Balanced Scorecard Model (an overview)

In this Post I will begin writing about the Balanced Scorecard Model (BSC Model).

Most likely, the readers will start at this moment asking "can you tell us what BSC Model is, what it used for, how we can use, and other related questions"

Writing about BSC Model properly needs many posts to really cover the subject.

Therefore, the objective of this post is not complete explanation to BSC Model but just an overview, and then I will replay your questions and cover other issues in the next posts.

Organizations now and from long time spend a lot of time and resources to measure their performance in achieving strategic goals (especially financial goals) and managing their functions. However, they are not happy and meet some obstacles due to the following reasons:

• They are feeling that while measurement is very crucial, their systems for capturing, monitoring, and sharing performance information are critically flawed
• They are feeling that measuring their performance is time-consuming and they need that time to mange their function
• They exclusively rely on financial measures of performance; the traditional method of measurement has been financial although financial measures have no power to predict the future.
• strategic goals are not well communicated
• Department objectives and their budget are not aligned with the strategic goals
• Some organizations neglect to have effective strategy and focus on:

Revenue and market share through heavy discounting, giveaways, and advertising rather than profits, Indirect revenue from advertising, and “click-through fees” from partners and neglect adding real value, and Making all things to all markets by offering many products and services rather than making the difficult trade-offs associated with strategy formulation.

BSC Model Story
What is needed is a system that balances the historical accuracy coming from the financial numbers with the drivers of future performance, while also assisting organizations in implementing their differentiating strategies. The Balanced Scorecard is the tool that answers both challenges and resolves the problems lastly faced.

In 1990, The Balanced Scorecard was developed by two men, Robert Kaplan, a professor at Harvard University, and David Norton, a consultant also from the Boston area. Nowadays, more than 10,000 organizations adopted the use of BSC model

What Is a Balanced Scorecard?
We can describe the Balanced Scorecard as a carefully selected set of measures derived from an organization’s strategy. The measures selected for the Scorecard represent a tool for leaders to use in communicating to employees and external stakeholders the outcomes and performance drivers by which the organization will achieve its mission and strategic objectives.

The balanced scorecard proposes viewing the organization from four perspectives:

• The Financial Perspective,
• The Customer Perspective,
• The internal process Perspective, and
• The Learning and Growth Perspective, and

To be continued in the next posts

Hence, if every reader "I think" is now attracted to know more about Balanced Scorecard model, he will be eager to follow the next posts and ask questions about BSC to be covered in the following posts.

Some Product Testing Techniques

Monadic

Monadic testing typically is the best method. Testing a product alone offers
many advantages. Interaction between products (which occurs in paired-comparison tests) is eliminated. The monadic test simulates real life (that's the way we
usually use products, one at a time). By focusing the respondent's attention
upon one product, the monadic test provides the most accurate and actionable
diagnostic information. Additionally, the monadic design permits the use of
normative data and the development of norms and action standards.
Virtually all products can be tested monadically, whereas many products cannot be
accurately tested in paired-comparison designs. For example, a product with a
very strong flavor (hot peppers, alcohol, etc.) may deaden or inhibit the taste
buds so that the respondent cannot really taste the second product.

Sequential Monadic

Sequential monadic designs are often used to reduce costs. In this design, each
respondent evaluates two products (he or she uses one product and evaluates it,
then uses the second product and evaluates it). The sequential monadic design
works reasonably well in most instances, and offers some of the same advantages
as pure monadic testing.
One must be aware of what we call the "suppression
effect" in sequential monadic testing, however. All the test scores will be
lower in a sequential monadic design, compared to a pure monadic test.
Therefore, the results from sequential monadic tests cannot be compared to
results from monadic tests. Also, as in paired-comparison testing, an
"interaction effect" is at work in sequential monadic designs. If one of the two
products is exceptionally good, then the other product's test scores are
disproportionately lower, and vice versa.

Protomonadic

The protomonadic
design (the definition of this term varies greatly from researcher to
researcher) begins as a monadic test, followed by a paired-comparison. Often,
sequential monadic tests are also followed by a paired-comparison test. The protomonadic design
yields good diagnostic data, and the paired-comparison at the end can be thought
of as a safety net—as added insurance that the results are correct. The protomonadic design is
typically used in central-location taste testing, not in-home (because of the
complexity of execution in-home).
Paired-Comparison
Paired-comparison
designs (in which the consumer is asked to use two products and determine which
product is better) appeal to our common sense. The Paired-Comparison is a
wonderful design if presenting evidence to a jury, because of its "face value"
or "face validity." It can be a very sensitive testing technique (i.e., it can
measure very small differences) between two products. Also, the
paired-comparison test is often less expensive than other methods, because
sample sizes can be smaller in some instances.
Paired-comparison testing,
however, is limited in value for a serious, ongoing product testing program. The
paired-comparison test does not tell us when both products are bad and does not
lend itself to the use of normative data. It is heavily influenced by the
"interaction effect" (i.e., any variations in the control product will create
corresponding variance in the test product's scores).

Repeated Pairs

A repeated paired-comparison taste test is exactly what the name suggests. Each
respondent participates in a paired-comparison taste test (e.g., product J
versus product H), followed by a second paired-comparison test (product J versus
product H). However, in the second test, the products are presented as two
different products (i.e., not labeled as products J and H).
The purpose of
the repeated paired-comparison taste test is to identify non-discriminators, the
people who don’t choose the same product in both tests. That is, it is assumed
that someone who chooses product J in the first paired-comparison test and
chooses product H in the second paired-comparison test cannot taste (or detect)
any difference between the two products. Typically, these non-discriminators’
answers would not be counted. The final results would be based only on
respondents who could discriminate between the two products (i.e., based only on
those who chose the same product both times).

Triangle Test

The triangle taste test is used primarily for "difference testing." Each
participant is presented with three products and asked to taste all three and
choose the one that is different from the other two. The triangle taste test is
used to determine who can discriminate (i.e., consistently identify the one
product that’s different), and who cannot.
These discriminators are in turn
used as members of small expert panels (sometimes called sensory panels) to
assist research and development in formulating and reformulating products, using
the triangle design to determine if a particular ingredient change, or a change
in processing, creates a detectable difference in the final product. Triangle
taste testing is also used in quality control to determine if a particular
production run (or production from different factories) meets the
quality-control standard (i.e., is not different from the product standard in a
triangle taste test using discriminators).

Sensory Research

The term "sensory research" tends to be used by research and development
scientists and food scientists in much the same way that the marketing world
uses the term "product testing." Many of the methods are identical or very
similar. In general usage, the term "sensory research" tends to refer to
small-scale product testing that is used by research and development scientists
to help them in formulating new foods and beverages, and in reformulating
existing food and beverage products.
Often sensory research is conducted
with small panels of consumers, or small groups of employees, who have
demonstrated an above-average ability to taste, or to detect, small differences
in the flavor profile of a food or beverage.

Ingredient Screening

As a preliminary step in attempting to optimize a particular food or beverage
formulation, it is valuable to develop an understanding of the relative
importance and role of the different ingredients in the formulation. Typically,
a number of product formulations are created, each with a high level and a low
level (or absence) of a particular ingredient—with all other ingredients held
constant. Each respondent usually rates three to five of these different
products, depending upon the type of product.
The products are rated on
overall appeal as well as specific attributes (sweetness, texture, mouth feel,
etc.). Who tastes which product is determined by a complex experimental design
plan. The resulting data are analyzed via ANOVA and MANOVA statistical techniques, as well as regression
and discriminated analyses.

Product Optimization

Product optimization refers to the process of improving a product until it
reaches a maximum level of consumer satisfaction or acceptability. A variety of
research methods can be used to achieve an optimal product, but the term
"product optimization" most typically refers to a structured process in which
various ingredients are systematically varied to create a number of different
products.
These products are then rated by a sample of category users, with
each respondent rating three to five different formulations on overall appeal
and rating specific qualities (moistness, saltiness, color, etc.) of the
products. The resulting data is then analyzed by ANOVA and MANOVA, regression and discriminate analyses, and
(depending upon the design) by response surface analyses. The output of the
analysis is a prediction of the product formulation that would be
optimal.