measures varies according to the structure of the industry in a particular region. Within an organization, the effect of different processes on each performance measure also varies
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
depending on factors such as cost structure and process capability.
2.2. Performance analysis Initial studies on performance measures emphasised on accounting factors extracted from financial reports [7]. However, they represent past cost performance and do not account for other intangible and qualitative information which are essential in today’s global market. Partial measurement may lead to incorrect conclusions [8]. Consequently, it is essential to evaluate global manufacturing capability based on a comprehensive performance measurement system that encompasses both financial and non-financial measures in a manner appropriate to a particular industry [9]. In this study, manufacturing performance is analysed under the four competitive priorities of cost, quality, delivery and flexibility. Better performing companies generally compete on multiple capabilities rather than focusing on one or two [10]. Manufacturing cost and transportation cost are considered to be the primary elements for cost evaluation. Manufacturing cost composes of materials cost, energy cost, labour cost and plant consumable cost. It can be expressed in non-financial productivity parameters. Compared with monetary figures, productivity parameters are processoriented and less commercially sensitive. They are converted to monetary data specific to a location using published data. Considering transportation costs is vital in globalised operations because any reduction in production costs achieved by the global shifting of manufacturing activities could substantially be offset by transportation costs involved. Quality measures can be classified according to in-bound, in-process and out-bound qualities to account respectively for the incoming materials, in-process parts and finished products [11]. Potentially, the inbound quality can be measured by a consistency factor which describes the variance in quality of different lots of materials purchased for a particular application. In-process quality can be represented by parameters such as percentage of products acceptable first time without re-work.
263
Product range, specification, frequency of customer complaints and the nature of the customer base provide a base to measure outbound quality. Delivery speed and delivery reliability are two major aspects to consider in evaluating the delivery capability dimension. Reliable on-time delivery is particularly important in global manufacturing operations because it involves movement of materials between plants for processing. For long-haul transportation, on-time delivery should imply on-time arrival and receipt of goods by the customers rather than ex-factory. It depends on production and delivery lead times. Flexibility measurement accounts for both process and product flexibility, with emphasis on new product introduction. Table 1 shows the relationship between performance measures and the production process using the steel industry as an example. It includes all the major processes associated with steel-making to maintain the generality. However, any steps not relevant to a particular situation can be omitted during the implementation of the model.
2.3. Evaluation technique The inclusion of several multidimensional variables is advocated because single item indicators limit the generalisability and the fail to capture the complexity of manufacturing competitive priorities [10]. However, there is no one right way to combine these multidimensional variables to produce a meaningful single indicator to represent a complex system or to make comparisons. The evaluation of manufacturing capabilities requires the development of a multi-attribute analytical method which quantitatively accounts and relates various interdependent performance and process factors to a single score. Analytic hierarchy process (AHP) [12] offers a technique that is consistent with our requirements. AHP attempts to decompose a complex multiple-factor problem in a hierarchical manner. At the highest top level of the hierarchy is the overall objective, with next levels representing the criteria and sub-criteria upon which the outcome of the objective depends. The relative importance of these criteria are calculated using pair-wise comparison where the relative
; ; ; ;
;
;
;
; ; ; ;
;
;
;
;
;
; ;
; ; ;
;
;
;
;
; ;
;
;
;
;
; ; ; ; ; ;
units/t
;
t/man
units/t
units/t
; ;
; ; ; ;
; ;
; ;
; ; ;
%
; ;
; ; ; ;
; ;
; ;
; ; ;
%
CU/PU FTC
Dist.to/ VQR from core mkts. km %
Energy
Materials
Plant cons.
In-bound In-process
Transp.
Manufacturing Labour
Quality
Cost
VQR"Vendor Quality Rating CU/PU"Conforming Units/Produced Units FTC"First Time Correct ‘;’"denotes postions where indicators could be assigned.
Coil build up Annealing Cold rolling Final anne. Bright anne. Coiling Slitting Packaging
Steelmelting Ladle Casting Reheating Slabbing mill Roughing mill Shear Finishing mill Coiling
Process
Table 1 Performance measures in steel-making
;
;
;
;
;
;
;
;
Complaints Vendor on time del. perf. no. %
Out-bound Incoming
Delivery
; ; ; ; ; ; ;
; ;
; ; ; ; ; ;
min.
Prod. time
Prod
;
;
;
;
;
;
;
;
days %
Lead On time time delivery
Delivery
; ;
;
; ;
no.
Products
Product
Flexibility
; ;
;
;
New prod. per year no.
;
;
; ;
;
;
min.
Set-up time
Volume
264 E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
importance of one criteria or sub-criteria with respect to the next higher level is compared with another based on a relative scale of 1 to 9. The lowest level is composed of decision alternatives. AHP can be successfully applied in a variety of production and operations management problems from product design and supplier selection to bench marking [13]. Rangone [14] has developed a framework to compare the performance of manufacturing departments using this technique while Lee et al. [15] have developed a business performance evaluation system.
2.4. Modelling process This model differs from other work in the area in that it attempts to measure and compare manufacturing performance at each major stage of the production process route as well as taking the entire production route as a whole in a global context. This is to enable a company to compare the manufacturing performances of different production route combinations encompassing different plants in different countries. In order to demonstrate the modelling process we use a simplified hypothetical example. Take the case of the steel industry. Steel is an industry with marginal growth prospects in the developed world but with vast opportunities globally, particularly in the Far East [16]. Consider the scenario where a company has the following optional routes for producing and supplying products to a particular country in the Far East (Fig. 2). We take the example where the company wants to compare the performance of these two routes at the final stage of the production, i.e. cold rolling and customising.
Fig. 2. Example — alternative production routes.
265
We use the hypothetical manufacturing data (i.e. c , q1 , c , q1 ,2 etc.) for the analysis as shown in Fig. 3. These data represent the cumulative effect of the outputs of preceding production steps. For example, the cost parameter at this stage includes all the manufacturing costs, transportation costs as well as tariffs and taxes incurred in the preceding production steps. 2.4.1. Prioritisation Once the hierarchy is developed it is necessary to decide the relative importance of each of these competitive priorities, namely cost, quality, delivery and flexibility based of on the requirements of the market. Under this, the user is expected to provide as input the relative importance of each competitive priority with respect to others using his knowledge about the market based on the abovementioned scale. For example, if the user assumes that cost is more strongly important than the flexibility, a score of 5 is given for this comparison. An example using notations as relative importance scores (i.e. a, b, c2etc.) is shown in Fig. 4. The AHP provides the technique to translate these paired comparison data into absolute weights to represent the overall relative importance of each of the priorities with respect to the market. Similarly, with respect to the sub-criteria coming under the competitive priorities, relative importance comparisons are made based on industrial requirements. 2.4.2. Ranking the process routes based on performance measures The next step in this technique is to assess the performance of each route with respect to each performance measure. The AHP technique facilitates analysis of both qualitative and quantitative data. Qualitative data is analysed using the same method described above. However, in our model we mostly use quantitative data and they are analysed in a slightly different way. This is demonstrated here using one measurement, on-time delivery performance. Table 2 analyses the percentage on-time deliveries made by the specific plant in each route. These data are then normalised and the results can be used for the comparison of two routes. The normalisation procedure is adopted
266
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
Fig. 3. Manufacturing data.
Fig. 4. Prioritisation procedure.
Table 2 Normalisation of data
Route 1 Route 2
% on time Normalised value deliveries
Notation used in the example
d4 d4
nd4 nd4
d4 /(d4 #d4 #d4 ) d4 /(d4 #d4 )
here because of the necessity to allow different types to be integrated together in order to arrive at an overall score for the system. This procedure is repeated for the other measures as well except for the ones where the measures are inversely proportional to better performance, for example cost. In such cases the
reciprocal of the value is normalised in order to maintain the consistency of the comparison. The complete picture of the hierarchy after the prioritisation and normalisation procedures is shown in Fig. 5. The final ratings are calculated by taking the summation of each normalised value of performance multiplied by the weight of the corresponding criteria or sub-criteria in the next highest level, starting from the lowest level (Fig. 6). This model provides the facility to compare the performances at the highest level competitive priorities as well as at individual components of them. The major feature of the model is the ability to make comparisons at each stage of the production process taking to the cumulative effect of the preceding production stages. This will assist companies in assigning specific productions steps to plants taking into account the effect to the overall performance of the entire system.
3. Manufacturing partnership 3.1. Partnership and synergy Over the last three decades, research issues associated with global manufacturing alliance and
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
267
Fig. 5. Data after prioritisation and normalisation.
Fig. 6. Overall ratings.
partnership have been dispersedly addressed. They have been included in the studies of plant location decisions, diversification strategy, acquisitions, vertical integration and the configuration and coordination decisions in global manufacturing, particularly in the studies of international joint ventures [17]. Most of them examine partnering issues within a broad environmental context taking account of locational or macrovariables. These research interests have paralleled a main concern about economic explanations for foreign direct in-
vestment. However, many of the works were concentrated on the formulation of strategy. There is a lack of efforts dedicated to implementation [18,19]. Furthermore, the fundamental conditions for global manufacturing alliances have significantly changed over the last two decades [6]. Thus, we would need to develop a full and rich understanding of the partnering process and an implemental methodology to explore for the external economies of interdependent activities in global manufacturing.
268
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
Partnership is an inter-business relationship which involves sharing and pooling of resources in order to: E reduce co-ordination and transaction costs, E concentrate on critical competency, E seek for additional assets and/or markets. Depending on the nature of the shared resources and the extent of sharing, partnership can be built on different levels of co-operation (e.g., vendor/customer, licenser/licensee, contractor/supplier and co-manufacturers). Very often, sharing a resource may provide positive contribution to one partner but negative to another. A successful partnership must lead to an overall win/win situation for all the partners. Synergy is the potential ability to synthesise individual resources so that the aggregate effect of sharing resources could be greater than the sum of individual contributions. The aggregate effect depends on the magnitude, relative significance and the complementary/supplementary, conflicting or counteracting characteristics of the contributions to its owners and to the partnership.
3.2. Modelling synergy The synergy evaluation model is designed to measure and compare, on a relative basis, essential synergistic effects resulted from sharing resources in different forms of partnership. It would assist partnership selection, and would provide a common understanding and means of communication in the formulation of partnership. Fig. 7 shows the modelling framework for partnership synergy evaluation. A and B are the two companies which are seeking for a partnership in the form of C by sharing/pooling resources together. Each of them has identified their strategies. They recognise their individual likes and dislikes. The flow of resources and their complementary/conflicting nature would provide a measure of the synergy of partnership. Two types of complementary evaluation methods developed by the major rating agencies can be used to ensure consistency in the partner rating process. One of them is from multi-variate
Fig. 7. Partnership synergy modelling framework.
statistical classification models typically used to assess economic and political risk, and the other is a judgmental method which assigns country risk ratings.
4. Conclusion The above two models represent generic frameworks that can be used by companies in different industries. Even though the models are of a quantitative nature in general, qualitative parameters can also be accommodated. The manufacturing capabilities model will be validated using industrial data from the Chinese steel industry. In addition to the general business and performance related factors discussed above, a company involved in globalised operations is directly or indirectly affected by other external factors such as political stability in the host countries, exchange rate fluctuations and environmental regulations, etc. Due consideration should be given to these factors also in order to arrive at a complete analysis. We expect to develop general guide lines in this respect also.
Acknowledgements This project is funded by EPSRC Grant No. GR/K 82512 and is supported by four major steel
E.K. Lo, C. Pushpakumara/Int. J. Production Economics 60—61 (1999) 261—269
companies, namely, Avesta Steel, Sheffield Forgemasters, British Steel Engineering Steels and Davy International. We gratefully acknowledge their support. References [1] G.G. Dess, A. Gupta, J.F. Hennart, C.W.L. Hill, Conducting and integrating strategy research at the international, corporate, and business levels: Issues and directions, Journal of Management 21 (3) (1995) 357—394. [2] E.D. Minor III, R.L. Hensley, D.R. Wood Jr., A review of empirical manufacturing strategy studies, International Journal of Operations and Production Management 14 (1) (1994) 2—25. [3] T. Minahan, What drives the supply chain, Purchasing 120 (11) (1996) 54—58. [4] P. Cocks, Partnership in pursuit of lean supply, Purchasing and Supply Management (2) (1996) 32—33. [5] N. Rackham, L. Friedman, R. Ruff, Getting Partnering Right, McGraw-Hill, New York, 1996. [6] J.H. Dunning, Re-appearing the eclectic paradigm in an age of alliance capitalism, Journal of International Business Studies, third quarter (1995) 461—491. [7] A.M. Ghalayini, J.S. Noble, The changing basis of performance measurement, International Journal of Operations and Production Management 16 (8) (1996) 63—80. [8] R.W. Schmenner, T.E. Vollmann, Performance measuresgaps, false alarms and the ‘usual suspects’, International Journal of Operations and Production Management 14 (12) (1994) 283—295.
269
[9] B. Maskell, Performance measurement for world class manufacturing. Articles 1-4, Management Accounting 67 (5) (1989). [10] M.A. Noble, Manufacturing strategy: Testing the cumulative model in a multiple country context, Decision Sciences 26 (5) (1995) 693—721. [11] A. De Toni, G. Nassimbeni, S. Tonchia, An Instrument for Quality Performance Measurement, International Journal of Production Economics 38 (1995) 199—207. [12] T.L. Saaty, The Analytic Hierarchy Process, McGrawHill, New York, 1989. [13] F.Y. Partovi, J. Burton, A. Benerjee, Application of Analytical Hierarchy Process in Operations Management, International Journal of Operations and Production Management 10 (3) (1990) 5—19. [14] A. Rangone, An analytical hierarchy process for comparing the overall performance of manufacturing departments, International Journal of Operations and Production Management 16 (8) (1996) 104—119. [15] H. Lee, W. Kwak, I. Han, Developing a business performance evaluation system: An analytic hierarchical model, The Engineering Economist 40 (4) (1995) 343—357. [16] C. Pushpakumara, E.K. Lo, Capabilities evaluation in global steel-making, in: Proceedings of the 32nd International MATADOR Conference, UMIST, Manchester, 1997. [17] D.A. Ricks, B. Toyne, Z. Martinez, Recent developments in international management research, Journal of Management 16 (2) (1990) 219—244. [18] A. Rock, Strategy vs. tactics from a venture capitalist, Harvard Business Review, Nov. —Dec. (1987) 63. [19] B. Taylor, Corporate planning for the 1990s: The new frontiers, Long Range Planning 19 (12) (1986) 13—18.