Operations and Supply Chain Management 9th edition
Operations and Supply Chain Management 9th edition
9th Edition
ISBN: 9781119320975
Author: Roberta S. Russell, Bernard W. Taylor III
Publisher: WILEY
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Chapter 15, Problem 1.4CP

Just ERP

Just Sofas (JS) had begun the year full of promise with a new facility, restructured manufacturing process, and high hopes for its new ERP system. Most of the domestic furniture manufacturers had long since gone overseas, but JS believed being close to the customer gave it a competitive edge. The workers had rallied behind President Ruffner’s idea of guaranteed four-day deliveries on customer orders. And for a while, the promo had worked. Then orders began pouring in and the scheduling system imploded.

It was for just such a case that Ruffner had sought out an ERP system—to automatically handle customer orders, factory schedules, and supply chain coordination as demand varied. Ruffner had carefully chosen the ERP software package used by all the large corporations he knew of, reasoning that if successful companies had chosen this vendor, who was he to choose otherwise? Implementation had proceeded carefully as well, one might say painstakingly slow, as the IT staff started with the finance module and worked down through sales and marketing, order fulfillment, production planning, MRP, capacity planning, and finally scheduling. Actually, the scheduling module was still having the kinks worked out and the bill of materials tile had not been updated to the current catalog offerings, but everything else seemed to be working tine.

Ruffner had included statements about the ERP system in his earnings reports for the past three quarters, noting that “productivity wanes as new IT system is being implemented,” “earnings down as company adjusts to new ordering system.” and “scheduling glitch causes backlog of customer orders.” For this quarter, he was trying to put a more positive spin on “only 5% of orders shipped on time due to incomplete jobs waiting for materials that were not ordered as they should have been.” He supposed a more innocuous “new scheduling system still not up to speed” would suffice. What Ruffner really wondered was if the company could survive another year like this one.

What problems contributed to the disappointing results of the ERP system? How would you suggest that Just Sofa proceed next year?

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Scenario You have been given a task to create a demand forecast for the second year of sales of a premium outdoor grill. Accurate forecasts are important for many reasons, including for the company to ensure they have the materials they need to create the products required in a certain period of time. Your objective is to minimize the forecast error, which will be measured using the Mean Absolute Percentage Error (MAPE) with a goal of being below 25%. You have historical monthly sales data for the past year and access to software that provides forecasts based on five different forecasting techniques (Naïve, 3-Month Moving Average, Exponential Smoothing for .2, Exponential Smooth for .5, and Seasonal) to help determine the best forecast for that particular month. Based on the given data, you will identify trends and patterns to create a more accurate forecast. Approach Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next…
Approach Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next month. Remember to select the forecasting technique that produces the forecast error nearest to zero. For example: a. Naïve Forecast is 230 and the Forecast Error is -15. b. 3-Month Moving Forecast is 290 and the Forecast Error is -75. c. Exponential Smoothing Forecast for .2 is 308 and the Forecast Error is -93. d. Exponential Smoothing Forecast for .5 is 279 and the Forecast Error is -64. e. Seasonal Forecast is 297 and the Forecast Error is -82. The forecast for the next month would be 230 as the Naïve Forecast had the Forecast Error closest to zero with a -15. This forecasting technique was the best performing technique for that month. You do not need to do any external analysis-the forecast error for each strategy is already calculated for you in the tables below. Naïve Month Period Actual Demand Naïve Forecast Error 3- Month Moving Forecast 3- Month Moving…
Scenario You have been given a task to create a demand forecast for the second year of sales of a premium outdoor grill. Accurate forecasts are important for many reasons, including for the company to ensure they have the materials they need to create the products required in a certain period of time. Your objective is to minimize the forecast error, which will be measured using the Mean Absolute Percentage Error (MAPE) with a goal of being below 25%. You have historical monthly sales data for the past year and access to software that provides forecasts based on five different forecasting techniques (Naïve, 3-Month Moving Average, Exponential Smoothing for .2, Exponential Smooth for .5, and Seasonal) to help determine the best forecast for that particular month. Based on the given data, you will identify trends and patterns to create a more accurate forecast. Approach Consider the previous month's forecast to identify which technique is most effective. Use that to forecast the next…

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