Essays on Quantitative Concepts and Methods in Supply Chain Coursework

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The paper "Quantitative Concepts and Methods in Supply Chain" is a great example of business coursework. The supply chain relies on accurate forecasts to ensure constant supply and satisfaction to the consumer market of its products or services. When forecasts become inaccurate it can lead to excesses and shortages across the supply chain. Chances of poor customer service, work disruption, and missed deliveries arise due to shortages of services, parts, and materials. On the other hand, overly optimistic forecasts which increase costs arise from excesses of capacity or materials.

In the supply chain, both excesses and shortages have a negative impact not only on profits but also on customer service. According to Wilson, Holton, and Barry (2004: 44), organizations lower the chance of occurrences in various ways as they strive to build up the best feasible forecasts. Forecasting, information sharing, and collaborative planning increase supply chain visibility when they engage in major supply chain partners. These supply chain partners to have access to inventory and sales information in real-time (Chandra & Grabis, 2005: 343). Owing to rapid communication about unplanned events and poor forecasts such as work stoppages causes changes in plans.

When choosing among different techniques, decision-makers will want to include accuracy along with cost as a factor. Every business organization for the success of its daily activities requires accurate forecasts which are the basis for schedules in the organization (Zhang, 2004:22). Schedules will be generated only if the forecasts are accurate using optimal resources, output, and correct timing of output. These lead to headaches for managers, dissatisfied customers, and additional costs if they remain unfulfilled. The use of demand forecasting applications uses time series techniques involving parameters such as weekly revenues while associative techniques for a one-time decision involve a single forecast such as the size of a production plant (Delurgio, 2008:42). Quantitative Concepts and Methods Quantitative methods involve the development of associative models or projection of historical data that attempts to use explanatory (causal) variables to develop a forecast.

For example, the time series consists of observations in a time-ordered sequence that are taken at regular intervals such as annually, quarterly, monthly, weekly, daily, and hourly. The data, on the other hand, assume measurements of productivity, precipitation, shipments, consumer price index, demand, accidents, earnings, output, and profits (Delurgio, 2008:43).

Such forecasting techniques take the assumption that past values of the series can help generate future values. Despite the methods being used widely, there has been a failed attempt to identify variables influencing the series that sometimes produce satisfactory results. Any underlying behavior of the series is brought about by analyzing time-series data by visually examining the plot and merely plotting the data. Some parameters that are observable in this circumstance are variations around average, cycles, seasonal variations, and trends which provide irregular or random variations (Dejonckheere et al.

2003: 575). The trend is the long-term downward or upward movement in the data such as cultural changes, changing incomes, and population shifts. Seasonality is fairly regular and short-term variations associated with factors like time of day or the calendar experienced by theatres, supermarkets, and restaurants depicting daily ‘ seasonal’ and weekly variations. Zhao and Leung (2002:328) note that cycles are essentially wavelike variations spanning more than one year and include agricultural, political, and economic conditions.

Irregular variations arise from unique situations like a major product or service change, strikes, and severe weather conditions though they do not reflect typical behavior since when they distort the overall picture when included in the series. These should be removed from the data when identified whenever possible. After all other behaviors have been factored, random or residual variations always remain (Sanders & Graman, 2009:121).

References

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Chandra C & Grabis J 2005, Application of multiple-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand, European Journal of Operational Research, 166, 337-350.

Dejonckheere J Disney S M Lambrecht M R & Towill D R 2003, Measuring and avoiding the bullwhip effect: A control theoretic approach, European Journal of Operational Research, 147, 567-590.

Dejonckheere J Disney S M Lambrecht M R & Towill D R 2004, The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective, European Journal of Operational Research, 153, 727-750.

Delurgio S 2008, Forecasting Principles and Applications. New York: Irwin/ McGraw-Hill.

Gardner E S 2000, Evaluating forecast performance in an inventory control system, Management Science, 36, 490-499.

Gardner E S 2006, Exponential smoothing: The state of the art-Part II, International Journal of Forecasting, 22, 637-666

Gardner ES & McKenzie E 2005, Forecasting trends in time series, Management Science, 31, 1237-1246.

Holt CC 2004, Forecasting seasonals and trends by exponentially weighted moving averages, International Journal of Forecasting, 20, 5-10.

Hyndman RJ & Koehler AB 2006, Another look at measures of forecast accuracy, International Journal of Forecasting, 22, 679-688.

Sanders NR & Graman GA 2009, Quantifying costs of forecast errors: A case study of the warehouse environment, Omega: The International Journal of Management Science, 37, 116-125.

Snyder RD Koehler AB & Ord JK 2002, Forecasting for inventory control with exponential smoothing, International Journal of Forecasting,18, 5-18.

Tiacci L & Saetta S 2009, An approach to evaluate the impact of interaction between demand forecasting method and stock control policy on the inventory system performances, International Journal of Production Economics, 118, 63-71.

Wilson J Holton R & Barry K 2004, Business Forecasting. New York: McGraw-Hill.

Zhao X Xie J & Leung J 2002, The impact of forecasting model selection on the value of information sharing in a supply chain, European Journal of Operational Research, 142, 321-344.

Zhang X 2004, The impact of forecasting methods on the bullwhip effect, International Journal of Production Economics, 88, 15-27.

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