7. Limitations and scope for future work
In the present work only fifteen variables are identified for modeling the agility of a supply chain. More number of variables affecting supply chain agility can be identified to develop ISM. The experts' help have been sought to analyze driving and dependence power of the variables of supply chains agility. Here the framework developed depends upon the opinion of few supply chain experts and has some element of bias. Through ISM, a relationship model among supply chain agility variables has been developed. This model has not been statistically validated. LISREL software can be used to examine the relationships derived from this model. Structural equation modeling (SEM), also commonly known as linear structural relationship approach has the capability of testing the validity of such hypothetical model. Therefore, it may be applied in the future research to test the validity of this model.
8. Conclusion
To formulate strategies for building agility in supply chain, it is important for the management of a supply chain to understand characteristics and interrelationship of variables. In the proposed model, data accuracy is affected by other variables such as use of IT tools, centralized and collaborative planning, process integration, minimizing uncertainty, reducing resistance to change, and development of trust. However, it acts as a driver for market sensitive supply chain and thus helps to quickly respond to the market demand. Strategy to cope up with resistance to change among trading partners helps to improve market sensitiveness, which supports supply chain agility. Ability to introduce new product in the market is governed by capability to visualize and manage the uncertainties. Uncertainty could be better managed if supply chain has centralized and collaborative planning which need to be supported by the effective use of IT tools. Trust among trading partners could be developed by process integration and centralized and collaborative planning. Trust development among trading partners helps to generate reliable data at each stage of the supply chain. All three variables namely, use of IT tools, centralized and collaborative planning, and process integration are significant drivers hence they must be at the top priority for an agile supply chain.Lead-time reduction is another important variables for agility of a supply chain. It is driven by the market sensitiveness of the supply chain. Lead-time reduction helps to make delivery fast, which further improves service level. New product introduction and service level improvement enhance the customer satisfaction level. Increase customer satisfaction level would help to improve the market share of supply chain business. Lead-time reduction would also help to improve quality level by reducing different types of waste. Customer satisfaction improves with better quality level. Quality improvement is captured in the present model by considering the customer satisfaction level, which is also necessary to gain market share. Variables like use of IT tools, centralized and collaborative planning and process integration have relatively low dependence and thus appears at bottom level of hierarchy in ISM. This implies that use of IT tools, centralized and collaborative planning, and process integration play significant role and work as the driver in effective supply chain integration. Integrated supply chain is capable of responding to customer demand in a volatile market.
ISM developed in this paper acts as a tool for top management to understand the variables of an agile supply chain. Though ISM is developed on the basis of perception of the experts of supply chains, the results are quite generic and helpful for the top management to drive the efforts towards the roots of the problem. ISM approach leads us to the variables where fruitful results in terms of market share improvement can be achieved. ISM developed in this paper is not specific to any sector and specific model for any other sector may differ slightly from the model developed in this paper.
7. Limitations and scope for future work
In the present work only fifteen variables are identified for modeling the agility of a supply chain. More number of variables affecting supply chain agility can be identified to develop ISM. The experts' help have been sought to analyze driving and dependence power of the variables of supply chains agility. Here the framework developed depends upon the opinion of few supply chain experts and has some element of bias. Through ISM, a relationship model among supply chain agility variables has been developed. This model has not been statistically validated. LISREL software can be used to examine the relationships derived from this model. Structural equation modeling (SEM), also commonly known as linear structural relationship approach has the capability of testing the validity of such hypothetical model. Therefore, it may be applied in the future research to test the validity of this model.
8. Conclusion
To formulate strategies for building agility in supply chain, it is important for the management of a supply chain to understand characteristics and interrelationship of variables. In the proposed model, data accuracy is affected by other variables such as use of IT tools, centralized and collaborative planning, process integration, minimizing uncertainty, reducing resistance to change, and development of trust. However, it acts as a driver for market sensitive supply chain and thus helps to quickly respond to the market demand. Strategy to cope up with resistance to change among trading partners helps to improve market sensitiveness, which supports supply chain agility. Ability to introduce new product in the market is governed by capability to visualize and manage the uncertainties. Uncertainty could be better managed if supply chain has centralized and collaborative planning which need to be supported by the effective use of IT tools. Trust among trading partners could be developed by process integration and centralized and collaborative planning. Trust development among trading partners helps to generate reliable data at each stage of the supply chain. All three variables namely, use of IT tools, centralized and collaborative planning, and process integration are significant drivers hence they must be at the top priority for an agile supply chain.Lead-time reduction is another important variables for agility of a supply chain. It is driven by the market sensitiveness of the supply chain. Lead-time reduction helps to make delivery fast, which further improves service level. New product introduction and service level improvement enhance the customer satisfaction level. Increase customer satisfaction level would help to improve the market share of supply chain business. Lead-time reduction would also help to improve quality level by reducing different types of waste. Customer satisfaction improves with better quality level. Quality improvement is captured in the present model by considering the customer satisfaction level, which is also necessary to gain market share. Variables like use of IT tools, centralized and collaborative planning and process integration have relatively low dependence and thus appears at bottom level of hierarchy in ISM. This implies that use of IT tools, centralized and collaborative planning, and process integration play significant role and work as the driver in effective supply chain integration. Integrated supply chain is capable of responding to customer demand in a volatile market.
ISM developed in this paper acts as a tool for top management to understand the variables of an agile supply chain. Though ISM is developed on the basis of perception of the experts of supply chains, the results are quite generic and helpful for the top management to drive the efforts towards the roots of the problem. ISM approach leads us to the variables where fruitful results in terms of market share improvement can be achieved. ISM developed in this paper is not specific to any sector and specific model for any other sector may differ slightly from the model developed in this paper.
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