Ijtra130523

International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75

IMPROVEMENT OF SUPPLY CHAIN MANAGEMENT BY MODEL ANALYSIS R.K.VERMA, K.M.MOEED, K.G.SINHA Department of Mechanical Engineering Integral University, Lucknow, India

Abstract—This paper analyses the case of any production system by making a model for the existing or a new industry we can analyze the different aspects of manufacturing and then by using various techniques we can minimize the flow from one end to another end so that the lead time decreases and productivity increases.

Index Terms—supply chain management, productivity, model analysis. (key words)

I. INTRODUCTION Simulation models are used in evaluating supply chain configuration decisions because of their ability to represent the problem realistically and to capture a wide range of factors.

They can also be applied to select the most appropriate configuration Trends of supply chain management have increasingly influenced independent companies or autonomous divisions along the supply chain to move away from adversarial towards cooperative arrangements. These trends stem from several driving forces on both the demand and supply sides of the chain. There are two key driving forces on the demand side, namely globalisation and mass customisation. Globalisation provides plentiful opportunities to multinational firms to capitalise on economies of scale and scope in research, product development, and manufacturing.

Further, they can expand their operations to capture increasingly similar demands of end users for global products.

To tap these potential opportunities, they must deal with logistics problems such as long delivery lead times, outsourcing vendors who are located on a different side of the globe, complex transportation costs, high buffer stock, and complex transaction costs including tax and foreign exchange.

II. LITERATURE REVIEW Simulation models are used in evaluating supply chain configuration decisions because of their ability to represent the problem realistically and to capture a wide range of factors.

They can also be applied to select the most appropriate configuration from a limited set of alternative configurations.

Existing models representing configuration, related issues are grouped according to the simulation-modelling approach used namely, process-oriented simulation, object-oriented

simulation, and agent-oriented simulation. We discuss these below.

A. Process-Oriented Models A simulation model is described by a sequence of processes initiated by events occurring in the system. This approach is attractive from the model- integration point of view. Supply chain process models can be transformed into simulation models. Bowersox (1972) presents an early study on application of simulation for long-term distribution planning.

The model consists of standardized nodes representing manufacturing plants with adjacent warehouses, distribution centres, consolidated shipping points, and demand units. In the case studies reported, simulation is used to evaluate several preconfigured supply chain design alternatives. Decisions to be made include capacity expansion and location of new facilities.

The author indicates that data availability and complex model building are major obstacles for widespread use of simulation in supply chain management. The business process orientation is adopted by Van der Vorst et al. (2000). The supply chain is defined as consisting of multiple business processes governed by design variables, defined as configuration level and operational level. Thus, a simulation model is used for decision-making in both strategic and operational decisions.

The business process modelling formalism used is Petri-nets, which are often considered over other process and network modelling methods because they are based on sound theoretical principles and enable some analytical evaluation. Strategic and operational supply chain design decisions to be made are identified following the principle that supply chain performance can be improved by reducing the impact of various sources of uncertainty. Configuration-related decisions are implementation of real-time inventory management information systems and reallocation of some of the supply chain management functions. The supply chain performance is evaluated for numerous scenarios, where each scenario is characterized by a set of design variables with specified values.

It is reported that the adoption of decisions made on the basis of simulation modelling has resulted in major performance improvements. Petri-nets also have been used for simulation of the manufacturing supply chain by Dong and Chen (2001).Ganeshan et al. (2001) simulate the performance of a retailing supply chain. The simulation model takes the supply

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International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75 chain network structure as an input parameter. Inventory cycle time, return-on-investment (ROI), and service levels are measured for several scenarios characterized by forecasting accuracy, information exchange mechanism used, and planning cycle length. Simulation results show that all three factors have significant impact on supply chain performance.

Process-oriented simulation of existing systems and envisioned systems for a logistics services provider is explored

by Jain et al. (2001). The modelling objective is to evaluate the possible benefits of replacing legacy IT systems and business processes. Performance measures are service level, inventory turns, and order-to-delivery lead time. Processes represented in the model are order fulfilment, procurement, and demand-andsupply planning. The authors emphasize the importance of providing an adequate level of abstraction, which should correspond to modelling objectives. Bag- chi et al. (1998) describes a supply chain simulator developed at IBM. This simulator defines seven typical supply chain processes available for model composition: customer, manufacturing, distribution, transportation, inventory planning, forecasting, and supply planning processes. Performance measures characterizing customer service, inventory, resources, and returns are collected during simulation. Similarly, Ingalls and Kasales (1999) present a simulation model used for supply chain analysis at Compaq. The model is used to answer managerial questions concerning customer service and profitability of the entire supply chain, as well as that of individual units. It includes eight standardized structures, such as customer, company, inventory site, manufacturing site, geo (i.e., sales component where revenue and costs are accounted for), and country. Thus, the model emphasizes global aspects of the supply chain. The model uses 59 input data tables and provides 112 output data tables. Schunk and Plott (2000) describe a tool, Supply Solver, which incorporates various specific features for specifying supply chains. For instance, an interface for inputting distances between supply chain units is provided. Persson and Hager (2002) apply simulation to select among three alter- native configurations of a manufacturing supply chain. Simulation can be perceived similar to the scenario-based approach, where a scenario is de- fined by a particular supply chain configuration under evaluation.

B. Object-Oriented Models The object-oriented approach allows the designing of modular simulation models. In the case of supply chain configuration, that implies compilation of the supply chain network from a set of standardized objects. The object-oriented approach also makes easier the transition between model development and executable software.

Several existing works on object-oriented supply chain simulation at- tempt to identify main classes characterizing supply chains in general. Alfieri and Brandimarte (1997) define key classes for representing demand points, factories, stocking points, and routing. The authors show the use of generalization and inheritance to describe specific management policies. For

instance, the general stock point class has two child classes representing (R, Q) and (s, S) inventory management policies, respectively. A simulation workbench for analysing information-sharing policies at the operational level is developed by Ng et al. (2002). It contains several classes used to represent a supply chain that include decision-making classes for forecasting and inventory planning, and classes for structural supply chain elements. Van der Zee and van der Vorst (2005) use object-oriented simulation model development to improve separation of concerns with particular emphasis on better representation of control elements, which tend to be dispersed anywhere in the simulation model. An object-oriented supply chain simulation system named SISCO has been developed by Chatfield et al. (2006). The system allows users to specify supply chain structure and management policies using a user friendly graphical interface. Users' inputs are saved in an XML-based Sup- ply Chain Modelling Language (SCML) format. The XML document obtained is used to generate an executable supply chain simulation model by mapping its elements to specific classes in the supply chain simulation library. The library contains the implementation of classes representing or- der, supply chain arcs, and nodes, and several manager and actor classes, which are implemented using a general purpose programming language called Java.

Hung et al. (2006) develop a supply chain simulation model for production scheduling purposes. The supply chain network is composed of generic nodes. Each node has three components: 1) inbound material management, 2) material conversion, and 3) outbound material management. The class diagram is used to design the model structure of each node. It describes various mechanisms available for material handling and processing.

C. Agent-Based Models Agent-based approaches attempt to capture collaborative and implicit aspects of supply chain behaviour. Swaminathan et al. (1998) propose an agent-based architecture for building and executing networked supply chain models. Developed models allow describing issues related to net- work structure evaluation according to lead time, transportation cost, currency fluctuations, inventory control, information exchange, supplier reliability, flexibility, and others. The architecture is based on employing a generic agent, which can be specialized to perform various supply chain activities. Agents communicate with each other by sending messages. The processing of each message is governed by a set of rules (for example, rules defining an inventory replenishment policy). Types of agents and available control policies are structured into the supply chain library. Reduction of model development workload is identified as the key advantage of this architecture, which is achieved by supporting modular model structure and the reuse of existing components (agents and control policies).

A supply chain simulation framework proposed by Van der Zee and Van der Vorst (2005) also advocates use of the agentbased approach.

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International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75 Agents are used to represent infrastructural elements of the supply chain as well as managers of these elements. External agents represent suppliers and customers. Internal agents operate according to allocated jobs and their local intelligence.

They transform available flow items (i.e., products, information). The agent-based approach is implemented using ARENA. The authors also list several key requirements for an efficient supply chain simulation modelling tool. These include an appropriate user interface, which facilitates trust building in collaborative decision making, and ease of handling modelling scenarios. Efficiency of agent-based modelling largely depends upon developed agent capabilities, which are often limited to most basic behaviour.

III. DEVELOPMENT OF SUPPLY CHAIN CONFIGURATION SIMULATION MODELS Development of simulation models is a complex process.

General simulation modelling methodologies have been developed (Law and Kelton (2000)). However, simulation models tend to be rather case specific, thus requiring a major development effort. Therefore, specific modelling tem- plates, methods, and tools for a particular problem domain are useful.

In the case of supply chain configuration, development of simulation models can be facilitated by exploring several specific characteristics including 1) A high level of abstraction 2) Representing two main elements, namely, supply chain nodes and arcs connecting nodes 3) Interactions with other supply chain configuration models Commercially available simulation packages have attained a high level of maturity. Therefore, using these packages for development of supply chain configuration models and specific utilities facilitating the development process is advisable, instead of relying on custom tools. The benefits of using Commercial-Of-The-Shelf (COTS) software in the framework of optimization and simulation are also identified by Vamanan et al. (2004).

Fig. 1 Integrated simulation model building.

A supply chain simulation model is developed using data from the supply chain management information system, and is initially specified using UML. If simulation is used to evaluate supply chain configuration optimization results, then optimization results are an important data source. The decisionmodelling system generates the simulation model by transforming information models into a specific simulation modeming language, which is generated on the basis of a predefined template. The template does not contain any simulation objects. It only contains procedures for executing control of the generic functions and data declarations. The procedures have a uniform design. Different procedures can be developed to perform the same activity. Thus, different management policies can be analysed. The generated simulation model can also be manually edited by a user to incorporate features not represented in the information models or not sup- ported by the model generation mechanism.The decision-modelling system also transforms input data in a format suitable for efficient execution of the simulation model.

This format is referred to as the modelling techniques’ specific data model.The generated simulation model is executed by a commercially avail- able simulator.

IV. APPROACH The proposed simulation model building approach utilizes two main concepts: 1) Separation between data and the model 2) A generic representation of supply chain units.

The main stages of the model building approach are shown in Fig

Fig 2 A generic representation of supply chain unit

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International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75 is implemented using Visual Basic (VB). It creates ARENA objects using the ActiveX technology (actually, the same data model can be used to create a simulation model in other simulation modeling environments supporting the ActiveX technology).

A separate submodel is used to represent CustomerZone objects. This sub-model is used to generate customer demand and to serve as a final destination for finished products.

The main model generation transformations are summarized as follows:

Fig 3 A class diagram of generic supply chain unit.

V. MODEL GENERATION The simulation model is generated on the basis of the object diagram. The object diagram contains realizations of classes shown in Fig. 9.3. Realizations are created according to optimization outcomes (for instance, optimi- zation yields that three out of five manufacturing units are to be opened at selected locations; objects representing these three manufacturing units are created) or for a given fixed supply chain configuration to be evaluated.

Different mechanisms are used to represent various entities from the supply chain object model. In the case of using ARENA (Rockwell, 2001) as a simulation modeling tool, a supply chain unit object is represented as a standardized sequence of simulation modeling blocks, customer zones are represented using a differently structured sequence of simulation mod- eling blocks, products and materials are represented using simulation mod- eling entities, and resources are represented using the resource module.

An ARENA submodel is generated for every supply chain unit included in the configuration (i.e., for every SupplyChainUnit object). Such a sub- model for one of the supply chains units is shown in Fig. 9.4. The FlowTransformation object is transformed into a sequence of processes realiz- ing manufacturing order for processing, setting up resources, requesting materials from the stock, and finally assembling the product. The object diagram prescribes that flow transformation is needed and allows the set- ting of variables in the ARENA model (for instance, the setupTime attrib- ute is used to generate a corresponding variable in the ARENA simulation model). At the same time, the object diagram does not specify the flow transformation process. That is perceived as model method and modeling tool specific data, which determine transformation of the object in ARENA blocks. Products and materials are represented by ARENA entities. Arrays are used to deal with multiple products and resources. The model generator in the decision support system

1. A sequence of simulation modeling blocks is generated for each ob- ject of the CustomerZone type. This sequence represents generation and queuing of demand orders and receiving.

2. The ARENA resource table is populated by generating an entry for each object of the Resource type.

3. An ARENA entity is generated for each object of the Product type.

4. An ARENA submodel is generated for each SupplyChainUnit object.

It consists of four sets of simulation modeling blocks corresponding to objects that compose the SupplyChainUnit object. The local con- trol set is generated from the appropriate object of the LocalControl type (the object is named lc_Unit1 in Fig. 9.4). Other sets of blocks are generated in a similar manner.

Global control, and some other local control mechanisms not shown in Fig. 9.4, are included in the supply chain modeling template or manually developed. They are implemented using VB code.

The modeling method specific data model is also generated and populated during the model generation process. The data model organizes data in a manner suitable for execution of the simulation model. This ensures quick access of necessary data items. The data model consists of multiple spreadsheets containing information about structure and operational characteristics of the system. At the beginning of simulation, modeling data from the data model are loaded in the simulation model. Before loading, intermediate data have been created by converting the data model tables from the Excel format into the text format because ARENA reads text files much faster than Microsoft Excel files. Some of the data tables are loaded into ARENA arrays for access by ARENA objects, while some others are loaded in VB arrays for access by control functions.

A more detailed description of functions performed by individual blocks of the simulation model, and structuring of the modeling technique specific data model for a specific industrial case study, can be found in Chandra and Grabis (2003) and in Chapter 13 of this book.

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International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75 The generated simulation model is subsequently used to evaluate the given supply chain configuration. The automated generation enables rapid development of simulation models representing various alternative supply chain configurations.

This paper investigates the flow of manufacturing in an industry and by using the model analysis the main aim is to reduce the total time of manufacturing in the industry and increase the total productivity A. Present Model Of OMAX Autos Limited

VI. CASE STUDY: OMAX AUTOS LIMITED

Fig.1 Present Model of OMAX Autos Limited

Now from fig as found that there is problem of supply from raw material to the finished 1) VARIOUS PROBLEM AREAS OF THE INDUSTRY In this industry the setup of the machines are well and good but there are some things can be improved so that easy supply can be occur.

Figure 17 Root Blockages Due To Raw Material

Figure 16 Improper Arrangement Of Raw Material After First Cutting

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International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75

figure 18 improper place for quality inspection

improper working on press machine

figure 21 material handeling problems Figure 19 safety problems

B. Proposed Model Since OMAX AUTOS LIMITED have number of products for TATA motors and we have studied the different operations in existing layout and finally we came to the point that the layout of machines are optimal hence our suggestions are not to

shift to any machine to anywhere which may create lot of time in machining for other products but some important changes can occur so many conclusions is to reduce the existing machining time by modifying the material handling system so it is better to modify the handling equipment’s

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International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75

Fig .7 proposed model C. Improvement Methods In OMAX AUTOS LIMITED various improvements of the existing models are: 1) Changes in existing model: There are several changes in existing model we made which is useful for improving the productivity. In these changes we made some departments such as Industrial Engineering Department: In this department when volume of the products and types of the products increases. Many problems created such as:  Machine problems  Tool problems  Workers problems  Material handling problems To reduce these types of problems we add this department a) Objectives Of This Department:  To fulfil all the machine requirements.  To make strategic planning.  To arrange the machinery in proper order.  To reduce the time by using work study and method study.  To make the good relation between workers and management.  To select the proper machines by proper machine selection by using existing technology. b) Temporary Inventory Of Raw Material: Documentation of the raw material should necessary for any industry in this model we add a department for the proper documentation. For this we use CIM for the proper documents.

c) Advantages Of This Department  In case you have a new job order design you can just search the previous samples and modify them to make a new one.  Reduce design procedure.  Can save time.  We can directly connect it to design department to make designs and procedures. d) Modification Of Handling Equipment  For Large Size Goods For large size goods the supply is to be done by forklift for the smooth supply of large goods we can make arrangement such as supply can be easily done by pre determine allowance limits of forklift machine.

FORKLIFT FOR LARG GOODS For such type of forklift truck we make some predetermined allowance limit and also made some stands so that easy flow can occur.

2. FOR MEDIUM SIZE GOODS

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International Journal of Technical Research and Applications e-ISSN: 2320-8163, www.ijtra.com Volume 1, Issue 3 (july-August 2013), PP. 68-75 We can supply the finished goods by small trollies so that easily supply of raw material can be done. These trollies should have small wheels so that can be movement can be done easily.

REFERENCES

TROLLY FOR MEDIOM SIZE GOODS 3. FOR SMALL SIZE GOODS We can supply the small goods by using trays such that easily handling can be done it may be noted that handle on the tray should be ergonomically correct due to avoid any muscular pain of worker

MODIFIED EQUIPMENT’S FOR SMALL GOODS VII. CONCLUSION The investigation can be concluded in following words  If we use proper layout of the industry than approximately 30-40 % problems of supply can be reduced.  Material handling can also be the problem so if we use proper material handling we can reduce time.  We can use ergonomics to eliminate the industrial stress also.

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Proceedings of Eleventh Annual Industrial Engineering Research Conference, May 19-22, 2002, Orlando, US [6] Chandra C, Grabis J (2003) A data driven approach to automated simulation model building. Proceedings of the 18th European Simulation Symposium, ESS2003, October 26-29, Delft, The Netherlands, pp. 372-380 [7] Chatfield DC, Harrison TP, Hayya JC (2006) SISCO: An objectoriented supply chain simulation system. Decision Support Systems 42:422-434 [8] Dong M, Chen FF (2001) Process modeling and analysis of manufacturing supply chain networks using object-oriented Petri nets. Robotics and Computer- Integrated Manufacturing 17:121129 [9] Ganeshan R, Boone T, Stenger AJ (2001) The impact of inventory and flow plan- ning parameters on supply chain performance: An exploratory study. Interna- tional Journal of Production Economics 71:111-118 [10] Hibino H, Fukuda Y, Yura Y, Mitsuyuki K, Kaneda K (2002) Manufacturing adapter of distributed simulation systems using HLA. Winter Simulation Con- ference Proceedings. pp. 10991107 [11] Hung WY, Samsatli NJ, Shah N (2006) Object-oriented dynamic supply-chain [12] modelling incorporated with production scheduling. European Journal of Op- erational Research 169:1064-1076 [13] Ingalls RG, Kasales C (1999) CSCAT: The Compaq supply chain analysis tool. P. Farrington, H. B. Nembhard, D. T. Sturrock and G. W. Evans (eds) Pro- ceedings of the 1999 Winter Simulation Conference. Phoenix, pp. 1201-1206 [14] Jain S, Workman RW, Collins LM, Ervin EC (2001) Development of a high-levelsupply chain simulation model. In: B. A. Peters, J. S. Smith, D. J. Medeiros and M. W. Rohrer (eds) Proceeding of the 2001 Winter Simulation Confer- ence, pp.

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