Analysis of Forecasting on Supply Chain
A supply chain is a network that performs functions from supplier’s supplier to customer’s customer. It encompasses all the process involved in delivering the final product to the final consumer. Supply chain is filled with various uncertainties such as demand, process, and supply. Inventories are often used to protect the chain from these uncertainties. The higher the variations the more the losses and every company needs to minimize the variations and uncertainties in its supply chain. There are various causes of uncertainties. Among them few that can be listed are demand variations based on the type of product, the suppliers’ receipt variations which depend on the supplier’s ability to provide the raw material. One of the important variations that I have come across during my work experience is the forecasting variation. A small change in forecasting of demand changes the planning stages of the product and subsequent orders across the supply chain. We already know about the supply chain uncertainties. Forecasting is an area which plays major role in deciding if the company has met the targets or is losing customer or incurring cost. There have been research in the area of bullwhip effect and demand uncertainties but I would like concentrate on the forecasting effect on the supply chain. How the variations in forecasting play along the supply chain, the effects on a company, its planning process and its own forecasting and demand variations. Forecasting is a major point which decides companies’ abilities to avoid: 1) Unsatisfied customers
2) Lost business
3) Increased cost and lower benefits
4) Inefficient utilization of company resources.
Hence is it important to know the effect the wrong forecasting has on the supply chain. This has encouraged my research on this topic.
The research project will find out the forecasting effects on a supply chain. The scope will be limited to a 3 tier supply chain model which will include a customer, manufacturing plant and its supplier. The question will try to find out in detail the effects of the forecasting changes on the planning of the manufacturing plant and its subsequent effect. The question will also try to answer quantitative as well as qualitative changes that are caused due to forecasting changes.
There has been research studying the role of forecasting in relation to bullwhip effect. The research has basically assumed different forecasting methods and its effect on the demand process. The two forecasting methods used are moving average and Exponential smoothing and how variations arising from these models effect the lead time. The findings suggest that different forecasting methods lead to bullwhip effect measures with fundamentally different properties in relation to lead-time and demand autocorrelation. The paper shows that these forecasting methods affect the average inventory cost in a straight forward manner. It has concluded that in general increase in lead time enhances lead time regardless of the forecasting method used. However, the size of the impact does depend on the forecasting methods. As can be seen the area involving changes in forecast has not yet been explored much. This has motivated me to go for this topic and find out the result in both quantitative and qualitative terms.
The project model involves a fixed forecasting method but the only change will be with respect to demand from the customer. It is assumed the customer’s demand will be varying on monthly basis. The model assumes that customer sends 3 months forecast to the manufacturer with first month’s forecast being firm and next two months can be altered with a variation of up to 50%.
Creating a model considering the forecasted demand, input demand and the actual quantity delivered to the customer.
The data analysed is from January 2009 to...
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