To support your work, use your course and text readings and also use outside sources. As in all assignments, cite your sources in

Operations and Productivity

How do you de�ne OM?  One simple de�nition is taking a set of inputs and transforming these into an

output.  For example, you might have two slices of bread, peanut butter, and grape jelly (three inputs). 

From those inputs, you can assemble them (a process) into a peanut butter and jelly sandwich (an

output). 

What is considered an input? That depends on the situation. It could be raw materials, information, or

customers themselves. The output could be a product or a service. 

It is easy to understand a product; it's something you can feel and touch. However, if someone is a

consultant, it may not be clear what is being offered by the person. This is a key difference between

goods and services. A service is usually intangible, being produced and consumed simultaneously.

There are many key decisions that need to be made within an organization, and OM plays an important role.  It creates the goods or services that are sold by the organization.  However, it is also an expensive

activity, because most of the money within the organization is spent in OM. 

No matter what type of product is being produced, an organization is interested in utilizing its

resources well. Organizations must focus on making the best use of their resources (labor, equipment,

and activities).

To determine how well you are doing as an organization or a department, it’s necessary to measure

your performance or productivity.  This is calculated as the ratio of outputs to inputs. 

Productivity= Units Produced

= Outputs

Inputs used Inputs

For example, if it takes 100 hours of work to make 100 widgets, you have a productivity factor of 1.  If

you make an improvement to your process and now take only 50 hours to make 100 widgets, you’ve

doubled your productivity to a factor of 2.

The relationship between operations and productivity has many other �ne points that must be

considered.  Review the Supplemental Media entitled “Productivity In More Depth” to understand

some of these �ner points.

Additional Materials

Productivity In More Depth

(media/week1/SUO_MGT3059%20W1%20L1%20Productivity%20In%20More%20Depth.pdf?

_&d2lSessionVal=SWzqLkE3HvLXZkZ375Dqp03nU&ou=86458)

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Forecasting

Life is full of uncertainties. You don't know what the weather will be tomorrow or what your

organization's sales �gures will be. However, you can try to predict the future. You can use the weather

forecast to estimate the weather and historical sales data to predict future sales.

In a business environment, a forecast is often an estimate of future demand. A forecast can be quantitative or qualitative. In addition, it can be based on factors that are either internal or external to

the organization.

Forecasts help reduce uncertainty as well as anticipate and manage change. There are three types of

forecasts: economic, technological, and demand. In this course, you will focus on demand forecasts. An

operations manager uses forecasts to anticipate inventory and capacity demand, manage lead times,

estimate costs for budgeting, and improve productivity.

Let's start with qualitative forecasts. There are no numbers involved in generating a qualitative

analysis. There are multiple methods, such as expert opinions or a consensus, which an organization

can use to collect data without performing a numerical analysis. Focus groups and market research are

used to collect data on a new product in case historical data is not available.

Another way to forecast a new product is to conduct a historical analogy. This process works especially

well when the new product is similar to a previous product of the organization. The use of historical

analogy assumes that the results experienced in offering the new product will be similar to the results experienced when the previous product was launched. 

In some situations, a qualitative forecast is appropriate. However, in other cases, a qualitative forecast

can be developed in conjunction with a quantitative forecast.  Such quantitative forecasts will be

discussed next. 

SUO Discussion Rubric (80 Points) – Version 1.2 Course: MGT3059-Operations Management SU01

Response No Submission 0 points

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Minor to no errors

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Productivity In More Depth Getting all you can out of what you have In this course, you will focus on processes, usually including people (labor), capital equipment, and activities to create the good or the service. Throughout this course, you will consider the following questions:

• How do you change the inputs into outputs?

• What impact do these inputs have on an organization?

• How can you improve the performance of these processes?

As children, most people learn about the five Ws (who, what, where, when, and why) of writing a paper. The same basic questions are important in OM. As an operations manager, you need be aware of the following:

• Who will do the work (staffing decisions)?

• What is the product or the service?

• Where will the production happen (in what facility or on what production line)?

• When will the production happen (the planning of input and output)?

• Why is there a need for the product or the service (to meet customer demand)?

Due to the fact that the profit of many organizations (after all expenses have been taken into consideration) is often not much larger than 2%, the ability to save $2 in the operations of a company has the same net impact on the organization’s bottom line as does a sales increase of $100. Using this relationship, since it is often easier to save $200 than to increase sales by $10,000, it is vital to have an understanding of operations and how to effectively manage OM processes.

In the Operations and Productivity video, you were introduced to the calculation of productivity. That equation can be used for a single input resource to calculate what is considered a “partial” (or “single factor”) productivity measure. For example, if you used 25 hours of labor make 100 widgets, your partial productivity would be 4 (four widgets per hour of labor).

Besides labor hours, you would also use materials, capital, energy, and overhead when making the widgets. Including more of these variables in your productivity measure will give you a broader view of system productivity. This type of productivity measure is a multifactor productivity measure, in which you can use two or more inputs, typically represented in dollars. When all factors are included in such a multifactor measure, it is known as a total productivity measure.

2 Productivity In More Depth Getting all you can out of what you have

Generally, you need to be concerned about three key productivity variables: labor, capital, and management. A change in these variables will directly link to productivity, and the variables are often the focus of productivity improvement projects.

© 2017 South University

Page 2 of 2

Operations Management

©2017 South University

Forecasts and Errors Can We Predict The Future? Let’s use the following table to demonstrate how forecasts differ on the basis of the selected technique. Using each technique, you’ll estimate the demand for Week 10.

Week Demand

1 960

2 1,340

3 1,790

4 1,500

5 1,220

6 1,710

7 1,140

8 1,030

9 1,560

Naïve Approach: This is the easiest forecasting technique. The forecast for the demand in the next period is equal to the sales in the previous period. Therefore, the forecast for the demand in Week 10 is 1,560. Moving Averages: This technique is slightly more difficult. You need to use the historical demand over a specific number of periods in order to estimate the demand in the next period. If you’re using a three-week moving average, the forecast for the demand in Week 10 will be equal to the sum of the demand in the previous three weeks divided by 3. (1,140 + 1,030 + 1,560)/3 = 1,243.33 ~ 1,244 units Note: In forecasting, you always need to round off (represented by ~) because you can’t produce partial units such as 1,243.33. Rounding is usually done in the upward direction, as you want to be sure to able to plan on enough inventory (or capacity) for the level of demand that you are anticipating. Exponential Smoothing: This technique is a bit more complicated than the moving average forecast. Exponential smoothing uses a smoothing factor, α (the Greek symbol alpha), having a value between 0 and 1 to weigh the difference between the demand and forecast for the last period. For example, if the forecast for Week 9 is 1,294 ([1,710 + 1,140 + 1,030]/3) using a three week moving average and α = 0.15, the forecast for Week 10 will be:

2 Forecasts and Errors Can You Predict The Future?

1,294 + 0.15(1,560 – 1,294) = 1,294 + 0.15(266) = 1,294 + 39.9 = 1,333.9 ~ 1,334

Now we turn our attention to measures that check the accuracy of a forecast. As stated in the video lecture, three of the most commonly used measures of forecast accuracy are MAD, MSE, and MAPE. The following table shows the forecast and actual demand data for a product for five weeks. On the basis of this information, we wish to calculate the MAD, MSE, and MAPE, in order to determine how effective the forecasts have been.

Note that the “Error Squared” column in the table is simply generated by multiplying the value in the “Deviation” column by itself (“squaring” it). Also, note that the “Absolute Percent Error” column is calculated by dividing the value in the “Absolute Deviation” column by the value in the “Actual” column, and expressing it as a percentage (by multiplying it by 100).

Week Forecast Actual Deviation Absolute

Deviation Error

Squared Absolute Percent Error

1 10 12.4 2.4 2.4 5.76 19.35% 2 10 8.2 -1.8 1.8 3.24 21.95% 3 10 11.2 1.2 1.2 1.44 10.71% 4 10 9.7 -0.3 0.3 0.09 3.09% 5 10 10.7 0.7 0.7 0.49 6.54%

Sum 6.4 11.2 0.616552

MAD = Sum of absolute deviation / n = 6.4/5 = 1.28 MSE = Sum of errors squared / n = 11.02/5 = 2.204 MAPE = (100 × Absolute percent error) / n = (100 × 0.616552)/5 = 12.33% Therefore:

MAD MSE MAPE

1.28 2.204 12.33%

What do these values tell you? Is a MAD of 1.28 good or bad? This is difficult to say. Organizations may compare such values among several forecasting techniques (like those discussed above) in order to determine what is “good enough” as a forecast. The MAD can change based on the magnitude of the data. The MAD measures the dispersion of the observed values from the expected value. You organization must make a judgment as to whether or not your forecast is doing an adequate job in terms of the MAD value. The MSE measures the squared differences between the actual demand and the forecast demand. Like the MAD, the MSE can change based Page 2 of 3

Operations Management

©2017 South University

3 Forecasts and Errors Can You Predict The Future?

on the magnitude of the data. The MAPE measures the average of the absolute differences between the forecast demand and the actual demand. The MAPE value of 12 percent indicates that, on average, your forecast is off by 12 percent.

© 2017 South University

Page 3 of 3

Operations Management

©2017 South University

Quantitative Forecasting

Quantitative forecasts are number based. They may be simple or may rely heavily on statistical

methods. 

You can determine the quality of a forecast by calculating a number of different measures of forecast

accuracy.  Among the most widely used are the mean absolute deviation (MAD), the mean squared error (MSE), or the mean absolute percent error (MAPE).

Besides forecast accuracy, other factors also should be considered when evaluating a forecast. For

example, is the data seasonal? What are the current trends? Are the customers' preferences changing?

These factors can have an impact on the forecast and need to be included. Seasonality can be included

through the use of a seasonal index that relates the average demand in a period to the average demand

in all periods.

A forecast can be created by graphing a series of historical data points and then �tting a line to the

series to predict future values. This graph is considered a trend projection because it assumes the

future will follow the current trend and the same path.

Another technique that helps you forecast demand is regression. This technique assumes a linear

relationship between the independent and dependent variables. Regression differs from other

forecasting techniques because it can provide a distribution of possible values rather than a single

value. This distribution is referred to as the standard error of the estimate. In addition, a regression equation indicates through the coef�cient of correlation how closely the model represents your data.

Quantitative forecasts require calculation.  See the Supplemental Media entitled “Forecasts and

Errors” in order to review multiple forecasting techniques and the calculations associated with the

measures of forecast error listed in the video. 

Additional Materials

Forecasts and Errors (media/week2/SUO_MGT3059%20W2%20L3%20Forecasts%20And%20Errors.pdf?

_&d2lSessionVal=SWzqLkE3HvLXZkZ375Dqp03nU&ou=86458)

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