Unleashing Creativity: Exploring the Relationship Between LMS Usage and Teacher-Student Innovation
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. Unleashing Creativity: Exploring the Relationship Between LMS Usage and Teacher-Student Innovation
Does using a LMS limit the creativity of teachers and students?
Post your initial response by the due date. Read and reply to at least two student’s posts by the due date.
Original Posts should include at least one properly APA formatted citation showing your answer was researched.
Supply Chain Management: Strategy, Planning, and Operation
Seventh Edition
Chapter 7
Demand Forecasting in a Supply Chain
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
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1
Learning Objectives
7.1 Understand the role of forecasting for both an enterprise and a supply chain.
7.2 Identify the components of a demand forecast and some basic approaches to forecasting.
7.3 Forecast demand using time-series methodologies given historical demand data in a supply chain.
7.4 Analyze demand forecasts to estimate forecast error.
7.5 Use Excel to build time-series forecasting models.
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Role of Forecasting in a Supply Chain
The basis for all planning decisions in a supply chain
Used for both push and pull processes
Production scheduling, inventory, aggregate planning
Sales force allocation, promotions, new production introduction
Plant/equipment investment, budgetary planning
Workforce planning, hiring, layoffs
All of these decisions are interrelated
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Characteristics of Forecasts
Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error
Long-term forecasts are usually less accurate than short-term forecasts
Aggregate forecasts are usually more accurate than disaggregate forecasts
In general, the farther up the supply chain a company is, the greater is the distortion of information it receives
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Summary of Learning Objective 1 (1 of 2)
Forecasting is a key input for virtually every design and planning decision made in a supply chain. It is important to recognize that all forecasts are likely to be wrong. Thus, an estimation of forecast error is essential to effectively use the forecast. Reducing the forecast horizon (by reducing the lead time of the associated decision) and aggregation are two effective approaches to decrease forecast error.
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Summary of Learning Objective 1 (2 of 2)
A relatively recent phenomenon, however, is to create collaborative forecasts for an entire supply chain and use these as the basis for decisions. Collaborative forecasting greatly increases the accuracy of forecasts and allows the supply chain to maximize its performance. Without collaboration, supply chain stages farther from demand will likely have poor forecasts that will lead to supply chain inefficiencies and a lack of responsiveness.
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Components and Methods (1 of 2)
Companies must identify the factors that influence future demand and then ascertain the relationship between these factors and future demand
Past demand
Lead time of product replenishment
Planned advertising or marketing efforts
Planned price discounts
State of the economy
Actions that competitors have taken
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Notes:
Components and Methods (2 of 2)
Qualitative
Primarily subjective
Rely on judgment
Time Series
Use historical demand only
Best with stable demand
Causal
Relationship between demand and some other factor
Simulation
Imitate consumer choices that give rise to demand
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Notes:
Components of An Observation
Observed demand (O) = systematic component (S)
+ random component (R)
Systematic component – expected value of demand
Level (current deseasonalized demand)
Trend (growth or decline in demand)
Seasonality (predictable seasonal fluctuation)
Random component – part of forecast that deviates from systematic part
Forecast error – difference between forecast and actual demand
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Five Important Points in the Forecasting Process
Understand the objective of forecasting.
Integrate demand planning and forecasting throughout the supply chain.
Identify the major factors that influence the demand forecast.
Forecast at the appropriate level of aggregation.
Establish performance and error measures for the forecast.
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Notes:
Summary of Learning Objective 2
Demand consists of a systematic and a random component. The systematic component measures the expected value of demand. The random component measures fluctuations in demand from the expected value. The systematic component consists of level, trend, and seasonality. Level measures the current de-seasonalized demand. Trend measures the current rate of growth or decline in demand. Seasonality indicates predictable seasonal fluctuations in demand. The goal of forecasting is to estimate the systematic component and the size (not direction) of the random component (in the form of a forecast error). Good forecasting requires a clear understanding of the objective of the forecast and should be integrated across the supply chain.
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Time-Series Forecasting Methods
Three ways to calculate the systematic component
Multiplicative
S = level × trend × seasonal factor
Additive
S = level + trend + seasonal factor
Mixed
S = (level + trend) × seasonal factor
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Static Methods
Systematic component = (level+trend)×seasonal factor
Where
L = estimate of level at t = 0
T = estimate of trend
St = estimate of seasonal factor for Period t
Dt = actual demand observed in Period t
Ft = forecast of demand for Period t
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Tahoe Salt (1 of 5)
Table 7-1 Quarterly Demand for Tahoe Salt
Year | Quarter | Period, t | Demand, Dt |
1 | 2 | 1 | 8,000 |
1 | 3 | 2 | 13,000 |
1 | 4 | 3 | 23,000 |
2 | 1 | 4 | 34,000 |
2 | 2 | 5 | 10,000 |
2 | 3 | 6 | 18,000 |
2 | 4 | 7 | 23,000 |
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Tahoe Salt (2 of 5)
Table 7-1 [continued]
Year | Quarter | Period, t | Demand, Dt |
3 | 1 | 8 | 38,000 |
3 | 2 | 9 | 12,000 |
3 | 3 | 10 | 13,000 |
3 | 4 | 11 | 32,000 |
4 | 1 | 12 | 41,000 |
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Tahoe Salt (3 of 5)
Figure 7-1 Quarterly Demand at Tahoe Salt
Deseasonalize demand and run linear regression to estimate level and trend.
Estimate seasonal factors.
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Estimate Level and Trend (1 of 2)
Periodicity p = 4, t = 3
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Notes:
Estimate Level and Trend (2 of 2)
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Notes:
Tahoe Salt (4 of 5)
Figure 7-2 Excel Workbook with Deseasonalized Demand for Tahoe Salt
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Tahoe Salt (5 of 5)
Figure 7-3 Deseasonalized Demand for Tahoe Salt
A linear relationship exists between the deseasonalized demand and time based on the change in demand over time
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Estimating Seasonal Factors (1 of 3)
Figure 7-4 Deseasonalized Demand and Seasonal Factors for Tahoe Salt
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Estimating Seasonal Factors (2 of 3)
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Estimating Seasonal Factors (3 of 3)
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Adaptive Forecasting (1 of 2)
The estimates of level, trend, and seasonality are updated after each demand observation
Estimates incorporate all new data that are observed
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Adaptive Forecasting (2 of 2)
Where
Lt = estimate of level at the end of Period t
Tt = estimate of trend at the end of Period t
St = estimate of seasonal factor for Period t
Ft = forecast of demand for Period t (made Period t – 1 or earlier)
Dt = actual demand observed in Period t
Et = Ft – Dt = forecast error in Period t
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Steps in Adaptive Forecasting
Initialize
Compute initial estimates of level (L0), trend (T0), and seasonal factors (S1,…,Sp)
Forecast
Forecast demand for period t + 1
Estimate error
Compute error Et+1 = Ft+1 – Dt+1
Modify estimates
Modify the estimates of level (Lt+1), trend (Tt+1), and seasonal factor (St+p+1), given the error Et+1
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Moving Average
Used when demand has no observable trend or seasonality
Systematic component of demand = level
The level in period t is the average demand over the last N periods
After observing the demand for period t + 1, revise the estimates
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Moving Average Example (1 of 2)
A supermarket has experienced weekly demand of milk of D1 = 120, D2 = 127, D3 = 114, and D4 = 122 gallons over the past four weeks
Forecast demand for Period 5 using a four-period moving average
What is the forecast error if demand in Period 5 turns out to be 125 gallons?
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Moving Average Example (2 of 2)
Forecast demand for Period 5
F5 = L4 = 120.75 gallons
Error if demand in Period 5 = 125 gallons
E5 = F5 – D5 = 120.75 – 125 = – 4.25
Revised demand
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Simple Exponential Smoothing (1 of 3)
Used when demand has no observable trend or seasonality
Systematic component of demand = level
Initial estimate of level, L0, assumed to be the average of all historical data
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Simple Exponential Smoothing (2 of 3)
Given data for Periods 1 to n
Current forecast
Revised forecast using smoothing constant (0 < α < 1)
Thus
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Simple Exponential Smoothing (3 of 3)
Supermarket data
F1 = L0 = 120.75
E1 = F1−D1 = 120.75−120 = 0.75
L1 = αD1+(1−α)L0
= 0.1×120+0.9 ×120.75=120.68
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Trend-Corrected Exponential Smoothing (Holt’s Model) (1 of 4)
Appropriate when the demand is assumed to have a level and trend in the systematic component of demand but no seasonality
Systematic component of demand = level + trend
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Trend-Corrected Exponential Smoothing (Holt’s Model) (2 of 4)
Obtain initial estimate of level and trend by running a linear regression
Dt = at + b
T0 = a, L0 = b
In Period t, the forecast for future periods is
Ft+1 = Lt + Tt and Ft+n = Lt + nTt
Revised estimates for Period t
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Trend-Corrected Exponential Smoothing (Holt’s Model) (3 of 4)
Smartphone player demand
D1 = 8,415, D2 = 8,732, D3 = 9,014, D4 = 9,808,D5 = 10,413, D6 = 11,961, α = 0.1, β = 0.2
Using regression analysis
L0 = 7,367 and T0 = 673
Forecast for Period 1
F1 = L0 + T0 = 7,367 + 673 = 8,040
Period 1 error
E1 = F1 – D1 = 8,040 – 8,415 = –375
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Trend-Corrected Exponential Smoothing (Holt’s Model) (4 of 4)
Revised estimate
With new L1
F2 = L1 + T1 = 8,078 + 681 = 8,759
Continuing
F7 = L6 + T6 = 11,399 + 673 = 12,072
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Trend- and Seasonality-Corrected Exponential Smoothing (1 of 2)
Appropriate when the systematic component of demand has a level, trend, and seasonal factor
Systematic component = (level + trend) × seasonal factor
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Trend- and Seasonality-Corrected Exponential Smoothing (2 of 2)
After observing demand for period t + 1, revise estimates for level, trend, and seasonal factors
α = smoothing constant for level
β = smoothing constant for trend
γ = smoothing constant for seasonal factor
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Winter’s Model (1 of 3)
L0 = 18,439 T0 = 524
S1= 0.47, S2 = 0.68, S3 = 1.17, S4 = 1.67
F1 = (L0 + T0)S1 = (18,439 + 524)(0.47) = 8,913
The observed demand for Period 1 = D1 = 8,000
Forecast error for Period 1
= E1 = F1 – D1
= 8,913 – 8,000 = 913
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Winter’s Model (2 of 3)
Assume α = 0.1, β = 0.2, γ = 0.1; revise estimates for level and trend for period 1 and for seasonal factor for Period 5
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Winter’s Model (3 of 3)
Forecast demand for Period 2
F2 = (L1 + T1)S2 = (18,769 + 485)(0.68) = 13,093
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Time Series Models
Forecasting Method | Applicability |
Moving average | No trend or seasonality |
Simple exponential smoothing | No trend or seasonality |
Holt’s model | Trend but no seasonality |
Winter’s model | Trend and seasonality |
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Summary of Learning Objective 3
Time-series methods for forecasting are categorized as static or adaptive. In static methods, the estimates of parameters are not updated as new demand is observed. Static methods include regression. In adaptive methods, the estimates are updated each time a new demand is observed. Adaptive methods include moving averages, simple exponential smoothing, Holt’s model, and Winter’s model. Moving averages and simple exponential smoothing are best used when demand displays neither trend nor seasonality. Holt’s model is best when demand displays a trend but no seasonality. Winter’s model is appropriate when demand displays both trend and seasonality.
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Measures of Forecast Error (1 of 2)
Forecast errors contain valuable information and must be analyzed for two reasons:
Managers use error analysis to determine whether the current forecasting method is predicting the systematic component of demand accurately
All contingency plans must account for forecast error
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Measures of Forecast Error (2 of 2)
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Summary of Learning Objective 4
Forecast error measures the random component of demand. This measure is important because it reveals how inaccurate a forecast is likely to be and what contingencies a firm may have to plan for. The M S E, M A D, and M A P E are used to estimate the size of the fore- cast error. The bias and T S are used to estimate if the forecast consistently over- or under- forecasts or if demand has deviated significantly from historical norms.
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Selecting the Best Smoothing Constant (1 of 2)
Figure 7-5 Selecting Smoothing Constant by Minimizing M S E
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Selecting the Best Smoothing Constant (2 of 2)
Figure 7-6 Selecting Smoothing Constant by Minimizing M A D
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Forecasting Demand at Tahoe Salt (1 of 10)
Moving average
Simple exponential smoothing
Trend-corrected exponential smoothing
Trend- and seasonality-corrected exponential smoothing
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Forecasting Demand at Tahoe Salt (2 of 10)
Figure 7-7 Tahoe Salt Forecasts Using Four-Period Moving Average
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Forecasting Demand at Tahoe Salt (3 of 10)
Moving average
L12 = 24,500
F13 = F14 = F15 = F16 = L12 = 24,500
σ = 1.25 × 9,719 = 12,148
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Forecasting Demand at Tahoe Salt (4 of 10)
Figure 7-8 Tahoe Salt Forecasts Using Simple Exponential Smoothing
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Forecasting Demand at Tahoe Salt (5 of 10)
Simple exponential smoothing
α = 0.1
L0 = 22,083
L12 = 23,490
F13 = F14 = F15 = F16 = L12 = 23,490
σ = 1.25 × 10,208 = 12,761
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Forecasting Demand at Tahoe Salt (6 of 10)
Figure 7-9 Trend-Corrected Exponential Smoothing
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Forecasting Demand at Tahoe Salt (7 of 10)
Trend-Corrected Exponential Smoothing
L0 = 12,015 and T0 = 1,549
L12 = 30,443 and T12 = 1,541
F13 = L12 + T12 = 30,443 + 1,541 = 31,984
F14 = L12 + 2T12 = 30,443 + 2 × 1,541 = 33,525
F15 = L12 + 3T12 = 30,443 + 3 × 1,541 = 35,066
F16 = L12 + 4T12 = 30,443 + 4 × 1,541 = 36,607
σ = 1.25 × 8,836 = 11,045
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Forecasting Demand at Tahoe Salt (8 of 10)
Figure 7-10 Trend- and Seasonality-Corrected Exponential Smoothing
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Forecasting Demand at Tahoe Salt (9 of 10)
Trend- and Seasonality-Corrected
L0 = 18,439 T0 =524
L12 = 24,791 T12 = 532
S1 = 0.47 S2 = 0.68 S3 = 1.17 S4 = 1.67
F13 = (L12 + T12)S13 = (24,791 + 532)0.47 = 11,902
F14 = (L12 + 2T12)S13 = (24,791 + 2 × 532)0.68 = 17,581
F15 = (L12 + 3T12)S13 = (24,791 + 3 × 532)1.17 = 30,873
F16 = (L12 + 4T12)S13 = (24,791 + 4 × 532)1.67 = 44,955
σ = 1.25 × 1,469 = 1,836
Copyright © 2019, 2016, 2013 Pearson Education, Inc. All Rights Reserved
Forecasting Demand at Tahoe Salt (10 of 10)
Table 7-2 Error Estimates for Tahoe Salt Forecasting
Forecasting Method | M A D | M A P E (%) | T S Range |
Four-period moving average | 9,719 | 49 | –1.52 to 2.21 |
Simple exponential smoothing | 10,208 | 59 | –1.38 to 2.15 |
Holt’s model | 8,836 | 52 | –2.15 to 2.00 |
Unleashing Creativity: Exploring the Relationship Between LMS Usage and Teacher-Student Innovation
RUBRIC |
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Excellent Quality 95-100%
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Introduction
45-41 points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned. |
Literature Support 91-84 points The background and significance of the problem and a clear statement of the research purpose is provided. The search history is mentioned. |
Methodology 58-53 points Content is well-organized with headings for each slide and bulleted lists to group related material as needed. Use of font, color, graphics, effects, etc. to enhance readability and presentation content is excellent. Length requirements of 10 slides/pages or less is met. |
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Average Score 50-85% |
40-38 points More depth/detail for the background and significance is needed, or the research detail is not clear. No search history information is provided. |
83-76 points Review of relevant theoretical literature is evident, but there is little integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are included. Summary of information presented is included. Conclusion may not contain a biblical integration. |
52-49 points Content is somewhat organized, but no structure is apparent. The use of font, color, graphics, effects, etc. is occasionally detracting to the presentation content. Length requirements may not be met. |
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Poor Quality 0-45% |
37-1 points The background and/or significance are missing. No search history information is provided. |
75-1 points Review of relevant theoretical literature is evident, but there is no integration of studies into concepts related to problem. Review is partially focused and organized. Supporting and opposing research are not included in the summary of information presented. Conclusion does not contain a biblical integration. |
48-1 points There is no clear or logical organizational structure. No logical sequence is apparent. The use of font, color, graphics, effects etc. is often detracting to the presentation content. Length requirements may not be met |
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