Instructions Course Project Tasks: Using the financial information gathered in Week 1, address the following questions or issues:

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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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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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. 

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

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

MGT3059 Week 2 Project Rubric Course: MGT3059-Operations Management SU01

Criteria No Submission 0 points

Emerging (F through D Range) (1-2) 2 points

Satisfactory (C Range) (3) 3 points

Proficient (B Range) (4) 4 points

Exemplary (A Range) (5) 5 points

Criterion Score

Identified

inefficiencies in

the way that

human

resources are

utilized in a

business at

which you have

worked in the

past or with

which you are

familiar.

/ 5Did not identify inefficiencies in the

way that human

resources are

utilized.

Described human

resource utilization

in a way that

indicated that the

current practices

were as efficient as

possible.

Described human

resource

utilizations that are

not clearly

inefficient.

Identified

inefficiencies in the

way that human

resources are

utilized in a

business at which

you have worked in

the past or with

which you are

familiar.

Provided insightful

observations as to

why human

resources are not

being utilized

efficiently.

Criteria No Submission 0 points

Emerging (F through D Range) (1-6) 6 points

Satisfactory (C Range) (7) 7 points

Proficient (B Range) (8) 8 points

Exemplary (A Range) (9-10) 10 points

Criterion Score

Criteria No Submission 0 points

Emerging (F through D Range) (1-6) 6 points

Satisfactory (C Range) (7) 7 points

Proficient (B Range) (8) 8 points

Exemplary (A Range) (9-10) 10 points

Criterion Score

Proposed

solutions to

eliminate

inefficiency in

terms of the use

of human

resources.

/ 10Did not suggest solutions that

might eliminate the

inefficiencies.

Proposed solutions

that would actually

decrease efficiency.

Proposed solutions

that did not appear

to be directly

related to the

inefficiency under

discussion.

Proposed solutions

that might

eliminate the

inefficiency in

terms of the use of

human resources.

Offered thoughtful

solutions to the

inefficiency

problem in human

resource

deployment.

Criteria No Submission 0 points

Emerging (F through D Range) (1-2) 2 points

Satisfactory (C Range) (3) 3 points

Proficient (B Range) (4) 4 points

Exemplary (A Range) (5) 5 points

Criterion Score

Defined

changes to job

designs that

might be

needed to

implement the

proposed

solutions.

/ 5Did not suggest job design changes that

might be needed.

Described changes

to job designs that

would make it less

likely to be able to

improve efficiency.

Described changes

to job design that

are not clearly

related to proposed

solutions.

Defined changes to

job designs that

might be needed to

implement the

proposed solutions.

Provided clever and

convincing

proposals to

change job designs.

Criteria No Submission 0 points

Emerging (F through D Range) (1-2) 2 points

Satisfactory (C Range) (3) 3 points

Proficient (B Range) (4) 4 points

Exemplary (A Range) (5) 5 points

Criterion Score

Identified

changes to

work

measurement

that might be

needed to

implement the

proposed

solutions.

/ 5Did not define changes to work

measurement that

might be needed.

Identified work

measurement

changes that would

lessen the ability to

implement the

proposed solutions.

Suggested changes

to work

measurement that

would not have an

impact on the

success of the

proposed solutions.

Identified changes

to work

measurement that

might be needed to

implement the

proposed solutions.

Offered changes to

work measurement,

including a clear

rationale for each

such change.

Criteria No Submission 0 points

Emerging (F through D Range) (1-6) 6 points

Satisfactory (C Range) (7) 7 points

Proficient (B Range) (8) 8 points

Exemplary (A Range) (9-10) 10 points

Criterion Score

Explained if and

why changes to

compensation

might be

needed to

implement the

proposed

solutions.

/ 10Did not discuss changes in

compensation.

Provided irrational

explanations as to

why compensation

changes might be

needed.

Did not offer a

clear rationale as to

whether or not

changes to

compensation

might be justified.

Explained if and

why changes to

compensation

might be needed to

implement the

proposed solutions.

Offered insightful

analysis as to why

compensation

changes might be

needed.

Criteria No Submission 0 points

Emerging (F through D Range) (1-2) 2 points

Satisfactory (C Range) (3) 3 points

Proficient (B Range) (4) 4 points

Exemplary (A Range) (5) 5 points

Criterion ScoreCriteria No Submission 0 points

Emerging (F through D Range) (1-2) 2 points

Satisfactory (C Range) (3) 3 points

Proficient (B Range) (4) 4 points

Exemplary (A Range) (5) 5 points

Criterion Score

Communication:

Use of tone,

word choice,

audience,

transitions, and

progression of

ideas.

/ 5

Mechanics: Use

of grammar,

sentence

structure, and

spelling.

/ 5

Submission

contained no

discernible overall

intent in author’s

selection of ideas.

Submission

contained random

presentation of

ideas, which

prevented

understanding the

majority of author’s

overall intent.

Ideas presented in

a way that forced

the reader to make

repeated inferences

in order to identify

and follow the

author’s overall

intent.

The reader could

follow the author’s

overall intent as

stated.

The writer’s overall

argument and

language were clear

and tightly focused,

leaving the reader

with no room for

confusion about

author’s intent.

Errors in basic

writing conventions

were sufficiently

numerous to

prevent reader

comprehension.

Errors in basic

writing conventions

were sufficiently

numerous to

prevent reader

comprehension of

majority of the

work.

Errors in basic

writing conventions

interfered with, but

did not prevent,

reader

comprehension.

The reader noticed

a few errors in

basic writing

conventions but

these few errors

did not interfere

with reader

comprehension.

Test was basically

error free, so that a

reader would have

to purposely search

to find any errors

that may be

present.

Total / 50

Criteria No Submission 0 points

Emerging (F through D Range) (1-2) 2 points

Satisfactory (C Range) (3) 3 points

Proficient (B Range) (4) 4 points

Exemplary (A Range) (5) 5 points

Criterion Score

Academic/APA/

PPT Formatting

Use of citations,

references, and

structural

formatting

including title

page, running

head, page

numbers,

headings, title

slides, graphics,

data, notes

section, (as

appropriate),

introduction,

and conclusion.

/ 5No attempt at Academic/APA/PP

T formatting in

presentation.

Academic/APA/PP

T format

attempted, but

errors were

significant,

preventing

comprehension of

message.

Academic/APA/PP

T format attempted

but errors were

distracting.

Used

Academic/APA/PP

T format

accurately. Errors

noticeable but

minor.

Used

Academic/APA/PP

T format

proficiently. Work

basically error free.

Overall Score

No Submission 0 points minimum

Emerging (F through D Range) 1 point minimum

Satisfactory (C Range) 35 points minimum

Proficient (B Range) 40 points minimum

Exemplary (A Range) 45 points minimum