Role of manufacturing logistics in Indian automobile industries--a case study.
The general opinion about many people thought that logistics means only warehouses and Lorries. However, the real logistics is concerned with all those activities of an enterprise, which ensure that customers are given total satisfaction at minimum cost. This means that almost every function of an organization is involved in the logistics process. In complex manufacturing processes with many different product lines, it is not possible to respond fast enough to match the customer's demand exactly day by day. Hence, the firms need for finished goods stocks. Ballou RH .
However, it is possible to improve the responsiveness, and hence reduce the size of the stocks, by careful control of the shop floor processes. Martin Christopher . This might mean working to reduce batch sizes and speed up changeovers, or it might mean improving forecasting methods so that output can be adjusted in advance of a rise or fall in demand. Benjamin S. Blanchard  & Blanchard and Fabbrycky . Majority of the manufacturing organizations are manufacturing their products without considering the logistic parameters. According to study, it was observed that the logistic features are available with them either directly or indirectly in doing their manufacturing activities.
However, larger manufacturing firms are having separate department for logistics and smaller manufacturing firms are giving least importance over logistics. The paper supports the trade-off between smaller and larger firms for easy implementation of logistic design.
Objectives and Organization of the Paper
The objective of this paper is threefold
 To demonstrate the importance of early logistics involvement in the product design and development process.
 To present a conceptual as well as an analytical basis for integrating logistics concerns, constraints, and contributions in the design process.
 To provide a product design conceptual framework, where managerial implications of design for logistics can be explored.
The efficiency and effectiveness of the methodology, results, and their managerial implications are analyzed. Section 3 gives brief theoretical issues of interest when discussing product design under concurrent engineering environment and presents a logistic design framework model for integrated product design. Section 4 comprises the application of cluster algorithms and presented a Concurrent Engineering Model (CEM), which was adopted with a case study and its results. Finally Section 5 implies the concluding section with the theoretical/managerial implications of process model and gives recommendations for further research.
Product Design in a Concurrent Engineering Environment
It focuses on an interdisciplinary approach that utilizes methods, procedures and rules to plan, analyze, select, and optimize the design of products. In the early stages of the design process, concurrent engineering considers and includes various product design attributes such as aesthetics, durability, ergonomics, interchangeability, logistics, maintainability, marketability, manufacturability, procurability, reliability, remanufacturability, Safety, schedulability, serviceability, simplicity, testability and transportability. Biren Prasad . The greatest impact and benefits of concurrent engineering are realized at the design stage of product development. This paper supports the logistics involvement in the early phases of product design and development in a concurrent engineering environment. The concurrent engineering environment and the benefits of such involvement are considered in detail. Gnanasekaran , V. Kovaicheliyan  & Willard I. Zangwill .The research facilitates the design interface between the designer and the logistician. A quantitative and conceptual interface of design for logistics is considered in the four areas of interfaces i.e. Logistics Engineering, Manufacturing logistics, Design for packaging, and Design for transportability. Modularity, as a basic rule of good design, is more easily changed, expanded, or contracted than large and complex system designs.
Most managers find modular system designs easier to understand and apply. Andrew Kusiak  &Gnanasekaran  . The module families can be designed according to the design for logistics and implemented simultaneously. This can potentially reduce the total design cycle time and bring about the merits of concurrent engineering to the design of integrated logistics. This paper has specifically explored a large number of areas where collaboration and interface of logistics and design activities can result in significant achievements for a manufacturing enterprise.
Logistic Design Framework Model for Integrated Product Design.
There are two issues at the core of successful implementation of concurrent engineering:
 All activities related to the development of a product should be focused in the early stages of product design, so that the greatest benefits of such integration are achieved. The information requirements and exchanges at the conceptual design are not well defined and usually fuzzy. This poses a challenge for implementing concurrent engineering.
 The impact and constraints associated with various functional requirements should be communicated to the designer on a timely, accurate, and relevant basis.
Figure 1 represents an integrated logistics system as it relates to product design. An effective design for logistics cuts across a number of functional areas as illustrated. These activities converge to product design as the embodiment of all future activities. As the design for logistics affects other functional areas, other areas in turn affect logistics considerations. This process is inherently a dynamic one requiring negotiation and trade-off among the functional areas in a concurrent engineering environment. Different functional areas and their interrelationships can be explored and culminated into several other research articles. The focus of this paper, however, is on exploring and analyzing the link between logistics and product design.
[FIGURE 1 OMITTED]
Product Design Conceptual Logistic Frame Work
The system approach to design for logistics is essentially includes the designer's functional requirements as well as the logistician's requirements of availability, supportability, cost, quality, volume changes, timely delivery, order frequencies etc. The design for logistics is decomposed into four subsystems. They are Logistics Engineering, Manufacturing Logistics, Design for Packing, and Design for transportability. To achieve the flexibility, design economics, and overall design optimization, the design for logistics needs to be decomposed further into manageable and homogeneous units for processing the data.
Logistic Design Parameters
In addition to above, the manageable and homogeneous units are named here as modules for the simplicity purposes. Therefore, each subsystem is divided into modules. Each subsystem can be composed of several modules. Modules are the building blocks of design for logistics. Each module is further decomposed into design parameters. Each module, then, has several respective design parameters that must be considered in the design of that particular module. Design parameters are the smallest functional requirements in the overall design of logistics. The detailed design parameters were collected from various units of a manufacturing and service industry under on site and off site locations. About two hundred design parameters were collected and taken for the analysis. The various modules under different subsystems of the design for logistics were also found. The collection of design parameters is important and appropriateness to the product being manufactured in the organization according to the design requirement, since it is based on the decisions of designers and logistician.
Decomposition process is defined as a process that breaks down a task or problem into a set of independent entities. There are two classes of decomposition methods. They are Formal methods and intuitive methods. Formal methods decompose a problem based on its mathematical representation. Understanding the physics or functions of the system is the prime factor directing decomposition in intuitive methods. The latter methods provide an alternative for decomposing those problems that do not possess a structure for which formal decomposition methods exist. Decomposition has been applied in many areas ranging from medicine and biology, to computer science and manufacturing. In manufacturing, decomposition has been used under the term group technology. Decomposition simplifies the design process and allows one to determine a potential group of tasks that might be performed simultaneously. As a result of decomposition, the design and manufacturing cycle time can be reduced. Another advantage of applying decomposition in design is simplification of scheduling and management of design and manufacturing projects. Andrew Kusiak 
Group Technology in Concurrent Engineering
Batch manufacturing is a dominant activity in the world, generating much industrial output. The major characteristics of batch manufacturing are a level of product variety and small manufacturing lot sizes. The product variations present design engineers with the problem of a design stage that significantly affects manufacturing cost, quality and delivery times.
The impacts of these product variations in manufacturing are high investment in equipment, high tooling costs, complex scheduling and loading, lengthy set-up times and costs, excessive scrap and high quality control costs. However, to compete in a global market, it is essential to improve the productivity in small batch manufacturing industries.
Concurrent Engineering Model (CEM) Analysis
In this paper, the complexity of design problems were decomposed into simplified sub design problems by using a new concept on CEM Analysis. This can be measured by using an optimizing procedure by maximizing some Measure of Effectiveness (MOE).
The forming of these cluster families allows a designer to concurrently consider and design the design parameters common to a set of modules. This modularized approach increases the efficiency of the logistics design. The interactions between modules and design parameters can be represented in a binary module design parameter incidence matrix. The CEM analysis integrated with an optimization approach to minimize the design cycle time. The CEM model designed for this research as shown in Figure 2.
The objective of this research is to develop a simultaneous optimization model of a product design systems, so as to minimize the design cycle time and maximize the system effectiveness with respect to all decision variables. The model evaluates alternative design solutions by calculating the associated operational effectiveness as well as manufacturing and logistics support systems. The objective function contains two components that are conflicting in nature. The manufacturing organizations are interested in minimizing design cycle time and also in maximizing the system effectiveness. The multi-criteria nature of this optimization is unavoidable. In order to model it, combine these two criteria into a single objective function, i.e. DCT+ MR (SI), where (SI) is the system ineffectiveness and MR is the marginal rate of substitution between DCT and (SI).
In determining an appropriate value for MR, the designers have to specify the tradeoff between DCT and (SI) based on the relative worth of each measure, i.e. maximize system effectiveness subject to a constraint on DCT or minimize DCT subject to a constraint on system effectiveness. The objective function, DCT+ MR (SI), can be separated into individual constituent functions that are combined in stages. Hence it is required to develop an optimization procedure by considering the merit and the behavior of these system and their dependencies on decision variables in order to break down this large optimization problem into manageable sub- problems.
Development of an Optimization Procedure
The optimization procedure is best suited for this research utilizes a Concurrent Engineering Model (CEM) Analysis called Bond Energy Algorithm (BEA), which is a clustering approach to the design for logistics systems. This algorithm maximizes some Measure of Effectiveness (MOE) for the system defined. Conversely, if the Measurement of Effectiveness is maximized of a system, then conceptually, the outcome of the system reduces the design cycle time considerably. Hence no separate optimizations are required for optimizing Design Cycle Time. The existing BEA is modified according to the CEM analysis to enrich the cluster density and enhance the cluster efficiency.
This paper is based on both desk research and field research project into providing a logistics support perspective to the product design process. This desk research has been further developed in cooperation with the field studies through day-to-day work in a manufacturing firm and their suppliers. The objective is to maximize the system effectiveness and minimize the design cycle time.
The existing Cluster Algorithm called Bond Energy Algorithm (BEA), McCormick, W.T  was analyzed for this study and developed a model for enhancing the cluster efficiency and introduced the same for the computational purpose to get rich clusters to the appropriate family of logistic design. An incidence matrix was designed to represent the assignment of design parameters to modules. In the incidence matrix the aij entries correspond to the design parameters available. Entry 1 signifies that the inclusion of a particular design parameter in a module is a necessary and essential requirement of forming that module or 0 otherwise. Each module inherently consists of a set of cohesive and bounded design parameters whose interactions determine the overall design and effectiveness of the module. The design parameters may vary from one design to another depending upon the unique requirements of each logistics system design.
A study was conducted in an automotive industry and tested the logistic design parameters with respect to available subsystems with different modules. The methodology used in this paper generates modules that are cohesive, bounded, or contains a self-contained group of activities. For effective implementation of integrated logistics design, each module solves one clearly defined segment of the total system. These clustering algorithms are used for the decomposition of complex design problems into simple and manageable sub design problems. A Case Study is presented for this problem to test and validate the algorithm.
The study was mainly focused on logistics concerns and supplier activities relationship between a main industry and a chain connecting with many suppliers. The manufacturer follows regular manufacturing practices including Product design and development, tool design, die design, raw material planning, scheduling, inventory, manufacturing, storage and warehousing etc. The problem is to make an interlink between the main industry and segregate the various logistic and logistic design activities involved in manufacturing activities from product design and development and to the disposal of products to the customer. From the above system, every supplier is also having some sub suppliers of various secondary operations of manufacturing components.
The study took all necessary steps to link all the activities held between the manufacturers and suppliers. The above study produced high bond energy clusters by the incremental rise in the system effectiveness when comparing the existing design and proposed the module based logistics design. The data collected for this case study as shown in Table 1. The resulting clusters are as shown in Figure 3. The modular based design approach is constructed for this study relates with logistics and product design. The major steps in system design and development and the interface relationships between the basic design and logistics functions are taken into this study. The principle of concurrent engineering is accomplished through the intensive teamwork between product design and developments, production planning and manufacturing.
In Figure 3, it is apparent that each module family (MF) addresses their respective modules and each design parameter family (DPF) addresses their respective design parameters. The detailed results are given below:--
1. Module Family 1 addresses the supportability and Transportability issues. It consists of design for supportability, Transportation requirements and Transportability design criteria.
2. Module Family 2 addresses the material and manufacturability issues. It consist of Design for manufacturability, manufacturing processes and materials and materials
3. Module Family 3 concentrates on product lines and product planning issues. It consist the product lines, production planning and control and plant location
4. Module Family 4 focuses the Design characteristics. It consists of design attributes, packaging and testing and packaging design features.
5. Module Family 5 concentrates about the packaging criteria. This module consists of Functional packaging requirements, Transportation mode and packaging materials.
The Initial Measurement of System Effectiveness (MSE) Value for this study was obtained is 35 and the final Measurement of System Effectiveness (MSE) obtained was 122 due to the above results. The system effectiveness is maximized and hence the clustering process releases highest bond energy and the solution consist optimal bond energy. The above result shown in Figure 3 indicates that the overall design of logistics can be accomplished in five self-contained clusters or modules. The DPF3 (Design Parameter Family 3) formed as a checkerboard cluster and other families forms body diagonal. The objective of BEA in a logistic design has been attained through clustering.
The design for logistics model results with five module families with related design parameters. By the application of Gantt chart and the application of concurrent engineering principles by overlapping the module family resulted in reducing design cycle time for this study is 1.5 weeks. The total cycle time for the existing system was 13.5 weeks.
[FIGURE 3 OMITTED]
In this research, a methodology is developed and presented for decomposition of the design process. A Concurrent Engineering Model (CEM) is developed and integrated with an enhanced Bond Energy Algorithm (BEA) for this research for analyzing the modular design for logistics. This new approach releases highest bond energy. A logistic engineering model called the Design for Logistics Model for Product Design Framework is developed for this research. The application of CEM and its features are utilized for this research for maximizing the logistic system effectiveness and minimizing the design cycle time for the product design for logistics subject to logistic sub systems, modules and its parameters.
The study conducted was resulted in significant achievements for a manufacturing enterprise through manufacturing logistics. Gnanasekaran . The concurrent engineering and design for manufacturing provides a continuous development to consider the logistics problems. The contributions of logistics and its constraints are enriched in the early phases of product design cycle. The merits of concurrent engineering were realized under the logistics requirements as a part of the overall product design. A logistic engineering model called the Design for Logistics Model for Product Design Framework is developed for this research. This model worked as a tool for logistician to include the necessary and relevant subsystems, modules, and design parameters. This approach allows the designer to become a full contribution in the logistics systems design. This methodology is applicable to matrices of any size or shape. The only requirement is that the elements of a matrix should be positive. The final solution obtained by using this algorithm is independent of the order in which the rows and columns are presented. This methodology generates modules that are cohesive, bounded, or contains a self-contained group of activities. For effective implementation of integrated logistics design, each module solves one clearly defined segment of the total system. The solutions are finite and it is applicable to new designs as well as currently existing designs.
 Ballou. RH, (1987) Basic Business Logistics (2nd Ed) Englewood cliffs, New Jersey: Prentice Hall.
 Benjamin S. Blanchard, (2000). Logistics Engineering and Management. Prentice-Hall, Inc. New Delhi,
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 Andrew Kusiak and Chun-Che Huang (1997) Design of Modular digital circuits for testability, IEEE Transactions on Components, Packaging and Manufacturing Technology-part C, Vol 20,No.1,
 Gnanasekaran J.S. and Dr. Shanmugasundaram S. (2002) Optimization in designing for logistics support--A Concurrent engineering approach, Proceedings of International conference on e-manufacturing, India, pp359-365.
 Gnanasekaran J.S, and Dr. Shanmugasundaram S. (2003) Concurrent Engineering Approach for Modeling to the Logistics, Proceedings of International Conference on Mechanical Engineering, Dhaka, Bangladesh, ICME2003-AM-31.
 Gnanasekaran J.S. & Dr. Shanmugasundaram S (2004) "Pioneer-manufacturing achievements through concurrent Engineering" International Mechanical Engineering Conference (IMEC-2004) Kuwait, December 5-8, 2004.
 Gnanasekaran J.S.& Dr. Shanmugasundram S. (2006)"Manufacturing Logistics-Research Implications", National Conference on Recent Advances in Product Design, Materials Technology and Manufacturing Systems (RAPMATS-06), Anna University, Chennai, January 6-7, 2006
 Gnanasekaran J.S. & Dr. S. Shanmugasundaram (2007)"Logistics Integrated Product Design under Concurrent Engineering Environment", International Conference on Manufacturing Engineering and Engineering Management (ICMEEM 2007) under World Congress on Engineering (WCE 2007) held at London, U.K., July 2-4, 2007.
 McCormick, W.T., Jr., Schweitzer, P.J., White, T.W., (1972). Problem decomposition and data reorganization by a clustering technique. Operations Research 20 (5), 993-1009.
 V. Kovaicheliyan (1999) Concurrent engineering experience of Indian companies, A Cover Story, The Machinist, May-June, pp 8-14.
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(1) The author has 18 years teaching experience and published more than 15 research publications. He is the Life Member of Indian Society for Technical Education (MISTE), New Delhi, The Institution of Engineers India, Kolkatta (India),MIE and The International Association of Engineers (Hong Kong),MIAENG.
(2) The second author has rich teaching experience of more than 40-years including administrative positions such as Vice Principal and Principal. He has published more than 30 research publications at various peripherals. He is the Life Member of Indian Society for Technical Education, New Delhi
J.S. Gnanasekaran (1) and S. Shanmugasundaram (2)
(1) Asstt. Prof., Dept. of Mechanical Engineering, Sri Krishna College of Engg. and Technology, Coimbatore-641008. India E-mail:firstname.lastname@example.org
(2) Prof., Dept. of Mechanical Engineering (Retired), Government College of Technology, Coimbatore-641013. India E-mail: email@example.com
Table 1: Data for Case Study Row Module (M) Design Parameters No. (DP) 1 Design for Supportability Analysis Supportability (Including Suppliers) (M1) Product performance Carrier type 2 Design for Die tool design for die casting Manufacturability Die manufacturing and (M2) Sample submission Sample validation 3 Product lines Die casting Product line (M3) Design lead times Product seasonality 4 Design Material Mix ratio Attributes Failure rate per unit time (M4) Inspection (Visual, sample, Process capability) 5 Manufacturing Aluminum die casting Processes Production volume (M5) Secondary operations 6 Production Machine Set up times Planning Inventory level and Control Scheduling (M6) (Including suppliers) 7 Materials ADC Aluminum Procurement (M7) Material availability Logistics of Material Move 8 Plant Location Multiple warehouses (M8) Demand schedule 9 Packaging Plastic partitioned Containers Materials Strength of Material (Package (M9) Material, Tensioners etc) 10 Packaging and testing Shock levels (M10) Vibration 11 Packaging Design Packaging shape, Size and features (M11) modules Package ease of handling 12 Functional Packaging Transportation requirements Requirements (M12) Shipping and Handling requirements 13 Transportation Mode Product density (M13) Number of Carriers 14 Transportability Average transit time Design criteria (M14) Product time to market 15 Transportability Transport method requirements (M15) Type of packaging Row Notation Column No. Number 1 (M1.1) 1 (M1.2) 2 (M1.3) 3 2 (M2.1) 4 (M2.2) 5 (M2.3) 6 3 (M3.1) 7 (M3.2) 8 (M3.3) 9 4 (M4.1) 10 (M4.2) 11 (M4.3) 12 5 (M5.1) 13 (M5.2) 14 (M5.3) 15 6 (M6.1) 16 (M6.2) 17 (M6.3) 18 7 (M7.1) 19 (M7.2) 20 (M7.3) 21 8 (M8.1) 22 (M8.2) 23 9 (M9.1) 24 (M9.2) 25 10 (M10.1) 26 (M10.2) 27 11 (M11.1) 28 (M11.2) 29 12 (M12.1) 30 (M12.2) 31 13 (M13.1) 32 (M13.2) 33 14 (M14.1) 34 (M14.2) 35 15 (M15.1) 36 (M15.2) 37 Figure 2: Concurrent Engineering Model (CEM) CONCURRENT ENGINEERING MODEL (CEM) Maximize System Effectiveness Minimize Design Cycle Time Subject to Logistics Integrated Product Logistics Engineering Design Sub Systems (and Modules with Manufacturing Logistics Design Parameters) such as Design For Packaging Design For Transportability
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|Author:||Gnanasekaran, J.S.; Shanmugasundaram, S.|
|Publication:||International Journal of Applied Engineering Research|
|Date:||Sep 1, 2008|
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