The international journal of advanced manufacturing technology, tập 60, số 5 8, 2012

Int J Adv Manuf Technol (2012) 60:421–436 DOI 10.1007/s00170-011-3636-4 ORIGINAL ARTICLE Enhancing total agility level through assessment and product mapping: A case study in the manufacturing of refrigeration air dryer C G Sreenivasa & S R Devadasan & R Murugesh Received: 10 October 2010 / Accepted: September 2011 / Published online: 27 September 2011 # Springer-Verlag London Limited 2011 Abstract The world’s manufacturing community has been questing for ways to face the onslaught of competition One of those ways is the adoption of agile manufacturing (AM) paradigm AM paradigm enables a company to quickly respond to the customers’ dynamic demands In order to implement AM paradigm, a model named “model for enhancing total agility level” (METAL) is proposed in this paper METAL enunciates the assessing of the total agility level (TAL), identifying the weak AM criteria and subsequently strengthening them The practicality of METAL has been explored in an air dryer manufacturing company During this case study, refrigeration air dryer was considered as AM capable product After assessment, three weak AM criteria were identified Proposals were drawn to strengthen these three weak AM criteria These proposals envisage the strengthening of the weak AM criteria through the removal of nonvalue adding activities, utilization of mathematical models, and creation of web portal The reassessment has indicated the possibility of enhancing the TAL value in the above company through the implementation of these proposals The experience of carrying out this research has revealed that the deployment C G Sreenivasa (*) University B.D.T College of Engineering, Davangere 577004 Karnataka, India e-mail: sreenivasacg@gmail.com S R Devadasan PSG College of Technology, Coimbatore 641004 Tamil Nadu, India e-mail: devadasan_srd@yahoo.com R Murugesh Darshan Institute of Engineering and Technology, Rajkot 363650 Gujarat, India e-mail: drmurugesh_m@yahoo.com of METAL would facilitate the contemporary companies to systematically infuse AM paradigm and enhance their TAL values Keywords Agile manufacturing Agility assessment Air dryer Time management Global optimization Introduction During past two decades, significant researches on “agile manufacturing” (AM) have been reported in the literature arena AM is a paradigm that makes a company capable of quickly responding to the customers’ dynamic demands [1, 2] Today, the manufacturers who successfully implement AM paradigm are able to thrive in globalized market environment Companies belonging to electronic industry, particularly television and mobile phone manufacturers are few of such examples [3] Those AM companies exhibit their agile capabilities by producing variety of models with innovative features within a very short period Hence, those manufacturers are able to sustain the competition despite the arrival of many competitors [4] Despite the dissemination of AM paradigm among researchers and practitioners, it is applied at slower pace in the manufacturing of traditional engineering products such as air conditioners, compressors, air dryers, generators, motors, and refrigerators It is high time that the manufacturers of these traditional products need to acquire AM characteristics at a higher pace to face the onslaught of competition Researchers started to work on AM after the formation of the institution called AM forum at Iacocca Institute, Lehigh University, USA in the year 1991 [5–7] A major emphasis of these researchers is that technology and management practices are required to get integrated in 422 proportionate form to implement AM paradigm in companies [8] These researchers have mainly viewed the outcomes of AM from four perspectives namely cost, market, time, and environment While viewed from these perspectives, the products produced by an AM company shall enjoy high sales in the market Also, these products shall be ecofriendly Those products shall require minimum time and cost to evolve new models [9–13] These enunciations suggest that a company shall map the characteristics of the products produced by it from these four AM perspectives The outcome of this mapping exercise would be useful to identify the potential products about which AM characteristics shall be infused to enhance the agility level of the company A survey conducted in the literature arena revealed the absence of any model that would enable the modern companies to enhance their agility levels by conducting such AM mapping exercise On identifying this absence, the research being reported in this paper was undertaken During the research being reported in this paper, the way of enhancing the “total agility level” (TAL) in a company through assessment and product mapping was explored This was accomplished in two stages In the first stage, a pneumatic products manufacturing company was identified Then the research was focused on the manufacturing of one of the products produced by this company namely air dryer Subsequently, the construction and working of air dryers were studied In the second stage, a model for infusing agility by strengthening the weak AM criteria was designed This model basically envisages the assessment of TAL and identifying the weak AM criteria which shall be strengthened to enhance the TAL The working of this model was explored by applying it on air dryer manufacturing Literature survey During the research being reported here, the literature was surveyed in two directions In the first direction, the literature was surveyed to identify agility assessment models A search in this direction revealed the appearance of 11 papers reporting agility assessment models The contributions of some of these papers are briefly described here Kumar and Motwani [14] identified 23 factors and subfactors which influence a firm’s agility These factors assist in the identification of strengths and weaknesses of the firms with regard to competing on time A parameter named “agility index” has been used for assessment The procedure for calculating the agility index has been explained However, this agility index has not been tested and validated Zhang and Sharifi [15] have proposed a conceptual model for implementing agility They have also contributed an agility assessment model This model facilitates the assessment of agility by gathering responses Int J Adv Manuf Technol (2012) 60:421–436 to the questions contained in a questionnaire This questionnaire consists of 72 questions for assessing agility needs and 66 questions for determining current agility level of the organization These authors have conducted case studies in 12 companies for validating this agility assessment model Ramesh and Devadasan [8] have reported their research on agile assessment using qualification and quantification tools The 72 questions for assessing agility needs proposed by Zhang and Sharifi [15] were used by these authors as qualification tool These authors have proposed a quantification tool consisting of 20 AM criteria The practicality of this model was explored by these authors by conducting a case study in an Indian pump manufacturing company In line to this research work, Vinodh et al [16] have redesigned the 20 AM criteria quantification tool proposed by Ramesh and Devadasan [8] These authors have statistically validated the redesigned 20 AM criteria quantification tool and carried out a case study in an Indian electronic switches manufacturing company An extended version of this research has been reported in Vinodh et al [17] In this paper, the method of measuring agility index using multigrade fuzzy logic approach is presented On analyzing the characteristics of the agile assessment models and tools which have surfaced in the literature arena during the recent years, it was found that the 20 criteria agility assessment tool proposed by Ramesh and Devadasan [8] which was further refined by Vinodh et al [16] was most simple, exhaustive, and accurate in assessing agility Yet it was found necessary to append this tool with the new criteria of AM reported in literature This is due to the reason that the researchers continue to work in the direction of identifying new criteria of AM [11] In the second direction of literature survey, the information on the application of AM paradigm on products was gathered This direction of literature survey revealed that few researches have been conducted by applying AM paradigm on products such as semiconductor, journal bearing, electronic switches, and pumps These researches have been conducted by adopting various technologies and management models to apply AM paradigm on these products Some of these technologies and management models include rapid prototyping technology (RPT), computer-aided design (CAD), activity-based costing, and Tabu-enhanced genetic algorithm For example, Cheng et al [18] have proposed an artificial intelligence and internet technologies-based system for implementing design and manufacturing agility in journal bearing In this system, the customer shall input the application requirements and then the system responds quickly to facilitate an optimum selection and offers design solutions Likewise, Vinodh et al [3] have explored the way of infusing agility in pump manufacturing company These authors have considered CAD and RPT for infusing agility The Pro/E software Int J Adv Manuf Technol (2012) 60:421–436 package was used for building the CAD model of the pump The GAMBIT and FLUENT software packages were used for conducting flow analysis Then, the model was prototyped using fused deposition modeling technique Few qualitative and quantitative techniques were used to gather the reactions of the practitioners As a result of this research experience, the CAD and RPT were found to be practically feasible for infusing agility in a traditional pump manufacturing company Thus although a few researchers have started to explore the way of infusing agility in products, no research on mapping of the characteristics of products with AM criteria has not so far been reported Altogether, the literature survey conducted during this research along the two directions has revealed that no model to enhance the agility of the company by systematically assessing it and mapping the product characteristics with AM criteria has been evolved In order to fill this research and practice gap, a model named as “model for enhancing total agility level” (METAL) was evolved during the research being reported in this paper The conceptual features of METAL are briefly described in the next section Conceptual features of METAL The conceptual features of METAL are depicted in Fig As shown, the TAL has to be assessed using an agility assessment tool in a product-manufacturing company If the TAL value is less than 50%, then the path of AM journey of the company is in disarray which cannot be easily corrected or enhanced This principle of fixing 50% as the minimum TAL base value has been drawn from Vinodh et al [16] According to this principle, a company scoring less than 500 marks using the agility assessment tool would lack management commitment This is due to the reason that, in the agility assessment tool encompassed in METAL, 50% of the marks are allotted under management commitment enabler Hence, a company lacking management commitment towards implementing AM will not be scoring more than 50% marks when agility assessment tool is used Hence, such a company will fail to implement AM successfully as it is an indication that AM pathway of this company is in disarray and cannot be corrected or improved The principle behind choosing 50% marks as the eligibility for implementing AM is also an impact of the grading system followed in educational system Most universities in the world fail the students who secure less than 50% of the marks [19, 20] This would mean that those students securing less than 50% of the marks would be failing to exhibit the traits of the education and skill imparted on them If the TAL value is greater than 90%, then the AM journey is correctly carried out on a hurdle-free path and hence there is no need to correct or accelerate it This 423 inference is drawn by observing the nature of evaluation of students followed in educational system It is observed that, most Universities in the world award highest grade to the students who secure more than 85% of the marks [19, 20] This would mean that those students securing more than 85% of the marks would be capable of successfully executing the knowledge and skill that are imparted on them during their courses of study In line to this observation, in the METAL, it is earmarked that, a company scoring more than 90% of the marks when agility assessment tool is used should be already in the AM path and free from facing any hurdles Such companies require no further actions to accelerate AM journey along the right path If the TAL value is equal to or greater than 50% but equal to or lesser than 90%, then there is a scope for infusing AM in the product/s of the company to enhance the TAL value In this case, the base value of TAL is fixed to declare weak AM criteria which are to be strengthened by infusing agility in the product/s of the company For example, if base value of TAL is fixed as 50%, then the AM criteria whose TAL values fall below 50% shall have to be declared as weak criteria Meanwhile, the product characteristics are mapped from the AM perspectives As the result of this exercise, the products possessing the propensity for infusing agility are identified These are chosen as candidate product/s for infusing agility with the objective of strengthening the weak AM criteria The TAL value is reassessed and compared with earlier TAL value Based on the results of this comparison, strategic decisions are made to remove the hurdles in the pathway so that the AM journey of the company is accelerated Practicality of METAL in the manufacturing of air dryers The practicality of METAL was investigated in a company by name Trident Pneumatics Private Limited (hereafter referred to as Trident) Trident is located in Coimbatore city of India Trident was started in the year 1988 with just four employees Today, Trident’s employee strength has increased to 51 Trident is associated with designing and manufacturing of various pneumatic application supporting products such as air dryers, drain valves, and filters Among the broad classification of air dryers, Trident manufactures two types of air dryers namely refrigeration and regenerative air dryers Till now, Trident has designed and manufactured 17 models of the refrigeration air dryer and 30 models of the regenerative air dryer These air dryers are widely applied in the fields like automobile, textile, medical, and cement manufacturing Trident’s air dryers cater to the need of the applications in these fields These air dryers are supplied to companies located within and outside India Besides designing and 424 Int J Adv Manuf Technol (2012) 60:421–436 Fig Conceptual features of METAL Assessment of total agility level (TAL) < 50% > 90% TAL value AM pathway is in disarray and cannot be corrected or improved AM pathway is free from hurdles; no need to correct or improve the company’s AM path ≥ 50% and ≤ 90% Agility needs to be infused in the product/s to increase TAL value Fixing of base TAL value Declaration and identification of weak AM criteria Product mapping from AM perspectives Identification of the product/s possessing maximum propensity for applying agility AM infusion in the candidate product/s with the objective of strengthening weak AM criteria Reassessment of TAL value Comparison of TAL value before and after the infusion of AM in the candidate product/s Strategic decision making manufacturing of pneumatic products, Trident is involved in research and development of pneumatic products The outcomes of Trident’s research and development activities have resulted in filing three patents These information indicate that Trident has been in the AM journey 4.1 Agility assessment at Trident The assessment of TAL value was carried out using a 30 criteria agility assessment tool This assessment tool is the extension of the 20 criteria agility assessment tool proposed by Ramesh and Devadasan [8] As the name implies, this tool facilitates the assessment of agility level of an organization from the perspective of 30 AM criteria These 30 AM criteria can be viewed in Fig This tool is incorporated with questionnaires under each AM criteria The competent personnel of Trident were interviewed with these questionnaires A conversion table encompassed in 30 AM criteria model was used to convert the responses of respondents into marks These marks were subsequently used to compute TAL of Trident The TAL value thus determined indicated that Trident has acquired 68.37% of agility This value falls between 50% and 90% Hence, according to the METAL, the need of infusing agility in the products manufactured by Trident was realized In order to declare the weak AM criteria, base TAL value was fixed as 50% Based on this Int J Adv Manuf Technol (2012) 60:421–436 425 Fig Actual agility levels and agility gaps at Trident fixation of base TAL value, the weak AM criteria at Trident were identified The weak AM criteria thus identified are graphically shown in Fig As shown, three AM criteria namely “time management” (with agility level of 30.625%), “global optimization” (with agility level of 37.5%), and “production methodology” (with agility level of 45%) were identified as weak AM criteria of Trident Due to space limitation, the detailed explanation about the 30 AM criteria has not fallen within the scope of this paper However, in order to facilitate the clarity of presentation, characteristics of the above three weak AM criteria considered for enhancing the TAL value of Trident are briefly described in Table 4.2 Mapping AM factors with air dryer characteristics Theoretically, an AM company may produce all types of products to meet the customers’ dynamic demands within a short duration of time without compromising profitability [1, 2] This is possible only if the company produces AM infused products As appraised in Section 1, an AM-infused 426 Int J Adv Manuf Technol (2012) 60:421–436 Table Weak AM criteria identified in Trident and their characteristics Weak AM criteria identified in Trident Characteristics Time management A primary capability of a company implementing AM is the ability to respond quickly against the customers’ dynamic demands Such quick response is possible if the time of developing the product and offering the services is totally eliminated While this task is not possible in all cases, efforts must be made to eliminate the nonvalue-adding activities in all endeavors that are required to quickly respond against customers’ dynamic demands In order to exert these efforts, the company implementing AM is required to utilize time management tools and techniques [16, 43] Global optimization AM is highly enabled through the operations carried out along both internal and external supply chains [16] Along these supply chains, the members of them have contradictory objectives [44] For example, a supplier prefers to get order for supplying huge quantity In this case, the supplier may offer discount to the company implementing AM On the other hand, this company will be attempting to enhance agility by applying just-in-time manufacturing principles in which case small quantities are ordered by allowing least lead times In this case, the company implementing AM stands to lose profit due to the absence of price discount offered by the supplier In this context, global optimization plays an important role in AM environment by providing optimized solutions to balance the contradictory objectives of the members of supply chains [31] Production methodology In an AM environment, the production methodology shall be efficient enough to meet the quantity and quality requirements of customers within a short period of time In order to develop such a capability, flexible and lean manufacturing principles need to be applied in the shop floor Application of flexible manufacturing principles will allow the production of customized and innovative products by fulfilling the quality requirements On the other hand, lean manufacturing principles will allow the production of exact quantity of goods without experiencing wastages [45] product will indicate its agility from the viewpoint of four perspectives namely cost, market, time, and environment These perspectives will be reflected in the forms of indicators For example, the agility of a product from market perspective will be indicated in the form of high sales These AM perspectives with their AM indicators are required to be mapped with the product characteristics This exercise carried out at Trident on air dryers is explained in the next subsection 4.2.1 Mapping exercise at Trident The exercise on mapping from the AM perspectives was carried out at Trident on refrigeration and regenerative air dryers which are manufactured at Trident This exercise was carried out by interviewing the managing director (MD) of Trident and gathering of relevant information along with his remarks Table presents the results of this exercise An analysis of the information presented in Table would reveal that the refrigeration air dryers possess propensities for implementing AM from the AM perspectives namely market and environment However, refrigeration air dryers not possess propensities to implement AM from the AM perspectives namely cost and time requirements The regenerative air dryers have been found to possess propensities to implement AM from all AM perspectives Thus, the exercise on mapping air dryer Table Mapping AM perspectives with air dryer characteristics in Trident Serial number AM perspectives AM indicators Refrigeration air dryer Regenerative air dryer Remarks by the MD of Trident Cost Market Time Low initial cost Low operating cost Low maintenance cost High demand (high sales volume) High customization capabilities Various product models High performance in long run Least design time – Yes – Yes – Yes – – Yes – Yes – Yes Yes Yes – Environment Least production and assembly time Ecofriendliness – Yes Yes Yes – – – – – – – All the types of air dryers are equally design intensive – – Int J Adv Manuf Technol (2012) 60:421–436 capabilities from AM perspectives has revealed that regenerative air dryers possess maximum propensity for infusing agility Hence, the regenerative air dryer was to be considered as the candidate product for infusing AM with the objective of strengthening weak AM criteria at Trident However, the MD of Trident wanted to consider refrigeration air dryer as the candidate product This is due to the reason that, refrigeration air dryers are more widely used [21] than regenerative air dryers In this context, during the research work being reported here, refrigeration air dryer was chosen as the candidate product for investigating the practicality of METAL 4.3 Strengthening the AM criterion “time management” As shown in the Fig 2, the agility level of the AM criterion “time management” at Trident is 30.625% The remaining 69.375% of agility gap in time management AM criterion needs to be filled In order to strengthen “time management” AM criterion, time compression technologies such as CAD/Computer-aided manufacturing (CAM), RPT, simulation, mass customization, “removal of non-value adding activities”, quick response strategies, “configure to produce according to the order”, and “redesign according to customers’ perceptions” are required to be employed [3, 22–28] While conducting agility assessment and carrying out subsequent analysis at Trident, two deficiencies were identified under “time management” AM criterion The first deficiency was that there has been no program to train the employees about the power of time compression in acquiring the competitiveness The second deficiency was that there has been no deployment of time-compression technologies at Trident The first deficiency could not be rectified during the research being reported here as allotting the time of employees to train them on time management is not currently affordable at Trident In this background, the efforts were made to rectify the second deficiency To begin with, the steps of assembling refrigeration air dryer were closely observed at Trident This observation indicated that, out of the several time compression technologies listed in the previous paragraph, the “removal of non-value adding activity” has high potential in applying time compression in the case of manufacturing refrigeration air dryer at Trident Hence, efforts were made to identify the nonvalue adding activities and the methods of removing them At Trident, assembly of refrigeration air dryer is carried out in an assembly cell In this assembly cell, three components namely compressor, condenser, and heat exchanger are assembled The activities carried out during this assembly practice are shown in Fig The time taken to carry out each of these activities is indicated in brackets These activities were studied to 427 Receiving and unpacking the outsourced components (10 minutes) Placing the unit containing compressor and condenser away from assembly cell Dismantling of compressor and condenser separately (25 minutes) Placing of compressor on the pallet Placing of condenser on the pallet Placing of heat exchanger on the pallet Assembly of compressor, condenser and heat exchanger (25 minutes) Brazing of copper tubes (25 minutes) Leak testing (10 minutes) Vacuum process (20 minutes) Gas charging (20 minutes) No load and full load testing (20 minutes) Final inspection and packaging (20 minutes) Fig Activities carried out in the assembly cell of refrigeration air dryer at Trident identify those that add no value while assembling the air dryer The nonvalue adding activities thus identified and the proposals drawn to eliminate them are enumerated in the following subsections 4.3.1 Unloading and unpacking of outsourced components As shown in Fig 3, the components outsourced are received and unpacked Since these components are heavy, this exercise consumes as much as 10 while assembling one refrigeration air dryer Subsequent to this exercise, these components are unloaded and moved to a place located at a distance of 10 ft from the assembly cell Instead if these components are unloaded at the assembly cell itself, then the time of 10 consumed to move them to the assembly cell and placing them there can be reduced to 4.3.2 Receiving compressor and condenser as separate units Currently, Trident receives compressor and condenser as an integral unit from a manufacturer Then the compressor and 428 Int J Adv Manuf Technol (2012) 60:421–436 condenser are dismantled and made as separate units Subsequently, the compressor and condenser are assembled on the base plate of a component called canopy This dismantling and fixing are necessitated as the compressor and condenser are assembled in the refrigeration air dryer in different orientation to suit the dimensions of the base plate of canopy This exercise consumes as much as 25 while assembling one unit of the refrigeration air dryer This exercise is pictorially depicted in Fig During the research being reported here, this whole exercise was recognized as a nonvalue adding activity After analyzing the steps, the following two proposals were suggested: Trident may place order for procuring the compressor and condenser separately from the same or two different manufactures These two components may be assembled on the base plate of the canopy at Trident Thus, the nonvalue adding activity shown as steps and in Fig can be eliminated Trident may ask the manufactures to manufacture the base plate of the canopy in accordance with the dimensions furnished by the Trident This manufacturer shall assemble both compressor and condenser on this base plate of canopy and dispatch the same to the Trident In this case, all the three steps shown in Fig can be eliminated at Trident Trident may choose to implement any one of the above proposals Here, it is obvious that the nonvalue-adding activity can be totally eliminated in the case the second proposal is implemented at Trident 4.3.3 Centralized inventory management facility One of the assembly activities is the positioning of the compressor, condenser, and heat exchanger according to the Fig Steps currently followed to assemble compressor and condenser canopy size of refrigeration air dryer at Trident The time taken to carry out this activity is 25 While carrying out this activity, the operator is required to collect the inventory located at two different places One place is located inside the assembly cell and the other is located outside the assembly cell Further, these inventories are stacked in the rack which cannot be rotated These conditions result in unnecessary motion of operators Hence, this motion is considered as a nonvalue-adding activity In order to remove this nonvalue adding activity, it is proposed that the inventory may be centrally maintained within the assembly cell Furthermore, it is suggested that the inventory shall be stacked in a rack which may be rotated Implementing these proposals may reduce 10 from the currently consumed time of 25 in collecting the inventory 4.3.4 Reducing the time of carrying out brazing operation In order to enhance agility level, all the components and sub-assemblies required to manufacture refrigeration air dryers are outsourced at Trident These components and sub-assemblies are assembled at Trident using few manufacturing processes One of those manufacturing processes employed is brazing Brazing is employed to join the copper tubes with the compressor, condenser, and heat exchanger During this process, the copper tube is bent to the required dimension and positioned The time taken for carrying out this process is 25 During this research, this process was recognized as a nonvalue-adding activity In order to remove this nonvalue-adding activity, following proposals were evolved: Trident may outsource the bending process to bend the copper tube to the required shape and dimension A fixture may be designed and manufactured to speed up the bending process Condenser Condenser Compressor Base plate supplied by the manufacturer Step : Compressor and condenser are supplied by the manufacturer as an integral unit Condenser Compressor Compressor Base plate of canopy manufactured at Trident Step 2: Compressor and condenser are dismantled Step : Compressor and condenser are separately fixed on the base plate of canopy Int J Adv Manuf Technol (2012) 60:421–436 Implementing either one of the above proposals would reduce 10 out of the currently consumed time of 25 in bending the copper tubes 4.3.5 Time reduction through the removal of nonvalue adding activities An estimation indicated that, on implementing the proposals evolved during this research being reported here to strengthen the AM criterion “time management”, the time of manufacturing a refrigeration air dryer may reduce from a minimum of 45 to maximum of 65 at Trident 4.4 Strengthening the AM criterion “global optimization” As shown in Fig 2, the agility level of the AM criterion “global optimization” at Trident is 37.5% Hence, the AM criterion “global optimization” needs to be strengthened to fill 62.5% of agility gap prevailing in Trident The efforts made to fill this agility gap are described in the following two subsections 4.4.1 Strengthening through information technology infrastructure While conducting agility assessment and carrying out subsequent analysis at Trident, two deficiencies were identified under the AM criterion “global optimization” The first deficiency is that there has been no deployment of information technology (IT) infrastructure to handle conflicting objectives prevailing in global supply chain In order to overcome this deficiency, the necessary IT infrastructure is required to be employed at Trident The IT infrastructure such as enterprise resource planning, electronic data interchange, internet protocols, local area network, database management systems, groupware, intranets, extranets, decision support systems, multimedia, e-commerce, expert system, modeling, and simulation are required to be employed [7, 29, 30] In this background, the IT infrastructure at Trident was closely observed Presently at Trident, the information among the customers, suppliers, and employees are managed over telephonic and postal communications A study in this direction revealed that, out of the several IT infrastructure listed above, internet technology possesses high potential for application in the case of manufacturing refrigeration air dryer at Trident The internet technology allows people to interact with each other If internet technology is used in Trident, then their employees, suppliers, and customers can interact with each other In this context, a web portal was designed to facilitate the Trident to overcome the aforementioned first deficiency This web portal is named as “Trident Global Optimization Platform” (Trident-GOP) 429 The “Trident-GOP” has been designed using the PHP version 5.2.3 as front end, MySQL client version: 5.0.45 as back end and XAMPP 1.6.3a software as editor The Trident-GOP can be accessed by three categories of users The first category of users of the Trident-GOP is named as “Trident group” These users are the top management and employees of Trident The other two categories of TridentGOP are suppliers and customers, Trident-GOP allow the customers to place the order online The web page of Trident-GOP enabling this process is shown in Fig Trident-GOP allows the Trident group users to record the components to be supplied by the suppliers and the due date of supplying them The web page of Trident-GOP enabling this process is shown in Fig On the other hand, Trident-GOP allows the supplier to submit a request to revise the due date The web page enabling this process is shown in Fig Now the Trident group user can view the orders placed by the customers and the request made by the suppliers to revise the due date The web page displaying these information is shown in Figs and Thus TridentGOP has been designed and developed to meet rudimentary requirements of globally optimizing the supply chain of the Trident More facilities can be added in the future to Trident-GOP for meeting many other globalized optimization requirements of Trident 4.4.2 Strengthening through the use of optimization techniques The second deficiency identified under the AM criterion “global optimization” at Trident was that there has been no deployment of techniques to optimize the contradictory objectives of supply chain management activities In order to overcome this deficiency, appropriate optimization techniques are required to be employed Several optimization techniques and mathematical models are available in the literature to optimize the contradictory objectives of supply chain For example, optimal policy models for handling temporary price reduction and price increase situations are presented in [31] Besides these kinds of mathematical models, other optimization techniques namely genetic algorithm and artificial neural network are required to be employed to enhance the efficiency and performance of supply chains [32–37] In this background, the contradictory objectives of the supply chain of Trident were closely observed Presently at Trident, the components are procured from their suppliers based on monthly forecasting As mentioned earlier in Section 4, Trident has been in the AM journey A company practicing AM principles is required to follow the just-in-time (JIT) philosophy [13] However, this company often has to confront the business dynamics such as temporary price discount and price increase During the Int J Adv Manuf Technol (2012) 60:787–797 789 Fig 3D warehouse studied in this paper this paper There are two blocks and seven picking aisles in the assembly supply warehouse Except for the first and the last aisles, all aisles have 76 picking arrays, and all arrays have five layers In the aggregate, the warehouse has 2,280 picking locations Orders coming from the production line can be automatic or manual It is known that all orders are accumulated in an order pool and data as order arrival time, order due time, order storage location is joint to the orders The capacity of the order-picking vehicle is constant, and each batch cannot exceed this capacity In the present case, the closest open location and random storage are used together for storage assignment strategy However, material arrival rates and order rates of materials are not considered in this strategy Just some specific materials that have higher order rates are cumulated to aisles without addressing to locations For these reasons, this strategy causes a chaotic structure in the warehouse Only, the order arrival time and related production line are taken into account for batching and routing Locations and routing procedures are not considered for picking lists These are distinct problems seen in the warehouse Therefore, the proposed approach is introduced to solve the above mentioned problems related to storage and order picking for the warehouse Storage assignment optimization In this paper, the proposed storage assignment optimization for automotive assembly supply warehouses includes four steps: Step Determination of the material classes Step Development of the mathematical formulations for distances from locations to warehouse input door and to order pool Step Development of the mathematical model Step Application of different scenarios for automotive assembly supply warehouse 4.1 Material classes Studies on class-based storage assignment policy show that better results are achievable with three classes In this paper, order rates have been considered on the determining of the material classes Three classes are determined according to order rates; class A, class B, and class C Order rates are calculated as follows: Order Rate ¼ Average daily consumption Case volumeðunitÞ ð1Þ 790 Int J Adv Manuf Technol (2012) 60:787–797 These classes will have an important role in determining assembly materials’ weights for storage location assignment 4.2 Proposed mathematical formulations for distances The main objective of this study is to minimize total travel time for storing and order picking in assembly supply warehouse On these grounds, distance computation is a critic issue in optimization Horizontal and vertical distances are ( D1i; j;k ¼ ( ¼ j ẳ ) jk +j " ỵ i 1ị = ỵ 5ị j ẳ ) jk +j " ỵ i 5ị þ ði À 1Þ Â =: ð3Þ The mathematical formulation for vertical distances from locations to order pool or locations to input door is: D3l ẳ l ( 4ị The mathematical formulation for travel time from input door to locations is: ti;1 j;k;l ẳ D1i;j;k D3l ỵ vh vv ð5Þ The mathematical formulation for travel time from locations to order pool is: ti;2 j;k;l ẳ D2i;j;k vh ỵ D3l vv ð6Þ where, α β γ δ y ω ε Horizontal distance between shelf columns and I/O point Horizontal distance between shelf divisions Location number in a shelf column layer Shelf column number in a warehouse block Horizontal distance between shelf columns Width of a shelf column Vertical distance between shelf layers ( xp;i;j;k;l ¼ i j k l 1,2,…,I for shelf columns 1,2 for direction of shelf 1,2,…,K for the numbers of shelf division 1,2,…,L for shelf layers The mathematical formulations for horizontal distances from locations to input door are: j ẳ ) ! ỵ minfk "ị; j2+ k j "ịg ỵ ẵ% iị ỵ = ỵ 5ị j ẳ ) ! ỵ minfk "ị; j2+ k j "ịg ỵ % iị = ỵ 5ị: The mathematical formulations for horizontal distances from locations to order pool are: D2i; j;k considered on developing of the mathematical formulations for distances We use the following notations: Vh Vv ð2Þ Horizontal speed of the order-picking vehicle (m/s) Vertical speed of the order-picking vehicle (m/s) 4.3 Proposed model At first stage of this study, the mathematical model of the storage location assignment optimization for an automotive factory was developed The aim of our model is to minimize total time for storing and order picking with consideration of assembly materials’ class weights The following notations are used in this model Indices: p i j k l 1,2,…,P for cases of assembly materials 1,2,…,I for shelf columns 1,2 for direction of shelf 1,2,…,K for the numbers of shelf division 1,2,…,L for shelf layers Parameters: w1p w2p ti;1 j;k;l ti;2 j;k;l w1p Weighting factor for travel time from input door to storage location Weighting factor for travel time from storage location to order pool Travel time from input door to storage location (s) Travel time from storage location to order pool (s) 1; w2p Decision Variables: if case p assigns to ith shelf column; jth shelf direction; kth shelf and l shelf layer Otherwise: Int J Adv Manuf Technol (2012) 60:787–797 791 Using the above indices, parameters and decision variables, the model is proposed as follows: z ¼ XXXXX p l Subject to X xp;i; j;k;l k j i w1p xp;i; j;k;l ti;1 j;k;l ỵ 8i; j; k; l Þ l j k p l k ð8Þ p XXXX XXXXX j i w2p xp;i; j;k;l ti;2 j;k;l 7ị DVp;l ẳ modp; )ị ỵ modl; )ịị ( 12ị Dp;l ẳ DHp;l ỵ DVp;l 13ị The formulation for travel time is as follows: xp;i; j;k;l ¼ 8p i 9ị tp;l ẳ DHp;l DVp;l ỵ vh vv 14ị where, f0; 1g xp;i; j;k;l ð10Þ Constraint specifies that number of cases assigned to each storage location cannot exceed Constraint states that each case must be assigned to only one location specified with indices Constraint 10 specifies that decision variable x must be or Objective function (7) minimizes total travel time from input door to storage locations and travel time from storage locations to order pool with consideration of assembly materials class’ weights η g b y ( vh vv DVp,l DHp,l Total location number in a shelf column Layer number in every shelf column Location number in a shelf layer Horizontal distance between shelf divisions Horizontal distance between shelf columns Vertical distance between shelf layers Picking vehicle’s horizontal speed (m/s) Picking vehicle’s vertical speed (m/s) Vertical distance between location p and location l (m) Horizontal distance between location p and location l (m) Order batching and routing optimization After performing storage assignment decisions, at the second stage of this study an integer programming model is proposed for batching and routing problem and optimal solution for small scaled problems obtained The formulation for distance is as follows: p ¼ 1; 2; ::1140 Location Numbers l ¼ 1; 2; ::1140 Location Numbers The problem environment mentioned above can be formulated mathematically as follows: Index: p : 1; 2; :::::::::::::; L for locations l : 1; 2; ::::::::::::::; L for locations b : 1; 2; :::::::::::::; B for batches DHp;l ẳ ẵmodjp l j; 8ị=) "; + ẵmodjp l j; 8ị=)ị "ịị þ floorðjp À l j=8Þ Â = Decision Variables: ( if the order of location p is assigned to batch b xb;p ẳ otherwise 11ị ( ybp;l ẳ if location l is visited immediately after location p in batch b otherwise 792 Int J Adv Manuf Technol (2012) 60:787–797 Decision variables determine the assignment of order locations to batches and the route of order pickers for each batch Parameters: Cap tp,l n Picking vehicle’s capacity (units) Travel time from location p to l (s) Number of orders The objective function minimizes travel time for order picking operations z ¼ B X L X L X bẳ1 pẳ1 lẳ1 L X ybp;l tp;l 15ị 5.1 Genetic algorithms xb; p 8b Cap 16ị pẳ1 B X xb; p ẳ 8p 17ị bẳ1 xb;BL ẳ 8b 18ị xb;FL ẳ 8b 19ị L X lẳ1 L X l¼1 location p to l in a batch must be equal to the batch’s order number Constraint 21 states that assignments from location l to p in a batch must be equal to the batch’s order number Constraint 22 is a sub-tour elimination constraint Constraint 23 specifies that decision variables y and x must be or This model is coded in GAMS [6], and an optimal solution for small-scaled problems is obtained However, the use of integer programming model in warehouse operations for exact solutions can be impractical and requires great computation time Therefore, heuristic approaches are applied to batching and routing problems to get solutions quickly and frequently One of these heuristics is genetic algorithms ybp;l ¼ xb;p p 6¼ l ð20Þ ybl;p ¼ xb;p p 6ẳ l 21ị upị ulị ỵ n L X L X pẳ1 lẳ1 ybp;l ỵ n 2ị L X L X p¼1 l¼1 ybp;l n À p 6ẳ l 22ị ybl;p ; xb;p f0; 1g 23ị Constraint 16 states that the number of orders assigned to a batch must not exceed the picking vehicle’s capacity Constraint 17 ensures that each order will be assigned to exactly one batch Constraints 18 and 19 specify that all batches must include the beginning and ending locations, respectively Constraint 20 states that assignments from Genetic algorithms were first introduced by Holland, who was inspired by the notion of natural and biological evolution In genetic algorithms, concepts inspired by population genetics and evolution theory are used to construct the optimization algorithms They attempt to optimize the fitness of a population of elements through recombination and mutation of genes To apply the genetic evolutionary concept to a real-world optimization problem, two issues must be addressed: encoding the potential solutions and defining the fitness function (objective function) to be optimized A solution, namely a chromosome, is encoded as a string composed of several components (genes) The initial population of chromosomes is either generated according to some principles or selected randomly The algorithm performs an evaluation to measure the quality (fitness) of the potential solutions Optimization using Genetic Algorithms is achieved by (a) selecting pairs of chromosomes with probabilities proportionate to their fitness and (b) matching them to create new offspring In addition to matching (crossover), small mutations are induced in new offspring The replacement of bad solutions with new solutions is based on some fixed strategies The chromosomes evolve through successive iterations, called generations The evaluation, optimization and replacement of solutions are repeated until the stopping criteria are satisfied [21] 5.2 Proposed algorithm In this section, a genetic algorithm is proposed to solve the order batching and routing problem together in all kinds of warehouse layouts Genetic algorithm is selected to take the advantage of evolutionary optimization approach In the initial stage of designing GA, order data of production for a month’s period is analyzed to determine chromosome Int J Adv Manuf Technol (2012) 60:787–797 793 length It is discovered that running the algorithm for min’s period is enough to fulfil orders and avoid tardiness There is an average of 15 orders in each 5-min period, and consequently, 15 genes in a chromosome The proposed algorithm, which is designed for simultaneous batching and routing in all kinds of warehouse layouts, is as follows: The genetic representation for the problem solution is encoded through a string composed of orders locations, which differs from the representation used in previous studies For example, the chromosome (A, B, C, D, E, F, G, H, I, K, L, M, N, O, P) means that order locations A, B and C are assigned to batch 1; D, E and F to batch 2; G, H and I to batch 3; K, L and M to batch 4; N, O and P to batch Step Define a genetic representation for a feasible solution of the problem Order Locations Chromosome Example: A B C D E Batch Numbers 1 2 F Step Create an initial population Set t=0 PO0ị ẳ x01 ; x02 ; x03 ; ::::::::; x0N N : Population size Step Calculate fitness function f ðxti Þ for all chromosomes in the population The fitness function is defined in terms of total travel time to pick orders in a batch that have the same line number and arrival time interval By minimizing the total distance travelled, and accordingly the total travel time, we can minimize the cost of warehouse operations Step Compute the probability of each chromosome in the population for the reproduction process G H 3 I K L M N O P 4 5 In the selection for reproduction, the roulette wheel selection mechanism is used The probability is calculated as follows: f xt ị Pxti ị ẳ P i f Pðxti Þ Â N : Reproduction number of related chromosome Step Apply crossover and mutation The crossover mechanism used in this study is reverse action, in which the genes between two randomly chosen points are reversed in order In the mutation mechanism, two randomly chosen points in the chromosome are swapped with each other The structure of the chromosome is considered in the selection of mutation and crossover mechanisms Step If the stopping criteria have been reached, then stop Otherwise, go to Step and set t=t+1 794 Int J Adv Manuf Technol (2012) 60:787–797 Table Weighting factors for material classes Weighting factor w1 Weighting factor w2 Class A Class B 0.1 0.3 0.9 0.7 Class C 0.4 0.6 Material classes storage assignment model Orders from the production line will be accumulated in an order pool By using this order pool to obtain all order information, such as arrival time, due time and order location, the algorithm will be run in a dedicated time interval When results are obtained from the algorithm, the order picker will fulfil the order list 6.1 Storage assignment optimization results Fig Genetic algorithm flow chart Figure shows flow chart of genetic algorithm Assignment Model given in Section 4.3 was coded using GAMS and run through different scenarios for automotive assembly supply warehouse Under the present circumstances, it is assumed that some storage locations are full Optimal solutions were computed for each scenario Among these scenarios, the most realistic one is the one that uses daily data The weighting factors (for travel time from input door to storage location: w1 and weighting factor for travel time from storage location to order pool: w2) are derived after a set of experiments for scenario Accepted weighting factors after the experiments are given in Table The program was run with weighting factors given in Table for assembly supply warehouse daily data and optimum storage assignment results are obtained as shown in Fig According to results presented in Fig 4, material case with reference number R102 of class A is assigned to locations nearby each other and order pool These results are obtained based on material class weights 6.2 Order batching and routing optimization results Experimental results and discussion The system designed for the warehouse optimization system will work as shown in Fig First, incoming products will be assigned to warehouse locations by using Fig Proposed warehouse optimization system To evaluate the performance of the proposed genetic algorithm designed for the batching and routing problem, actual data are used for testing An example dataset is presented in Table Int J Adv Manuf Technol (2012) 60:787–797 795 Fig Storage assignment results Table shows that this order dataset is from the 3rd production line and comes from the time interval 17:00– 17:12 hours Orders are requested, on average, h prior to their due times Due times are distributed in the interval 18:00–18:05 hours The requested orders from the production line are always for one product The parameters of a genetic algorithm—crossover rate, mutation rate, elitism rate and population size—also affect the effectiveness of the algorithm Parameter settings are derived by four factor experimental design Three levels are determined for all parameters to get optimum picking time Experiment summary is presented in Table Results of experimental design for critical parameters to obtain optimum picking time are presented in Table The crossover rate is set to 0.7, the mutation rate to 0.008, population size to 125 and elitism rate to 0.04 Table shows batching and picker routing results derived from the proposed genetic algorithm using the example dataset The algorithm is run at a 5-min due time interval For purposes of batching, we must restrict the size of each batch to three items, the capacity of the picking vehicle Table shows computational results of proposed genetic algorithm obtained from different datasets Conclusions In order picking systems, storage assignment and batch picking are approaches aimed at minimizing order picker’s Table Order information for an example dataset Order number Arrival time Due time Order location Production line number of order … 14 15 17:00 17:01 17:02 17:02 17:03 17:04 17:06 … 17:11 17:12 18:00 18:01 18:01 18:03 18:03 18:02 18:02 … 18:05 18:05 96 272 188 67 23 41 315 … 150 215 3 3 3 … 3 Table Experiment summary Crossover rate Mutation rate Elitism rate 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.008 0.009 0.01 0.008 0.009 0.01 0.008 0.009 0.01 0.02 0.03 0.04 0.04 0.02 0.03 0.03 0.04 0.02 Population size 75 100 125 100 125 75 125 75 100 796 Int J Adv Manuf Technol (2012) 60:787–797 Table Parameter settings for genetic algorithm Parameters Table Computational results for different datasets Genetic algorithm Crossover rate Mutation rate Elitism rate Population size Datasets Number of orders Number of batches formed Picking time (s) CPU time (s) 0.7 0.008 DS1 15 858 13 0.04 DS2 DS3 21 30 10 1694 2611 40 47 DS4 45 15 4619 54 125 travel time Many methods have been developed by researchers In this paper, these problems are discussed and optimized together for a specific warehouse in the automotive industry In storage location assignment, storage location proximity is considered for obtaining exact solutions to minimize total travel time in an assembly supply warehouse The distance between locations in a 3D warehouse is calculated using functions proposed in this paper, rather than approximate distances We present an integer programming formulation that optimizes the storage assignment Proposed model coded by GAMS and tested using data of an automotive factory Two main factors: time interval and storage location proximity considered to get exact solutions for batching and routing problem in the warehouse First, we represent an integer programming formulation that optimizes batching and routing problem together However, due to the need for short computation time in real world problems, we also developed a genetic algorithm to approximate the results Differently from previous studies genetic representation of solutions in Genetic Algorithm, designed in this paper, are encoded through order locations The distance between locations in 3D warehouse is calculated via functions proposed in this paper, rather than approximate distances Algorithm coded in C # programming language The main advantage of the algorithm is the quick response to production orders in realtime applications The solutions showed that the proposed approach based on GAs can be applied and integrated to any kind of warehouse layout in automotive industry This paper demonstrated that developed models can be used by Table Batching and routing results for dataset example Batching Batch number Total Picking Time 858 s Routing Items in batch 3 3 Picking route 272-78-67 215-41-66 315-122-96 150-175-23 200-12-188 similar automotive manufacturers’ warehouses effectively to reduce their supply chain costs Acknowledgement The authors thank TOFAŞ-FIAT Automotive Factory for providing data for the problem References Roodbergen KJ, De Koster R (2001) Routing Methods for Warehouses with Multiple Cross Aisle Int J Prod Res 39 (9):1865–1883 ELA/AT Kearney (2004) Excellence in logistics ELA, Brussels Hwang H, Baek W, Lee M (1988) Cluster Algorithms for Order Picking in an Automated Storage and Retrieval System Int J Prod Res 26:189–204 Rana K 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Koster R (2005) Travel Distance Estimation and Storage Zone Optimisation in a 2-Block Class-Based Storage Strategy Warehouse Int J Prod Res 43(17):3561– 3581 13 Hsu CM, Chen KY, Chen MY (2005) Batching Orders in Warehouses by Minimizing Travel Distance with Genetic Algorithms Comput Ind 56:169–178 14 Tsai CY, Liou JJH, Huang TM (2008) Using a Multiple-GA Method to Solve the Batch Picking Problem: Considering Travel Distance and Order Due Time Int J Prod Res 46 (22):6533–6555 15 Ho YC, Tseng YY (2006) A Study on Order-Batching Methods of Order-Picking in a Distribution Centre with Two Cross-Aisles Int J Prod Res 44(17):3391–3417 Int J Adv Manuf Technol (2012) 60:787–797 16 Ho YC, Su TS, Shi ZB (2008) Order Batching Methods for an OrderPicking Warehouse with Two Cross Aisles Comput Ind Eng 55:31–347 17 Hwang H, Kim DG (2005) Order-Batching Heuristics Based on Cluster Analysis in a Low-Level Picker-to-Part Warehousing System Int J Prod Res 43(17):3657–3670 18 Caron F, Marchet G, Perego A (1998) Routing Policies and COI Based Storage Policies in Picker-to Part Systems Int J Prod Res 36(3):713–732 797 19 Hwang H, Oh YH, Lee YK (2004) An Evaluation of Routing Policies for Order-Picking Operations in Low-level Picker-to Part System Int J Prod Res 42(18):3873–3889 20 Won J, Olafsson S (2005) Joint Order Batching and Order Picking in Warehouse Operations Int J Prod Res 43(7):1427– 1442 21 Goldberg DE (1989) Genetic algorithm in search Optimization and Machine Learning Addison-Wesley, New York Int J Adv Manuf Technol (2012) 60:799–809 DOI 10.1007/s00170-011-3594-x ORIGINAL ARTICLE The effects of using different type of inlet vents on the thermal characteristics of the automobile cabin and the human body during cooling period Muhsin Kiliỗ & Gửkhan Sevilgen Received: 26 October 2010 / Accepted: May 2011 / Published online: 26 August 2011 # Springer-Verlag London Limited 2011 Abstract A three-dimensional (3-D) transient numerical analysis was performed inside an automobile cabin during cooling period A three-dimensional vehicle cabin including glazing surfaces was modelled by using the real dimensions of a car A virtual manikin with real dimensions and physiological shape was added to the model of the vehicle cabin, and it was assumed that the manikin surfaces were subjected to constant temperature The virtual manikin was divided into 17 parts in standing posture to evaluate the local heat transfer characteristics of the human body during transient cooling period We considered three different cases that the cooling capacity of the automobile cabin was same for all cases Three-dimensional fluid flow, temperature distribution and heat transfer characteristics inside the automobile cabin were calculated with different type of inlet vents Comparisons of the numerical results were presented and discussed Keywords Inlet vents CFD Manikin Transient cooling Introduction The automobile is considered as the most common mode of transportation in the world People spend great amount of time per day in automobiles Researchers and engineers should design more effective HVAC systems than the classical ones to ensure passengers thermal comfort even in extreme conditions by considering consumers' demand M Kiliỗ (*) : G Sevilgen Faculty of Engineering and Architecture, Department of Mechanical Engineering, Uludağ University, TR-16059 Bursa, Turkey e-mail: mkilic@uludag.edu.tr For all needs described above, more numerical and experimental studies should be performed under different environmental conditions to get more effective HVAC systems for automobiles However, the complexity of human thermo-physiological model and physiological shape of the human body and highly transient conditions in the vehicle cabin make the CFD analysis more difficult [1] In previous researches, many numerical and experimental studies for the fluid flow and the thermal characteristics of passenger compartments have been carried out for cooling processes Lin et al [2] studied steady-state cooling process in a simplified model of a passenger compartment Aroussi and Aghil [3] used a passenger compartment with one-fifth scale model of a vehicle cabin and they studied the characteristics of the fluid flow inside the cabin However, many researches existing in the literature did not take account the dimensions of a real car and physiological shape of human body In addition, the number of the transient numerical analyses of the automobile cabin including a virtual manikin, thermal characteristics of the human body, velocity, temperature distributions of the automobile cabin and the calculation of three modes of heat transfer, i.e radiation, convection and conduction in the literature are very limited Sevilgen and Kilic[4] reported a three-dimensional (3-D) transient numerical analysis of airflow and heat transfer in a vehicle cabin during heating period In their study, they used a virtual manikin divided into 17 parts with real dimensions and physiological shape and they modelled an automobile cabin in 1:1 scale This computational model consisted of tetrahedron volume cells Kilic and Sevilgen [5] also developed their model and used hex-core mesh structure for getting more precious results in terms of computing time In their study, they used different types of boundary conditions on the human body surfaces to determine the 800 Int J Adv Manuf Technol (2012) 60:799–809 Fig 3-D CAD model of the vehicle cabin suitable boundary condition for evaluating thermal comfort Their numerical results were in good agreement with the experimental and theoretical data In this study, transient numerical analyses of standard cooling period of an automobile were performed with different types of inlet vents considering that the cooling load of the automobile HVAC system was same in all cases Therefore, the effects of selecting different type of inlet vents on the air flow and the temperature distributions of the automobile cabin were investigated during the cooling period We also employed local heat transfer characteristics of the human body and cabin interior The flow and the temperature distributions at different planes were computed and discussed for all cases The present study shows that different climate control strategies as they relate to human thermal comfort will be developed with 3-D CFD analysis Numerical simulation Table Segments and surface areas of the manikin Surface name Surface area (m2) 1234- 0.119 0.020 0.016 0.237 Head Neck Left shoulder Chest 5- Left arm 6- Left hand 7- Left thigh 8- Left leg 9- Left foot 10- Right shoulder 11- Right arm 12- Pelvis 13- Right hand 14 –Right thigh 15- Right leg 16 –Right foot Total surface area: 1.20 m2 0.113 0.018 0.096 0.139 0.027 0.016 0.113 0.005 0.018 0.096 0.139 0.027 In this study, Fluent software package was used for transient numerical analysis of air flow and heat transfer in the automobile cabin Fluent software solves continuum, energy and transport equations numerically with natural convection effects In numerical solution, second-order discretization method was used for convection terms and SIMPLE algorithm was chosen for pressure velocity coupling In the numerical analysis, we used renormalization group (RNG) k-ε model for modelling the turbulent flow This turbulence model is generally used for such Fig The slide of volume cells at Z=−0.28 m Int J Adv Manuf Technol (2012) 60:799–809 801 Table Initial conditions of the simulations Table Boundary conditions used in the numerical calculations The initial cabin temperature 41°C Operating conditions(HVAC system) Standard mode Simulation time Time step of numerical solution 30 1s calculations due to stability and precision of numerical results in literature [6–8] The RNG-based k-ε turbulence model is derived from the instantaneous Navier–Stokes equations, using a mathematical technique called RNG methods This model is different from standard k-ε model, and additional terms and functions in the transport equations for k and ε A more comprehensive description of RNG theory and its application to turbulence can be found in the references [9] In the computational domain, a 3-D hex-core mesh was generated which contained triangular elements at the surfaces of the cabin parts and hexahedron elements in the central-volume region In this study, the calculations of radiation heat transfer between cabin interior surfaces and human body surfaces were performed by using surface-to-surface radiation model including calculation of view factor(s) in Fluent The computing time of the view factors between the human body surfaces and the cabin interior surfaces were took about or days More detailed information on this model can be obtained in reference [10] Surface Boundary Condition Manikin surfaces Glass surfaces Constant temperature Convective Outer surfaces Convective Other surfaces Console type inlet vents Momentum: Vx (m/s), Vy (m/s) Heat transfer: Tinlet =T(t) (temperature profile (T(t)) is shown in Fig 4.) Defrost type inlet vents Adiabatic surfaces Momentum: Vx (m/s), Vy (m/s) Heat transfer: Tinlet =T (t) Outlet vents (backflow properties) Gauge pressure : Pa and it had a total surface area (1.81 m2) suitable for a standing posture In the automobile cabin, the free surface area of the virtual manikin with sitting posture reduces to 1.20 m2 as shown in Table The rest of the area (0.6 m2) is contact with the solid surfaces 2.2 Mesh structure In numerical calculations, mesh structure of the computational domain is very important for getting accurate 2.1 Modelling geometry In this study, 3-D computer-aided design (CAD) model of the vehicle cabin was modelled by using the dimensions of the automobile which was a 2005 model 1,600 cc FIAT Albea The CAD model of the vehicle cabin and the main cabin interior surfaces are shown in Fig To predict and evaluate the thermal characteristics of the human body, a virtual manikin was added to the CAD model The segments of the human body are shown in Table The manikin had a standard height (1.70 m) and weight (70 kg), Table Mean velocity values in all cases Cases Type of inlet vents Vx (m/s) I II Console type Console type Defrost type Console type Defrost type 1.58 1.50 0.85 2.00 0.42 III Vy (m/s) 1.58 0.00 0.85 0.00 0.42 Mean value (m/s) 2.50 1.50 1.20 2.00 0.60 Fig Horizontal (a) and vertical (b) plane of the vehicle cabin 802 Int J Adv Manuf Technol (2012) 60:799–809 predicted results and reducing computing time Sevilgen and Kilic[4, 5] used tetrahedral and hex-core mesh structures to calculate the thermal environments of the vehicle cabin In this study, a 3-D hex-core mesh was used in the present computations This mesh structure contains triangular elements on the surfaces, hexahedron elements in the volume region The computational grid used in this study consists of about 900,000 volume cells The section view of volume cells at Z=−0.28 m is shown in Fig An examination of the grid independence of the numerical solution has shown that such a grid system can obtain a nearly grid independent solution Computations carried out with a finer mesh, which includes about two million hexcore volume cells, showed no significant difference to the computed results but required a large increase in computing time and memory Time step was chosen as s for all iterations Computations were performed on a workstation with two Quad-Core Intel Xeon processor Computation time for one case presented in this study was approximately days The convergence is assumed when the normalized residuals of flow equations are less than 10−4 and the energy and radiation equations are less than 10−7 temperature of the surfaces without clothes such as head and hands was set to 33.7°C and the temperature of the surfaces with clothes was set to 33°C This value corresponds to the thermal resistance of the summer clothes Heat interactions between human body and the immediate surroundings occurs by several modes of heat exchange: sensible heat flow from the skin by convection and (a) Case-I (t=600s) 2.3 Boundary conditions and method In the numerical calculations, constant temperature boundary condition was applied at the manikin surfaces The (b) Case-II (t=600s) (a) Console type inlet vents (b) Defrost type inlet vents (c) Outlet vents (a) (b) Fig Temperature profile for all type of inlet vents (c) (c) Case-III (t=600s) Fig Velocity (metres per second) predictions at the horizontal plane of the vehicle cabin at 10 of cooling period Int J Adv Manuf Technol (2012) 60:799–809 radiation; latent heat flow from the evaporation of sweat and from evaporation of moisture diffused through the skin; sensible heat flow during respiration and latent heat flow due to evaporation of moisture during respiration In order to predict the latent heat loss from the body, the moisture transport equation must be solved However, parameters related to dry or evaporative heat flows are, generally, not independent because they both rely, in part, on the same physical processes The Lewis relation describes the relations between convective heat transfer and mass transfer coefficients for a surface [11] The Lewis relation can be used to relate convective and evaporative heat transfer coefficients Therefore, latent heat loss was not considered and respiration was neglected in the present computations We assumed that the boundary conditions for the areas contact with the solid surfaces were adiabatic; thus, we considered sensible heat which is transferred from human body to the environment by convection and radiation In transient numerical simulations, initial conditions are also very important to get precise results The initial conditions used in the numerical calculations are shown in Table In the first case, we just used console type inlet t=1s 803 vents for cooling analysis and we assumed that the defrost inlet vents were turned off In the second and third cases, both defrost and console inlet vents were used for cooling analysis but different velocity components were employed for inlet vents In this study, we assumed that the cooling capacity of the HVAC system of the automobile was the same for all three cases Thus, we calculated the velocity components in x, y direction for all inlet vents These velocity components and mean values are shown in Table The boundary conditions used in this study are shown in Table Convective boundary condition was considered at the glass surfaces and outer surfaces of the cabin Convective heat transfer coefficient at outside of the cabin was set as 15 W/m2°C We defined virtual planes in the vehicle cabin for evaluating the flow and thermal characteristics of the vehicle cabin in different aspects during standard cooling simulation The locations of these planes are shown in Fig The transient temperature inlet boundary condition was employed for all inlet vents and this temperature profile obtained from the measured data is shown in Fig t=1s t=1800s t=1800s Fig Temperature (°C) predictions at the horizontal plane of the vehicle cabin during cooling period (case I) Fig Temperature (°C) predictions at the horizontal plane of the vehicle cabin during cooling period (case II) 804 Int J Adv Manuf Technol (2012) 60:799–809 Results and discussion The velocity fields at the horizontal plane for all cases are shown in Fig In the front region of the horizontal plane, the calculated velocity values varied between 0.08 and 2.33 m/s for case I On the other hand, in the rear region, these values ranged from 0.08 to 0.21 m/s From these results, we can conclude that the uniform velocity distribution occurred in the rear region of the horizontal plane We obtained a different velocity distribution for case II and the air flow divided into two regions in the front part of the horizontal plane The calculated velocity values in this region varied between 0.07 and 1.47 m/s These values ranged from 0.07 to 0.16 m/s in the rear region and these values were lower than the values obtained for case I A velocity value of 0.56 m/s was computed near the left shoulder surface for case II and this value was calculated about 0.08 m/s in that region for case I The main reason for this was that strong air motion occurred near the left shoulder surface in case II The general structure of the velocity distribution obtained for case III is very similar to the case II In the front region of the horizontal plane, the calculated velocity values changed between 0.07 and m/s in case III These values ranged from 0.10 to 0.78 m/s in the rear region The temperature fields at the horizontal plane of the cabin for case I is shown in Fig The maximum temperature difference at the horizontal plane was computed about 10°C for case I at of cooling period This value was raised to 15°C at of cooling period In the front region of the horizontal plane, computed temperature values were quite different In contrast to temperature values obtained in the front region of the horizontal plane, the temperature values were slightly different in the rear region of this plane The maximum temperature occurred near the manikin surfaces and it was computed about 30°C at of cooling period With increasing time, the interior of the automobile cabin was cooled continuously and the predicted temperature values ranged from 12°C to 28°C at 30 of cooling period From the comparison of the temperature values computed at 20 and 30 of cooling periods, we conclude t=1s t=1s t=1800s t=1800s Fig Temperature (°C) predictions at the horizontal plane of the vehicle cabin during cooling period (Case-III) Fig Temperature (°C) predictions at the vertical plane of the vehicle cabin during cooling period (case I)