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A simulation based comparison: Manual and automatic distribution setup in a textile yarn rewinding unit of a yarn dyeing factory
Simulation Modelling Practice and Theory 45 (2014) 80–90
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Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat
A simulation based comparison: Manual and automatic distribution setup in a textile yarn rewinding unit of a yarn dyeing factory Brahmadeep, Sébastien Thomassey ⇑ GEMTEX/ENSAIT, Roubaix, France University of Lille, North of France, Villeneuve d’Ascq, France
a r t i c l e
i n f o
Article history: Received 19 December 2013 Received in revised form 7 March 2014 Accepted 2 April 2014
Keywords: Simulation model Setup comparison Internal supply chain Lean tools Automation Arena Simulation
a b s t r a c t This paper aims to explain the production flow and the distribution logic of bobbins for rewinding process in a yarn dyeing factory, comparing the different scenarios of production (manual and automatic) using the computer simulation tools. The goal of this project is to build a model in which all the involved processes can be simulated with the consideration of all the parameters and constraints. The simulation model is used as a tool for the comparison of present manual setup and future automated setup for the production management of bobbin distribution in yarn rewinding process in terms of delays and costs. Since, the manual operation involves defaults, improper time management, errors and with the growing competitiveness globally, the companies in Europe need to automate as much as possible their production lines. The expected impacts are to increase the productivity and profitability, to have the possibility to customize the production, to develop production tools, implementation of the lean manufacturing tools. Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction Industries in Europe are facing with pressures to reduce their production costs and to increase the efficiency of their existing staff or equipment. The customer demand is becoming more personalised which requires industries to produce small lot size, in short lead time and in unique specifications. Thus the only way for a producer is to improve their productivity, reactivity, flexibility, and quality. The trend of mass customization and personalized production leads to fundamental changes in the material flow, plant layout and work organisation [1]. Producers have to rely on smart automation of their production system coupled with an adequate use (qualified staff, computer aided setup), efficient and intelligent planning management, and overall supervision and decision systems. This could be very challenging for some companies such as textile companies, and more particularly dyeing and winding production units which are commonly faced with large batches, long process and setup times. Therefore, it is now the requirement to determine the best possible solution and find the feasibility of such change in a very short time. This project concerns the development of automated feeding of winders in order to fight against outsourcing caused by the cheaper workforces and to fight with the lost employment in the textile sector in Europe. ⇑ Corresponding author at: GEMTEX/ENSAIT, 2 allée Louise et Victor Champier, 59100 Roubaix, France. Tel.: +33 (0)3 20 25 64 64; fax: +33 (0)3 20 24 84 06. E-mail address: [email protected] (S. Thomassey). http://dx.doi.org/10.1016/j.simpat.2014.04.002 1569-190X/Ó 2014 Elsevier B.V. All rights reserved.
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The concept behind this project is based on automation, optimal flow management (internal supply chain management), lean tools and setup comparison of the yarn rewinding processes (manual and automatic) with the support of modelling and simulation. Companies with the most optimised and automated production tools will be privileged in order to obtain the best product at an acceptable cost. The objectives of this paper are to identify and to analyse the present scenario of the supply chain of the production plan, to develop a standard simulation model with consideration of all parameters and constraints, to identify Key Performance Indicators (KPI’s), to analyse the results obtained from the simulations, to simulate and analyse different scenarios (manual and automatic input of bobbins), to compare and analyse the results obtained from the simulated scenarios and to optimise and implement the results in the actual production. The paper is structured as follows. In Section 2, an overview of the existing literature and case studies related to this work is presented. In Section 3, the methodology of this work is described along with the detailed definition of the problem, parameters, constraints and simulation model logic. In Section 4, the validation of the simulation model and the experiments done (testing scenarios) in this work are explained. In Section 5, the results obtained from the experiments performed are analysed and formulated (comparison of results obtained from manual and automatic process simulation models). An overview of the future scopes and development of this work is explained with a global conclusion in the Section 6.
2. State of the art 2.1. Simulation in manufacturing The simulation is widely recognised as the best and most suitable methodology for investigation and problem-solving in real-world complex systems in order to choose correctly, understand why, explore possibilities, diagnose problems, find optimal solutions and transfer R&D results to real systems [2]. There are various types of simulation models. The discrete event simulation involves the modelling of a system as it progresses through time and is particularly useful for modelling queuing systems [3]. There are many examples of queuing systems: manufacturing systems, banks, fast food restaurants, airports, etc. A major facet of discrete event simulation is its ability to model random events based on standard and nonstandard distributions and to predict the complex interactions between these events. For instance, the effects of a machine breakdown on a production line can be modelled. There are many factors which contribute to the use of the simulation in the manufacturing. The advantages include analysing the manufacturing processes, ease of use, flexibility, ability to model dynamic and stochastic nature of production systems, ability to test various scenarios of production, layout design, logistics, material handling, etc. which lead us to have a close insight into the performance of a manufacturing company [4–8]. In the following paragraphs, few successful examples concerning the applications of simulation in the manufacturing sector are discussed. The factors that affect production flow time are explained using the simulation in [5] with the parameters which include the comparison of the two different layout plans. The simulation included the scheduling rules, machine breakdowns, batch sizes and transporters. The work demonstrated the use of simulation to play with the different setting level of manufacturing parameters which affect the performance of a facility. The application of simulation to study and analysis of internal logistics in a chemical manufacturing plant is demonstrated in [6]. The flow of materials involves a continuous flow of materials, such as liquid, gas or solid through the manufacturing and logistics processes. With the aid of simulation the capacity requirement for logistic operations were determined. The model described is extremely flexible in terms of the user’s ability to make changes for different scenarios. Most of the input parameters and data are table-driven. Users can test different scenarios without changing the simulation model, and the model generates several useful reports automatically. The concept of simulation in manufacturing can also be seen in case of a very complex system like in an automobile assembly plant, the authors of [7] demonstrated a data driven simulation model. The significance of this methodology adopted is that it provides a rapid prototyping, capability for production system modelling and enables a quick analysing and remodelling capability to respond to the fluctuation of demands. The simulation modules for assembly line and material handling system of the plant floor are analysed and are developed where the data driven approach is implemented to enable the modelling and simulation of the complex assembly plant in a real time like condition and therefore effectively improve the responsiveness and flexibility of the production line. The methodology used for simulation is very logical in relation to the development of a simulation model to analyse the complex production. The steps involved in the respective manner are: data preparation, model generation, model validation and scenario simulation. The floor operations, production type, assembly line configuration and material handling processes are defined for setting the parameters in the simulation model. The modules developed for the simulation are assembly line models and the material handling modules which are interlinked to each other. The simulation based production decision enabled a lot of automobile manufacturers to quickly adjust the assembly lines for achieving maximum labour utilisation and lowering production cost using the concepts of Lean, JIT (just-in-time) while satisfying the market demands. The application of simulation in manufacturing can be seen in case of some modern complex concepts like backward online change scheduling in production [9] where the insight is the WIP management, lean processes and reduction of idle times. Also, in the case of the analysis of lot streaming in job shops with transportation queue disciplines where there are
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multi-products and sub lots [10]. It considered the idling and availability, setup time of the machines, load-unload and trip times of the transporter, weekly demands and job routings. The manufacturing units having the concepts of Make-to-Order utilise the advantages of the simulation based models. The proposed model in [11] is developed using a system dynamics approach where the operations performed within a supply chain are a function of a great number of key variables which have strong interrelationships. The modelling effort focused on measuring the supply chain system performance in terms of key metrics such as inventory, WIP levels, backlogged orders and customer satisfaction at all four echelons. The methodology for the simulation follow the steps starting with the logic diagrams, level diagrams, definition of model operation conditions (initial and in-operation) and the testing of the different scenarios. In [11], it shows that an efficient modelling with the proper links and interrelations is vital for a thorough understanding of the system dynamics and behaviour which is a key step towards the optimised performance. These above production designs and manufacturing plans are very similar to the textile production setups and in case of a yarn-rewinding section of a yarn dyeing unit in terms of the production parameters and constraints. However, the specificities are different which demands a concentrated study of simulation in the domain of textile and garment manufacturing. 2.2. Simulation in textile and garment manufacturing The textile and apparel manufacturing units are indeed very complex systems because of the number of production parameters and high variety of references. Nowadays, the demand is also moving towards more personalised products which in result would give rise to small lot sizes, short lead times and unique specifications. To compete in this changing environment the producer has to improve their productivity, reactivity, flexibility, and quality. Determining the best facility design is a classical industrial engineering problem [12]. Each layout problem has its own unique characteristics and the objectives in a plant layout study might include one or more of the following: minimizing the investment required in new equipment, minimizing the time required for production, utilizing the existing space most efficiently, minimizing the materials handling cost, facilitating the manufacturing process, etc. Flexibility is also an essential requirement in order to respond to shorter product life cycles, low to medium production volumes, changing demand patterns, and a higher variety of product models and options [13]. With the use of the process simulation, the analysis of various scenarios could be executed within a short time with more ease. For instance, the scenarios of an automatic production and a manual production could be easily compared with the given performance indicators like cost and lead times. In the following paragraphs, few successful examples concerning the applications of simulation in textile and apparel manufacturing sector are discussed. The problems such as planning and scheduling of the fabric production orders, to achieve the shipment on time, maximising the loom utilisation, and labour requirement are the major concern in [4]. The processes are simulated and analysed in order to find feasible solutions to the problems. The results obtained demonstrated the flexible nature of the simulation model which easily copes with the textile manufacturing requirements and the application can be extended to other textile sectors like apparel production and dyeing units. The main challenges in garment manufacturing units are the labour force for production processes, small quantities with few repetitions, frequently changing styles and short delivery times. The aim is to meet the challenges inherent in producing a greater range of products, but at the same time trying to keep the economy-of-scale benefits of mass production by using computer simulations [14]. In the apparel production sector, the simulation tools are used to link all processes of a flexible production system in [15]. The objective is to have an intelligent manufacture of quality clothing. The setups are analysed by computer simulation in order to verify the actual potentials of the facilities with a foresight to find the most effective plans for long run operations by adaptive exploitation of flexibility at different levels with effective schedules to meet the growing demands which are personalized, quick response and total quality. The simulation techniques can be implemented for the forecasting of the sourcing processes of a retailer and a manufacturer that enables to quantify the impact of the forecast errors on the financial and supply performances which are linked with the production plan, inventory level, service level and profit margin of the manufacturer [16]. The simulation in the textile and apparel manufacturing can be implemented in facility design like in case of the automobile assembly units. The authors of [8] demonstrate a comparison between a traditional assembly line and of a U-line assembly line. In [17], a discrete event simulation model is developed and used to estimate the storage area required for a proposed overseas textile manufacturing facility. The objective is to predict the amount of WIP in the proposed layout under two different scenarios of dye capacity which is identified as the bottleneck in the overall plant capacity. The ability to store attribute values and queuing levels at an individual product level make the simulation more detailed which is a requirement in case of the apparel and textiles. The case study in [17] shows that discrete event simulation in its facilitation role can provide a qualitative understanding of behaviour over and above the normal benefits associated with this technique. A case study by the authors in [18] explains about a company which is trying to eliminate the effect of long lead times, high logistic costs and improve customer satisfaction. To achieve the goals of the company, various simulation model alternatives are designed in order to test and compare the models and to find the most suitable result which can be implemented later. They take five situations namely initial simulation and model validation, additional workload for customised products, reducing the duplication of products, enlargement of workplaces and investment into appropriate equipment. Each situation is simulated and analysed for the results which lead to many positive findings. They are able to increase the number of processed customized orders twenty folds, the costs per piece decreased by 84% and obtain a high service level.
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These works verifies that the simulation models are very powerful and efficient to enable the companies to achieve their goals in this competitive world by analysing different scenarios by simulations and later implementing the proposed organizational changes. This technique of simulation is perfectly adapted to sectors where the production constraints are huge and complex like in dyeing, winding and other production sectors in order to test and verify different scenarios of production with ease. The comparison based upon the simulation models can be implemented for instance to compare the manual and automatic setups and also for the facility design but this would require customising the existing model as per the changes in the respective work flows. 2.3. Simulation software Simulation Software is the heart and brain of a simulation model. It is the actual place where all the possibilities are constructed, processed, analysed, optimised and later the derived conclusions get implemented to the real situation. The analysis in [19] suggests the simulation software with good visualization or animation properties are easier to use but limited in case of complex and non-standard problems. The various techniques of data entry for simulation model are explained in [20]. Nowadays a lot of simulation software products are available in the market for the supply chain management including for the internal factory supply chain and production flows. The use of Witness in textile production can be seen in [4] and in some automobile industries. Arena Simulation is simulation software by Rockwell Corporation [21] and it is used in different application domains: from manufacturing to supply chain (including logistics, warehousing and distribution) from customer service and strategies to internal business processes. It provides the user with object libraries for systems modelling and with a domain-specific simulation language SIMAN. The application of this software in manufacturing and textile/apparel can be seen in the papers [5,7,8,10,17]. In [7] it is integrated with VBA coding which enhances the possibilities of the simulation model. The simulation environment uses a flowchart-based modelling methodology that facilitates the description of discrete- event systems. Systems are described using the point of view of the entities that flow through them using the available resources. Models are structured in a hierarchical and modular way. They are defined by means of a flowchart diagram and static data [23]. The simulation was able to demonstrate the concepts of TRANSPORTER, WAIT-SIGNAL and BUSY-IDLE modules in [24]. These modules play an important role in modelling an automated manufacturing process or with an involvement of scheduling, queuing and waiting. As per the authors of [22], The Arena Simulation is one of the most popular and powerful simulation software in its domain. Thus, we select this software for our work. 3. Methodology In simulation issues, the methodology has to be developed specifically with respect to the project requirements and parameters. For examples in [7,11], the methodology is defined for the simulation in manufacturing. We have taken these works as a baseline and developed our project specific methodology. The schema of which is stated in the Fig. 1. The following sub-sections describe the steps mentioned in Fig. 1 with details. 3.1. Identification and analysis of the present scenario (parameters and constraints) The manufacturing unit in our case study is a yarn dyeing factory. The material flow in the factory starts from the dyeing section where the yarns on the bobbins are dyed in a fully automated process. The dyeing production is scheduled according
Fig. 1. Methodology for the project.
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to the shipment date and the management of dyeing batches. Indeed, dyeing batches should be strictly sequenced to follow the cycle of colour from the cleanest to the darkest. Then, the bobbins are transferred to a temporary storage where they wait for an operator to be picked up and transported manually to the winding machines by trolleys (push from the dyeing section). The winding process is done in order to transfer the yarn from loose tube bobbin to a universal tight cone bobbin which is clean and continuous. The operator checks the shipment dates of the orders and accordingly transfers the trolleys to the winding machines. After the winding is done the bobbins are automatically transferred to the packaging section and finally they are shipped out of the factory. Thus the simulation model we have to develop should integrate both push and pull flows as illustrated in Fig. 2. The winding section is composed of four yarn winding machines called (A, B, C and D) in the following. Each machine is having six sections (Sec I, II, III, IV, V and VI) while the sections have ten winders each (60 winders per machine). The general layout of the machines and the sections on the production floor is presented in Fig. 3. One operator is responsible for the distribution and transportation of bobbins and one operator per each machine is responsible for the manual input of bobbins on the machine winders (5 operators per shift). The factory works 3 shifts per day from Monday to Friday.
Fig. 2. Work flow in the factory.
Fig. 3. Basic layout of machines and sections and operators.
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Brahmadeep, S. Thomassey / Simulation Modelling Practice and Theory 45 (2014) 80–90 Table 1 Machine constraints. Constraints
M/C: A
M/C: B
M/C: C
M/C: D
Colour Control Tension Control Thickness control Multiply winding
Yes Yes Yes Only Sec I
No No Yes Yes
No Yes Yes Only Sec I
No Yes Yes Only Sec I
The main assets of the company are the high variety of yarn specifications, the small lot sizes and the lead times. To meet the wide variety of products, each machine/section has its own features which enable to process one or several yarn specifications. Indeed, some yarn specifications involve specific processes to reach the required quality in terms of colour, thickness, etc. The machine constraints are the same for the ten winders in a section i.e. each section can be identified as a system of 10 winders with same machine parameters and constraints. Table 1 illustrates the respective machine-section constraints in relation to the yarn bobbins. In the case of multiply yarn winding, if the yarn undergoes the self-twisting process then they are considered to have the same machine constraints as of the single ply yarns. Only the non-self-twisted multiply yarns are considered to have the constraints of a multiply yarn on the machine.
3.2. Design of the logic required for the simulation The potential and theoretical yarn qualities (types) found is in hundreds which means the product variety and parameters is a huge number. It is not feasible to plan the distribution and management on this basis. For the simulation purposes, there is a need to find a logic taking into account a reasonable number of product categories. Since the bobbins are ultimately distributed to the machines, it is decided to plan the product category as per the machines. The bobbins are categorised on the basis of machine constraints and a total of 13 categories are obtained (Fig. 4). The category distribution of bobbins takes into account the constraints mentioned in the Table 1. The explanation of the various possibilities for a category of yarn-bobbin to be distributed to the respective winding machine can be seen in Table 2. The machine setup does not change for the shift of one category of bobbin to another because the categories are already determined as per the machine constraints. As only the similar type of the category of bobbin go to the respective machine section. With the consideration of all the global parameters a simulation logic diagram is developed on the basis of which the actual modelling of simulation is done on the software (Fig. 5). In the case of the manual flow process, the logic explains the input of the yarn-bobbins from the dyeing section to the winding section (real-push entry) where there is a temporary storage. These are assigned on their respective trolleys whose
Fig. 4. Logic tree for the classification of the categories.
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Brahmadeep, S. Thomassey / Simulation Modelling Practice and Theory 45 (2014) 80–90 Table 2 Possibilities of winding for each category with respect to the machines. Category
Machine/section
1 2 3 4 5 6 7 8 9 10 11 12 13
M/C: A M/C: D, M/C: C or M/C: A if place is available M/C: A M/C: D, M/C: C or M/C: A if place is available M/C: A (section I) M/C: D (section I), M/C: C (section I) or M/C: A (section I) if place is available M/C: A M/C: D, M/C: B, M/C: C or M/C: A if place is available M/C: A M/C: D, M/C: B, M/C: C or M/C: A if place is available M/C: A (section I) M/C: B, M/C: D (section I), M/C: C (section I) or M/C: A (section I) Not going to any of the machines in the study
Fig. 5. Simulation model logic.
capacity is dependent on the lot size. The bobbins contain all the parameters like entry data, yarn qualities, yarn product category, and machine requirements. Then from the temporary storage they are transported in the trolleys to the winding machines with the help of a sequence which is based upon the delivery date of the order, machine utilisation and present human resources (pull flow sequence). The recording of the process time, in-process constraints are saved automatically in the database. Later there is the recording for the performance indicators like the delay or on-time delivery, and costing. A similar approach is executed for the simulation logic of the automatic process with different constraints and parameters. This logic aid in the comparison of the different scenarios like for the manual and automatic process which could to be easily analysed and the results obtained could play an important role in the final decision making. 3.3. Modelling of the simulation for the scenarios The modelling is done with the software Arena Simulation. The decision making, distribution logic and material movement are linked to the parameters like the categories of the bobbins, speed of the operator with loaded and empty trolley, working hours, machine efficiency (each section of the machine have different efficiencies), minimum WIP (work in process-lean), number of operators, operator and machine cost per hour, conveyor speed, etc. The model is a multi-level; consisting of various levels (production floor level, machine level and section level) and modules (material flow and operations, information flow, transport, reading and recording, etc.). In the model the input information for each category of the bobbins is imported from the excel files. One month duration of real data is taken for the inputs: this represents 1622 orders. Decision and Hold modules are used for the distribution of the bobbins to their respective machine sections. The initial pull sequence (the order in which the bobbins are to be taken from the temporary storage to the machines on trolleys) for the model is taken as per the delivery dates for the orders which is also read from the excel files. The pull of bobbins is also dependent upon the human resource and machine work in progress i.e. only when the machine winders WIP is one or less then only the next bobbin in line would be assigned onto it. The pull system and lean concepts (minimum WIP) are linked operationally from the machine winder level up to the storage area.
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The simulation is performed on three scenarios: manual, automatic input working weekdays and automatic input working full week. For the ‘manual’ scenario, the distance and the speed of the operator to transfer the bobbin trolley to the machines are taken as per the actual time study analysis in the factory. The operator speed is taken as 23 m/s and 28 m/s with load and without load respectively. The number of operators is taken as 5 per shift. In case of the assumed ‘automatic’ process scenario, the speed of the conveyors is taken as 6 m/min. The number of operators is taken as 3 per shift. The operator cost in both the scenarios is assumed as 16 euros per hour. The transporters in the simulation are assigned as the trolleys. All these data are estimated from real measures in the factory. The average machine speed of each winder is calculated and is taken to be 26 min per bobbin but at different efficiencies for each section of a machine ranging from 72.24% to 91.52%. These efficiencies are estimated from the results obtained from the time study reports and are based on the machine constraints which vary from section to section in the machine. 4. Experimentation The simulation model is required to be validated and standardised to satisfy the scenarios and the parameters discussed previously. This will lead to a flexible system where we can test and compare different scenarios at will and can draw conclusions. 4.1. Test the simulation logic and validation of the model The test is done for the validation of the simulation model. The purpose of the validation is to test the conditions, decision modules and to check the material and information flow to demonstrate the accuracy of the model. The conditions, parameters and constraints mentioned previously in the Section 3 are experimented. The validation of the present model signifies a standard simulation model. This standard model will be used in the future work such as in the comparison of manual and automatic scenarios by changing the model parameters. The simulation showed the proper flow and transfer of materials from the storage area to the machine and finally the dispatch to the packaging area for shipment. The demonstration showed that the model is managing the decision and distribution of bobbins properly as per the shipment date sequence, machine parameters and constraints with a given number of human resources (for validation purpose). The costs (operator and machine) incurred in the process are recorded in the software database for the comparison of the costing parameters. The results obtained for the actual shipment dates are recorded for each order in the excel sheets to compare with the planned shipment date for the determination of the delay or on-time shipment. The validation of the model is established by using the real data from the concerned factory. The concerned data is the same which is used to test the current manual process scenario in the factory. The comparison between the real factory values and the values obtained from the simulation model is mentioned in Table 3 with respect to the key performance indicators i.e. cumulative delay and global cost index. The results obtained are acceptable as per the factory values since the difference in between the real and the simulation values are less than 5% (see Table 3). It shows that the results obtained from the model are consistent and acceptable with the real industrial values. The results obtained from the model enabled for the validation. With the standard model ready, the next step is the comparison of scenarios. 4.2. Testing of the scenarios (manual and automatic) by simulation The comparison of three different scenarios is simulated in the developed model. One scenario is taken for the manual process and two scenarios for the automatic process. The working time for the manual process scenario is taken as weekdays (Monday to Friday). For the two automatic process scenarios the working time is taken as weekdays in the first case and full Table 3 Comparison between real and simulation values for model validation on manual setup. Key performance indicators
Real industrial value
Simulation model value
Gap real/simulated in%
Cumulative delay (in hours) Global cost (index)
1464 100
1403 102.35
4.2 2.35
Table 4 Scenarios for simulation. Scenario
Process setup
Work day
Scenario 1 Scenario 2 Scenario 3
Manual Automatic Automatic
Monday to Friday, only weekdays (current factory scenario) Monday to Friday, only weekdays Full week, including weekends
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week (Monday to Sunday) in the other case (see Table 4). The key performance indicators for the comparison of scenarios are the shipment date (on-time or delay shipment) and the global cost index incurred in the processes. The testing of scenarios is done with the logic and the information which are mentioned in the Section 3.3. Since the model is flexible in nature so it is possible to simulate different conditions by changing the required parameters for each scenario in the model. The results obtained for the comparison are recorded in the excel sheets and in the model database in real time of the simulation run. 5. Results The results are obtained and analysed for each scenario mentioned in the Section 4.2. The shipment delays are measured as the total aggregate of the delays for all the orders in one month. The total aggregate of shipment delays = total number of negative values in the Figs. 6–8. The x-axis represents the actual shipped orders for one month in sequence and y-axis represents the time difference from the planned shipment dates (Positive value = on-time, negative value = delay). 5.1. Scenario 1: Manual process (working hours from Monday to Friday) The total aggregate of shipment delays of the all the orders is found to be 1403 h. 6.72% of the orders out of 1622 orders are delayed in shipment. The delays and on-time shipment of orders is represented in the Fig. 6. The global cost index in this scenario is 102.35 (see Fig. 9). The data used here is the same used for the validation of the model in Section 4.1 because both of the models simulated the current scenario in the factory. 5.2. Scenario 2: Automatic process (working hours from Monday to Friday) The total aggregate of shipment delays of the all the orders is found to be 3733 h. 12.16% of the orders out of 1622 orders are delayed in shipment. The delays and on-time shipment of orders is represented in the Fig. 7. The global cost index in this scenario is 47.67 (see Fig. 9).
Fig. 6. Manual process shipment time differences.
Fig. 7. Automatic process shipment time differences.
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Fig. 8. Automatic process (with WE) shipment time differences.
Fig. 9. Global cost index comparison of scenarios.
5.3. Scenario 3: Automatic process (working hours-full week) The total aggregate of shipment delays of the all the orders is found to be 2684 h. 11.34% of the orders out of 1622 orders are delayed in shipment. The delays and on-time shipment of orders is represented in the Fig. 7. The global cost index in this scenario is 63.54 (see Fig. 9).
5.4. Discussion The manual process scenario which shows the best results in terms of the delays but the global cost is very high because of the flexibility of the operators in managing the orders. The automatic scenario shows a higher value of delays but a very low value of global cost but in the third scenario the delay is reduced when the working hours of weekends are included with some increase in the cost because the working hours on weekends compensate the lack of flexibility involved by the automatic process. The authors suggest that the decision on the implementation should be based on the global cost because the delays incurred in the automatic process can be eliminated using the optimisation algorithms.
6. Conclusions and future In this work, a simulation model is developed with the conditions and parameters of a yarn rewinding section of a dyeing factory which presents a considerable degree of detail and complexity. The logic of the simulation model is designed to cope with the huge number of production parameters, internal transportation of materials, push and pull flow of information and materials, reading inputs and recording the results in the internal and external databases. The validated multi-level model ensures the flexible nature of the model which enables to change the parameters at ease and hence to simulate different scenarios of production. The objective in this case study is to compare the alternate scenarios of production (manual and
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automatic) distribution of yarn bobbins in the yarn rewinding section and to analyse the results obtained in terms of the key performance indicators which are shipment time (on-time or delay) and global cost of the respective process. The results obtained are consistent and acceptable to the industrial values for the manual process setup (Section 5.1) which also enabled for the validation of the model (Section 4.1). Whereas for the automatic process setups (Sections 5.2 and 5.3), the results are obtained and compared on the basis of the parameters and the data derived from the proposed automation setup planning in the company. Thanks to the simulation, it obviously appears that the automatic setup enable a significant reduction of the cost but involve an increase in delays. Since short lead time is one of the main assets of the company, it is not suitable to implement the automation setup in the factory under the current situation.As future work, the optimisation of the production schedule by integrating the meta-heuristic methods [25] in the model which could compensate the lack of flexibility of the automatic setup. This would also select the optimum schedule for production that would satisfy in no delays in shipments leaving only the cost as the determining factor in the implementation, leading to the possible implementation of the automatic setups in the factory. Finally, this simulation modelling of an internal manufacturing supply chain gives numerous possibilities and scenarios for the testing of the present and future proposed models. These comparison results obtained will help to implement the proposed plans which lead to generating maximum profit and staying competitive in the global market. Acknowledgement The authors would like to thank the FEDER funds (European Union) and the Nord-Pas-de Calais region for their financial support. References [1] G.M. Acaccia, M. Conte, D. Maina, R.C. Michelini, R. Molfino, Integrated manufacture of high standing dresses for customized satisfaction, Globalisation Manuf. Digital Commun. Era (1999) 511–523. [2] J. Banks, Handbook of Simulation, Wiley Interscience, New York, 1998. [3] A.M. Law, W.D. Kelton, Simulation Modeling and Analysis, second ed., McGraw-Hill, New York, 1991. [4] Guorong Chen, S.C. Harlock, A computer simulation based scheduler for woven fabric production, Text. Res. J. 69 (1999) 431–439. [5] Banu Y. Ekren, Arslan M. Ornek, A simulation based experimental design to analyze factors affecting production flow time, Simul. Modell. Prac. Theory 16 (2008) 278–293. [6] E. Jack Chen, Young M. Lee, Paul L. Selikson, A simulation study of logistics activities in a chemical plant, Simul. Modell. Prac. Theory 10 (2002) 235–245. [7] Junfeng Wanga, Qing Chang, Guoxian Xiao, Nan Wang, Shiqi Li, Data driven production modeling and simulation of complex automobile general assembly plant, Comput. Ind. 62 (2011) 765–775. [8] Can Ünal, Semra Tunali, Mücella Güner, Evaluation of alternative line configurations in apparel industry using simulation, Text. Res. J. 79 (2009) 908–916. [9] Taedong Kim, Byoung K. Choi, Production system-based simulation for backward on-line job change scheduling, Simul. Model. Pract. Theory 40 (2014) 12–27. [10] Rahime Sancar Edis, Arslan Ornek, Simulation analysis of lot streaming in job shops with transportation queue disciplines, Simul. Model. Pract. Theory 17 (2009) 442–453. [11] Mustafa. Ôzbayrak, Theopisti C. Papadopoulou, Melek. Akgun, Systems dynamics modelling of a manufacturing supply chain system, Simul. Model. Pract. Theory 15 (2007) 1338–1355. [12] S. Nahmias, Production and Operations Analysis, McGraw-Hill, New York, 2005. [13] J. Bukchin, E.M. Dar-El, J. Rubinovitz, Mixed model assembly line design in a make-to-order environment, Comput. Ind. Eng. 41 (4) (2002) 405–421. [14] J.A. Sepulveda, H.M. Akin, Modelling a garment manufacturer’s cash flow using object-oriented simulation, in: R.G. Ingalls, M.D. Rossetti, J.S. Smith, B.A. Peters (Eds.), Proceedings of the Winter Simulation, vol. 2, Association for Computing Machinery, New YorkM 2004, pp. 121-128. [15] G.M. Acaccia, M. Conte, D. Maina, R.C. Michelini, Computer simulation aids for the intelligent manufacture of quality clothing, Comput. Ind. 50 (2003) 71–84. [16] S. Thomassey, Sales forecasts in clothing industry: the key success factor of the supply chain management, Int. J. Prod. Econ. 128 (2010) 470–483. [17] Andrew. Greasley, Using simulation for facility design: a case study, Simul. Model. Pract. Theory 16 (2008) 670–677. [18] Gert Zülch, Halil Ibrahim Koruca, Mikko Börkircher, Simulation-supported change process for product customization: a case study in a garment company, Computers in Industry 62 (2011) 568–577. [19] V. Hlupic, Discrete-event simulation software: what the users want, Simulation 73 (6) (1999) 362–370. [20] N. Robertson, T. Perera, Automated data collection for simulation?, Simul Pract. Theory 9 (2002) 349–364. [21] Arena Simulation by Rockwell Automation,. [22] L.S. Dias, G. Pereira, P. Vik, J.A. Oliveira, Discrete simulation tools ranking: A Commercial Software Packages comparison based on popularity, in: Proceedings of 9th Annual Industrial Simulation Conference, Venice, 2011. [23] W.D. Kelton, R.P. Sadowski, D.T. Sturrock, Simulation with Arena, fourth ed., McGraw-Hill, New York, 2007. [24] D.A. Bodner, L.F. McGinnis, A structured approach to simulation modelling of manufacturing systems, in: Proceedings of the Industrial Engineering Research Conference Atlanta, Georgia, USA, 2002. [25] D.E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Addison-Wesley, New York, 1989.
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Simulation Modelling Practice and Theory journal homepage: www.elsevier.com/locate/simpat
A simulation based comparison: Manual and automatic distribution setup in a textile yarn rewinding unit of a yarn dyeing factory Brahmadeep, Sébastien Thomassey ⇑ GEMTEX/ENSAIT, Roubaix, France University of Lille, North of France, Villeneuve d’Ascq, France
a r t i c l e
i n f o
Article history: Received 19 December 2013 Received in revised form 7 March 2014 Accepted 2 April 2014
Keywords: Simulation model Setup comparison Internal supply chain Lean tools Automation Arena Simulation
a b s t r a c t This paper aims to explain the production flow and the distribution logic of bobbins for rewinding process in a yarn dyeing factory, comparing the different scenarios of production (manual and automatic) using the computer simulation tools. The goal of this project is to build a model in which all the involved processes can be simulated with the consideration of all the parameters and constraints. The simulation model is used as a tool for the comparison of present manual setup and future automated setup for the production management of bobbin distribution in yarn rewinding process in terms of delays and costs. Since, the manual operation involves defaults, improper time management, errors and with the growing competitiveness globally, the companies in Europe need to automate as much as possible their production lines. The expected impacts are to increase the productivity and profitability, to have the possibility to customize the production, to develop production tools, implementation of the lean manufacturing tools. Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction Industries in Europe are facing with pressures to reduce their production costs and to increase the efficiency of their existing staff or equipment. The customer demand is becoming more personalised which requires industries to produce small lot size, in short lead time and in unique specifications. Thus the only way for a producer is to improve their productivity, reactivity, flexibility, and quality. The trend of mass customization and personalized production leads to fundamental changes in the material flow, plant layout and work organisation [1]. Producers have to rely on smart automation of their production system coupled with an adequate use (qualified staff, computer aided setup), efficient and intelligent planning management, and overall supervision and decision systems. This could be very challenging for some companies such as textile companies, and more particularly dyeing and winding production units which are commonly faced with large batches, long process and setup times. Therefore, it is now the requirement to determine the best possible solution and find the feasibility of such change in a very short time. This project concerns the development of automated feeding of winders in order to fight against outsourcing caused by the cheaper workforces and to fight with the lost employment in the textile sector in Europe. ⇑ Corresponding author at: GEMTEX/ENSAIT, 2 allée Louise et Victor Champier, 59100 Roubaix, France. Tel.: +33 (0)3 20 25 64 64; fax: +33 (0)3 20 24 84 06. E-mail address: [email protected] (S. Thomassey). http://dx.doi.org/10.1016/j.simpat.2014.04.002 1569-190X/Ó 2014 Elsevier B.V. All rights reserved.
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The concept behind this project is based on automation, optimal flow management (internal supply chain management), lean tools and setup comparison of the yarn rewinding processes (manual and automatic) with the support of modelling and simulation. Companies with the most optimised and automated production tools will be privileged in order to obtain the best product at an acceptable cost. The objectives of this paper are to identify and to analyse the present scenario of the supply chain of the production plan, to develop a standard simulation model with consideration of all parameters and constraints, to identify Key Performance Indicators (KPI’s), to analyse the results obtained from the simulations, to simulate and analyse different scenarios (manual and automatic input of bobbins), to compare and analyse the results obtained from the simulated scenarios and to optimise and implement the results in the actual production. The paper is structured as follows. In Section 2, an overview of the existing literature and case studies related to this work is presented. In Section 3, the methodology of this work is described along with the detailed definition of the problem, parameters, constraints and simulation model logic. In Section 4, the validation of the simulation model and the experiments done (testing scenarios) in this work are explained. In Section 5, the results obtained from the experiments performed are analysed and formulated (comparison of results obtained from manual and automatic process simulation models). An overview of the future scopes and development of this work is explained with a global conclusion in the Section 6.
2. State of the art 2.1. Simulation in manufacturing The simulation is widely recognised as the best and most suitable methodology for investigation and problem-solving in real-world complex systems in order to choose correctly, understand why, explore possibilities, diagnose problems, find optimal solutions and transfer R&D results to real systems [2]. There are various types of simulation models. The discrete event simulation involves the modelling of a system as it progresses through time and is particularly useful for modelling queuing systems [3]. There are many examples of queuing systems: manufacturing systems, banks, fast food restaurants, airports, etc. A major facet of discrete event simulation is its ability to model random events based on standard and nonstandard distributions and to predict the complex interactions between these events. For instance, the effects of a machine breakdown on a production line can be modelled. There are many factors which contribute to the use of the simulation in the manufacturing. The advantages include analysing the manufacturing processes, ease of use, flexibility, ability to model dynamic and stochastic nature of production systems, ability to test various scenarios of production, layout design, logistics, material handling, etc. which lead us to have a close insight into the performance of a manufacturing company [4–8]. In the following paragraphs, few successful examples concerning the applications of simulation in the manufacturing sector are discussed. The factors that affect production flow time are explained using the simulation in [5] with the parameters which include the comparison of the two different layout plans. The simulation included the scheduling rules, machine breakdowns, batch sizes and transporters. The work demonstrated the use of simulation to play with the different setting level of manufacturing parameters which affect the performance of a facility. The application of simulation to study and analysis of internal logistics in a chemical manufacturing plant is demonstrated in [6]. The flow of materials involves a continuous flow of materials, such as liquid, gas or solid through the manufacturing and logistics processes. With the aid of simulation the capacity requirement for logistic operations were determined. The model described is extremely flexible in terms of the user’s ability to make changes for different scenarios. Most of the input parameters and data are table-driven. Users can test different scenarios without changing the simulation model, and the model generates several useful reports automatically. The concept of simulation in manufacturing can also be seen in case of a very complex system like in an automobile assembly plant, the authors of [7] demonstrated a data driven simulation model. The significance of this methodology adopted is that it provides a rapid prototyping, capability for production system modelling and enables a quick analysing and remodelling capability to respond to the fluctuation of demands. The simulation modules for assembly line and material handling system of the plant floor are analysed and are developed where the data driven approach is implemented to enable the modelling and simulation of the complex assembly plant in a real time like condition and therefore effectively improve the responsiveness and flexibility of the production line. The methodology used for simulation is very logical in relation to the development of a simulation model to analyse the complex production. The steps involved in the respective manner are: data preparation, model generation, model validation and scenario simulation. The floor operations, production type, assembly line configuration and material handling processes are defined for setting the parameters in the simulation model. The modules developed for the simulation are assembly line models and the material handling modules which are interlinked to each other. The simulation based production decision enabled a lot of automobile manufacturers to quickly adjust the assembly lines for achieving maximum labour utilisation and lowering production cost using the concepts of Lean, JIT (just-in-time) while satisfying the market demands. The application of simulation in manufacturing can be seen in case of some modern complex concepts like backward online change scheduling in production [9] where the insight is the WIP management, lean processes and reduction of idle times. Also, in the case of the analysis of lot streaming in job shops with transportation queue disciplines where there are
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multi-products and sub lots [10]. It considered the idling and availability, setup time of the machines, load-unload and trip times of the transporter, weekly demands and job routings. The manufacturing units having the concepts of Make-to-Order utilise the advantages of the simulation based models. The proposed model in [11] is developed using a system dynamics approach where the operations performed within a supply chain are a function of a great number of key variables which have strong interrelationships. The modelling effort focused on measuring the supply chain system performance in terms of key metrics such as inventory, WIP levels, backlogged orders and customer satisfaction at all four echelons. The methodology for the simulation follow the steps starting with the logic diagrams, level diagrams, definition of model operation conditions (initial and in-operation) and the testing of the different scenarios. In [11], it shows that an efficient modelling with the proper links and interrelations is vital for a thorough understanding of the system dynamics and behaviour which is a key step towards the optimised performance. These above production designs and manufacturing plans are very similar to the textile production setups and in case of a yarn-rewinding section of a yarn dyeing unit in terms of the production parameters and constraints. However, the specificities are different which demands a concentrated study of simulation in the domain of textile and garment manufacturing. 2.2. Simulation in textile and garment manufacturing The textile and apparel manufacturing units are indeed very complex systems because of the number of production parameters and high variety of references. Nowadays, the demand is also moving towards more personalised products which in result would give rise to small lot sizes, short lead times and unique specifications. To compete in this changing environment the producer has to improve their productivity, reactivity, flexibility, and quality. Determining the best facility design is a classical industrial engineering problem [12]. Each layout problem has its own unique characteristics and the objectives in a plant layout study might include one or more of the following: minimizing the investment required in new equipment, minimizing the time required for production, utilizing the existing space most efficiently, minimizing the materials handling cost, facilitating the manufacturing process, etc. Flexibility is also an essential requirement in order to respond to shorter product life cycles, low to medium production volumes, changing demand patterns, and a higher variety of product models and options [13]. With the use of the process simulation, the analysis of various scenarios could be executed within a short time with more ease. For instance, the scenarios of an automatic production and a manual production could be easily compared with the given performance indicators like cost and lead times. In the following paragraphs, few successful examples concerning the applications of simulation in textile and apparel manufacturing sector are discussed. The problems such as planning and scheduling of the fabric production orders, to achieve the shipment on time, maximising the loom utilisation, and labour requirement are the major concern in [4]. The processes are simulated and analysed in order to find feasible solutions to the problems. The results obtained demonstrated the flexible nature of the simulation model which easily copes with the textile manufacturing requirements and the application can be extended to other textile sectors like apparel production and dyeing units. The main challenges in garment manufacturing units are the labour force for production processes, small quantities with few repetitions, frequently changing styles and short delivery times. The aim is to meet the challenges inherent in producing a greater range of products, but at the same time trying to keep the economy-of-scale benefits of mass production by using computer simulations [14]. In the apparel production sector, the simulation tools are used to link all processes of a flexible production system in [15]. The objective is to have an intelligent manufacture of quality clothing. The setups are analysed by computer simulation in order to verify the actual potentials of the facilities with a foresight to find the most effective plans for long run operations by adaptive exploitation of flexibility at different levels with effective schedules to meet the growing demands which are personalized, quick response and total quality. The simulation techniques can be implemented for the forecasting of the sourcing processes of a retailer and a manufacturer that enables to quantify the impact of the forecast errors on the financial and supply performances which are linked with the production plan, inventory level, service level and profit margin of the manufacturer [16]. The simulation in the textile and apparel manufacturing can be implemented in facility design like in case of the automobile assembly units. The authors of [8] demonstrate a comparison between a traditional assembly line and of a U-line assembly line. In [17], a discrete event simulation model is developed and used to estimate the storage area required for a proposed overseas textile manufacturing facility. The objective is to predict the amount of WIP in the proposed layout under two different scenarios of dye capacity which is identified as the bottleneck in the overall plant capacity. The ability to store attribute values and queuing levels at an individual product level make the simulation more detailed which is a requirement in case of the apparel and textiles. The case study in [17] shows that discrete event simulation in its facilitation role can provide a qualitative understanding of behaviour over and above the normal benefits associated with this technique. A case study by the authors in [18] explains about a company which is trying to eliminate the effect of long lead times, high logistic costs and improve customer satisfaction. To achieve the goals of the company, various simulation model alternatives are designed in order to test and compare the models and to find the most suitable result which can be implemented later. They take five situations namely initial simulation and model validation, additional workload for customised products, reducing the duplication of products, enlargement of workplaces and investment into appropriate equipment. Each situation is simulated and analysed for the results which lead to many positive findings. They are able to increase the number of processed customized orders twenty folds, the costs per piece decreased by 84% and obtain a high service level.
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These works verifies that the simulation models are very powerful and efficient to enable the companies to achieve their goals in this competitive world by analysing different scenarios by simulations and later implementing the proposed organizational changes. This technique of simulation is perfectly adapted to sectors where the production constraints are huge and complex like in dyeing, winding and other production sectors in order to test and verify different scenarios of production with ease. The comparison based upon the simulation models can be implemented for instance to compare the manual and automatic setups and also for the facility design but this would require customising the existing model as per the changes in the respective work flows. 2.3. Simulation software Simulation Software is the heart and brain of a simulation model. It is the actual place where all the possibilities are constructed, processed, analysed, optimised and later the derived conclusions get implemented to the real situation. The analysis in [19] suggests the simulation software with good visualization or animation properties are easier to use but limited in case of complex and non-standard problems. The various techniques of data entry for simulation model are explained in [20]. Nowadays a lot of simulation software products are available in the market for the supply chain management including for the internal factory supply chain and production flows. The use of Witness in textile production can be seen in [4] and in some automobile industries. Arena Simulation is simulation software by Rockwell Corporation [21] and it is used in different application domains: from manufacturing to supply chain (including logistics, warehousing and distribution) from customer service and strategies to internal business processes. It provides the user with object libraries for systems modelling and with a domain-specific simulation language SIMAN. The application of this software in manufacturing and textile/apparel can be seen in the papers [5,7,8,10,17]. In [7] it is integrated with VBA coding which enhances the possibilities of the simulation model. The simulation environment uses a flowchart-based modelling methodology that facilitates the description of discrete- event systems. Systems are described using the point of view of the entities that flow through them using the available resources. Models are structured in a hierarchical and modular way. They are defined by means of a flowchart diagram and static data [23]. The simulation was able to demonstrate the concepts of TRANSPORTER, WAIT-SIGNAL and BUSY-IDLE modules in [24]. These modules play an important role in modelling an automated manufacturing process or with an involvement of scheduling, queuing and waiting. As per the authors of [22], The Arena Simulation is one of the most popular and powerful simulation software in its domain. Thus, we select this software for our work. 3. Methodology In simulation issues, the methodology has to be developed specifically with respect to the project requirements and parameters. For examples in [7,11], the methodology is defined for the simulation in manufacturing. We have taken these works as a baseline and developed our project specific methodology. The schema of which is stated in the Fig. 1. The following sub-sections describe the steps mentioned in Fig. 1 with details. 3.1. Identification and analysis of the present scenario (parameters and constraints) The manufacturing unit in our case study is a yarn dyeing factory. The material flow in the factory starts from the dyeing section where the yarns on the bobbins are dyed in a fully automated process. The dyeing production is scheduled according
Fig. 1. Methodology for the project.
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to the shipment date and the management of dyeing batches. Indeed, dyeing batches should be strictly sequenced to follow the cycle of colour from the cleanest to the darkest. Then, the bobbins are transferred to a temporary storage where they wait for an operator to be picked up and transported manually to the winding machines by trolleys (push from the dyeing section). The winding process is done in order to transfer the yarn from loose tube bobbin to a universal tight cone bobbin which is clean and continuous. The operator checks the shipment dates of the orders and accordingly transfers the trolleys to the winding machines. After the winding is done the bobbins are automatically transferred to the packaging section and finally they are shipped out of the factory. Thus the simulation model we have to develop should integrate both push and pull flows as illustrated in Fig. 2. The winding section is composed of four yarn winding machines called (A, B, C and D) in the following. Each machine is having six sections (Sec I, II, III, IV, V and VI) while the sections have ten winders each (60 winders per machine). The general layout of the machines and the sections on the production floor is presented in Fig. 3. One operator is responsible for the distribution and transportation of bobbins and one operator per each machine is responsible for the manual input of bobbins on the machine winders (5 operators per shift). The factory works 3 shifts per day from Monday to Friday.
Fig. 2. Work flow in the factory.
Fig. 3. Basic layout of machines and sections and operators.
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Brahmadeep, S. Thomassey / Simulation Modelling Practice and Theory 45 (2014) 80–90 Table 1 Machine constraints. Constraints
M/C: A
M/C: B
M/C: C
M/C: D
Colour Control Tension Control Thickness control Multiply winding
Yes Yes Yes Only Sec I
No No Yes Yes
No Yes Yes Only Sec I
No Yes Yes Only Sec I
The main assets of the company are the high variety of yarn specifications, the small lot sizes and the lead times. To meet the wide variety of products, each machine/section has its own features which enable to process one or several yarn specifications. Indeed, some yarn specifications involve specific processes to reach the required quality in terms of colour, thickness, etc. The machine constraints are the same for the ten winders in a section i.e. each section can be identified as a system of 10 winders with same machine parameters and constraints. Table 1 illustrates the respective machine-section constraints in relation to the yarn bobbins. In the case of multiply yarn winding, if the yarn undergoes the self-twisting process then they are considered to have the same machine constraints as of the single ply yarns. Only the non-self-twisted multiply yarns are considered to have the constraints of a multiply yarn on the machine.
3.2. Design of the logic required for the simulation The potential and theoretical yarn qualities (types) found is in hundreds which means the product variety and parameters is a huge number. It is not feasible to plan the distribution and management on this basis. For the simulation purposes, there is a need to find a logic taking into account a reasonable number of product categories. Since the bobbins are ultimately distributed to the machines, it is decided to plan the product category as per the machines. The bobbins are categorised on the basis of machine constraints and a total of 13 categories are obtained (Fig. 4). The category distribution of bobbins takes into account the constraints mentioned in the Table 1. The explanation of the various possibilities for a category of yarn-bobbin to be distributed to the respective winding machine can be seen in Table 2. The machine setup does not change for the shift of one category of bobbin to another because the categories are already determined as per the machine constraints. As only the similar type of the category of bobbin go to the respective machine section. With the consideration of all the global parameters a simulation logic diagram is developed on the basis of which the actual modelling of simulation is done on the software (Fig. 5). In the case of the manual flow process, the logic explains the input of the yarn-bobbins from the dyeing section to the winding section (real-push entry) where there is a temporary storage. These are assigned on their respective trolleys whose
Fig. 4. Logic tree for the classification of the categories.
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Brahmadeep, S. Thomassey / Simulation Modelling Practice and Theory 45 (2014) 80–90 Table 2 Possibilities of winding for each category with respect to the machines. Category
Machine/section
1 2 3 4 5 6 7 8 9 10 11 12 13
M/C: A M/C: D, M/C: C or M/C: A if place is available M/C: A M/C: D, M/C: C or M/C: A if place is available M/C: A (section I) M/C: D (section I), M/C: C (section I) or M/C: A (section I) if place is available M/C: A M/C: D, M/C: B, M/C: C or M/C: A if place is available M/C: A M/C: D, M/C: B, M/C: C or M/C: A if place is available M/C: A (section I) M/C: B, M/C: D (section I), M/C: C (section I) or M/C: A (section I) Not going to any of the machines in the study
Fig. 5. Simulation model logic.
capacity is dependent on the lot size. The bobbins contain all the parameters like entry data, yarn qualities, yarn product category, and machine requirements. Then from the temporary storage they are transported in the trolleys to the winding machines with the help of a sequence which is based upon the delivery date of the order, machine utilisation and present human resources (pull flow sequence). The recording of the process time, in-process constraints are saved automatically in the database. Later there is the recording for the performance indicators like the delay or on-time delivery, and costing. A similar approach is executed for the simulation logic of the automatic process with different constraints and parameters. This logic aid in the comparison of the different scenarios like for the manual and automatic process which could to be easily analysed and the results obtained could play an important role in the final decision making. 3.3. Modelling of the simulation for the scenarios The modelling is done with the software Arena Simulation. The decision making, distribution logic and material movement are linked to the parameters like the categories of the bobbins, speed of the operator with loaded and empty trolley, working hours, machine efficiency (each section of the machine have different efficiencies), minimum WIP (work in process-lean), number of operators, operator and machine cost per hour, conveyor speed, etc. The model is a multi-level; consisting of various levels (production floor level, machine level and section level) and modules (material flow and operations, information flow, transport, reading and recording, etc.). In the model the input information for each category of the bobbins is imported from the excel files. One month duration of real data is taken for the inputs: this represents 1622 orders. Decision and Hold modules are used for the distribution of the bobbins to their respective machine sections. The initial pull sequence (the order in which the bobbins are to be taken from the temporary storage to the machines on trolleys) for the model is taken as per the delivery dates for the orders which is also read from the excel files. The pull of bobbins is also dependent upon the human resource and machine work in progress i.e. only when the machine winders WIP is one or less then only the next bobbin in line would be assigned onto it. The pull system and lean concepts (minimum WIP) are linked operationally from the machine winder level up to the storage area.
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The simulation is performed on three scenarios: manual, automatic input working weekdays and automatic input working full week. For the ‘manual’ scenario, the distance and the speed of the operator to transfer the bobbin trolley to the machines are taken as per the actual time study analysis in the factory. The operator speed is taken as 23 m/s and 28 m/s with load and without load respectively. The number of operators is taken as 5 per shift. In case of the assumed ‘automatic’ process scenario, the speed of the conveyors is taken as 6 m/min. The number of operators is taken as 3 per shift. The operator cost in both the scenarios is assumed as 16 euros per hour. The transporters in the simulation are assigned as the trolleys. All these data are estimated from real measures in the factory. The average machine speed of each winder is calculated and is taken to be 26 min per bobbin but at different efficiencies for each section of a machine ranging from 72.24% to 91.52%. These efficiencies are estimated from the results obtained from the time study reports and are based on the machine constraints which vary from section to section in the machine. 4. Experimentation The simulation model is required to be validated and standardised to satisfy the scenarios and the parameters discussed previously. This will lead to a flexible system where we can test and compare different scenarios at will and can draw conclusions. 4.1. Test the simulation logic and validation of the model The test is done for the validation of the simulation model. The purpose of the validation is to test the conditions, decision modules and to check the material and information flow to demonstrate the accuracy of the model. The conditions, parameters and constraints mentioned previously in the Section 3 are experimented. The validation of the present model signifies a standard simulation model. This standard model will be used in the future work such as in the comparison of manual and automatic scenarios by changing the model parameters. The simulation showed the proper flow and transfer of materials from the storage area to the machine and finally the dispatch to the packaging area for shipment. The demonstration showed that the model is managing the decision and distribution of bobbins properly as per the shipment date sequence, machine parameters and constraints with a given number of human resources (for validation purpose). The costs (operator and machine) incurred in the process are recorded in the software database for the comparison of the costing parameters. The results obtained for the actual shipment dates are recorded for each order in the excel sheets to compare with the planned shipment date for the determination of the delay or on-time shipment. The validation of the model is established by using the real data from the concerned factory. The concerned data is the same which is used to test the current manual process scenario in the factory. The comparison between the real factory values and the values obtained from the simulation model is mentioned in Table 3 with respect to the key performance indicators i.e. cumulative delay and global cost index. The results obtained are acceptable as per the factory values since the difference in between the real and the simulation values are less than 5% (see Table 3). It shows that the results obtained from the model are consistent and acceptable with the real industrial values. The results obtained from the model enabled for the validation. With the standard model ready, the next step is the comparison of scenarios. 4.2. Testing of the scenarios (manual and automatic) by simulation The comparison of three different scenarios is simulated in the developed model. One scenario is taken for the manual process and two scenarios for the automatic process. The working time for the manual process scenario is taken as weekdays (Monday to Friday). For the two automatic process scenarios the working time is taken as weekdays in the first case and full Table 3 Comparison between real and simulation values for model validation on manual setup. Key performance indicators
Real industrial value
Simulation model value
Gap real/simulated in%
Cumulative delay (in hours) Global cost (index)
1464 100
1403 102.35
4.2 2.35
Table 4 Scenarios for simulation. Scenario
Process setup
Work day
Scenario 1 Scenario 2 Scenario 3
Manual Automatic Automatic
Monday to Friday, only weekdays (current factory scenario) Monday to Friday, only weekdays Full week, including weekends
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week (Monday to Sunday) in the other case (see Table 4). The key performance indicators for the comparison of scenarios are the shipment date (on-time or delay shipment) and the global cost index incurred in the processes. The testing of scenarios is done with the logic and the information which are mentioned in the Section 3.3. Since the model is flexible in nature so it is possible to simulate different conditions by changing the required parameters for each scenario in the model. The results obtained for the comparison are recorded in the excel sheets and in the model database in real time of the simulation run. 5. Results The results are obtained and analysed for each scenario mentioned in the Section 4.2. The shipment delays are measured as the total aggregate of the delays for all the orders in one month. The total aggregate of shipment delays = total number of negative values in the Figs. 6–8. The x-axis represents the actual shipped orders for one month in sequence and y-axis represents the time difference from the planned shipment dates (Positive value = on-time, negative value = delay). 5.1. Scenario 1: Manual process (working hours from Monday to Friday) The total aggregate of shipment delays of the all the orders is found to be 1403 h. 6.72% of the orders out of 1622 orders are delayed in shipment. The delays and on-time shipment of orders is represented in the Fig. 6. The global cost index in this scenario is 102.35 (see Fig. 9). The data used here is the same used for the validation of the model in Section 4.1 because both of the models simulated the current scenario in the factory. 5.2. Scenario 2: Automatic process (working hours from Monday to Friday) The total aggregate of shipment delays of the all the orders is found to be 3733 h. 12.16% of the orders out of 1622 orders are delayed in shipment. The delays and on-time shipment of orders is represented in the Fig. 7. The global cost index in this scenario is 47.67 (see Fig. 9).
Fig. 6. Manual process shipment time differences.
Fig. 7. Automatic process shipment time differences.
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Fig. 8. Automatic process (with WE) shipment time differences.
Fig. 9. Global cost index comparison of scenarios.
5.3. Scenario 3: Automatic process (working hours-full week) The total aggregate of shipment delays of the all the orders is found to be 2684 h. 11.34% of the orders out of 1622 orders are delayed in shipment. The delays and on-time shipment of orders is represented in the Fig. 7. The global cost index in this scenario is 63.54 (see Fig. 9).
5.4. Discussion The manual process scenario which shows the best results in terms of the delays but the global cost is very high because of the flexibility of the operators in managing the orders. The automatic scenario shows a higher value of delays but a very low value of global cost but in the third scenario the delay is reduced when the working hours of weekends are included with some increase in the cost because the working hours on weekends compensate the lack of flexibility involved by the automatic process. The authors suggest that the decision on the implementation should be based on the global cost because the delays incurred in the automatic process can be eliminated using the optimisation algorithms.
6. Conclusions and future In this work, a simulation model is developed with the conditions and parameters of a yarn rewinding section of a dyeing factory which presents a considerable degree of detail and complexity. The logic of the simulation model is designed to cope with the huge number of production parameters, internal transportation of materials, push and pull flow of information and materials, reading inputs and recording the results in the internal and external databases. The validated multi-level model ensures the flexible nature of the model which enables to change the parameters at ease and hence to simulate different scenarios of production. The objective in this case study is to compare the alternate scenarios of production (manual and
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automatic) distribution of yarn bobbins in the yarn rewinding section and to analyse the results obtained in terms of the key performance indicators which are shipment time (on-time or delay) and global cost of the respective process. The results obtained are consistent and acceptable to the industrial values for the manual process setup (Section 5.1) which also enabled for the validation of the model (Section 4.1). Whereas for the automatic process setups (Sections 5.2 and 5.3), the results are obtained and compared on the basis of the parameters and the data derived from the proposed automation setup planning in the company. Thanks to the simulation, it obviously appears that the automatic setup enable a significant reduction of the cost but involve an increase in delays. Since short lead time is one of the main assets of the company, it is not suitable to implement the automation setup in the factory under the current situation.As future work, the optimisation of the production schedule by integrating the meta-heuristic methods [25] in the model which could compensate the lack of flexibility of the automatic setup. This would also select the optimum schedule for production that would satisfy in no delays in shipments leaving only the cost as the determining factor in the implementation, leading to the possible implementation of the automatic setups in the factory. Finally, this simulation modelling of an internal manufacturing supply chain gives numerous possibilities and scenarios for the testing of the present and future proposed models. These comparison results obtained will help to implement the proposed plans which lead to generating maximum profit and staying competitive in the global market. 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