Chaining the Supply of Business Analytics

Today we come to the end of the Business Analytics course. We learned various data analysis approaches over the period of this course. Many topics were discussed and their applications were blogged about in a serious as well as in a humorous way. But now we come to an end. So, today, in this blog, instead of picking up an approach or a topic, I am going to discuss about the importance of business analytics in general, however specific towards the supply chain management. So here goes.

BA is being increasingly used in SCM. Improving SC performance has become a continuous process that requires an analytical performance measurement system. Moreover, the use of BA aids a knowledge enterprise by promoting efficiency within an organization, particularly by using analytical methods to provide valuable decision-making knowledge to minimize operating costs and accurately forecast market trends. Companies with more mature SC practices, i.e. improved BA capabilities, are thus reducing their costs faster and achieving higher profit margins than their less mature peers. Moreover, higher levels of SCM practice such as a higher level and quality of information sharing can lead to an enhanced competitive advantage and improved performance.

Examples of the potential use of analytics in various areas include:

• in Plan: analyzing data to predict market trends of products and services; until recently, these have often been done in the form of monthly and yearly reports by marketing and finance departments.

• in Source: the use of an agent-based procurement system with a procurement model, search, negotiation and evaluation agents to improve supplier selection, price negotiation and supplier evaluation and the approach for supplier selection/evaluation.

• in Make: the correct production of each inventory item not only in terms of time, but also about each production belt and batch.

• in Deliver: various applications of BA in logistics management have been made in order to bring products to market more efficiently. Nevertheless, since decisions about delivery are usually at the end of the decision cycle and several companies have outsourced their delivery processes the impact of BA in delivery may be limited.

Thus we can see how much BA is important in the field of Supply Chain Management.

With Regards,

Mittul Desai,

13146.

Ops Grp 1

Crosstab

Crosstabs is an SPSS procedure that cross-tabulates two variables, thus displaying their relationship in tabular form. In contrast to Frequencies, which summarizes information about one variable, Crosstabs generates information about bivariate relationships.
Crosstabs creates a table that contains a cell for every combination of categories in the two variables.Inside each cell is the number of cases that fit that particular combination of responses.
SPSS can also report the row, column, and total percentages for each cell of the table.
Because Crosstabs creates a row for each value in one variable and a column for each value in the other, the procedure is not suitable for continuous variables that assume many values. Crosstabs is designed for discrete variables--usually those measured on nominal or ordinal scales.

Discriminant function analysis is used to determine which continuous variables

discriminate between two or more naturally occurring groups.

Discriminant function analysis is multivariate analysis of variance (MANOVA)

reversed. In MANOVA, the independent variables are the groups and the

dependent variables are the predictors. In DA, the independent variables are the

predictors and the dependent variables are the groups.   As previously

mentioned, DA is usually used to predict membership in naturally occurring

groups. It answers the question: can a combination of variables be used to

predict group membership? Usually, several variables are included in a study to

see which ones contribute to the discrimination between groups

A single interval variable might discriminate between groups in an almost perfect fashion, not at all, or somewhere in between. For example, if one wished to differentiate adult males and females, one could collect information on how many bras the person owned, score on the last statistics test, and height. In the case of the number of bras, the discrimination would be very good, but not perfect (some women don't own any bras, some men do). In the case of the score on the last statistics test, little discrimination would be possible because males and females generally score about the same. In the case of height, some discrimination between adult males and females would be possible, but it would be far from perfect.

In general, the larger the difference between the means of the two groups relative to the within groups variability, the better the discrimination between the groups. The following program allows the student to explore data sets with different degrees of discrimination ability

Posted By : -

Raghavendra Ramchandra Nitturkar (13030)

Operations Grp 1

Uses of Factor Analysis in Scale Development and Validation

1. Item analysis. Factor analysis can be used to create subscales of items in a test. For example, in a job satisfaction scale, we might find several different factors corresponding to satisfaction with the work itself, supervision, pay and so forth. We could use the analysis to delete items based on the following criteria:

1. Low final communality (fails to load highly on any factor).

2. Small loading on proper factor (e.g., an item from the work scale doesn't load on the work factor).

3. Large loadings on the wrong factor (e.g., and item from the work scale loads highly on the supervision factor).

Some people advise us to avoid using factor analysis on items for several reasons. One reason is that we often get factors that correspond to characteristics of the distribution of responses rather than content. For example, we may get factors that correspond to easy and hard items. We may get factors of positive and negative items just because a few people missed the NOT in some of the negative items. Another reason is that the distribution of responses and errors cannot be normally distributed (even approximately) with variables that only have 9 or less possible values. This matters if we are going to use maximum likelihood estimates or significance tests. In my opinion you certainly have to watch out for bogus factors. However, when the factors correspond to meaningful content differences, factor analysis presents a very powerful tool for creating multiple scales with high internal consistency and good discriminant validity. High internal consistency will result if you choose items that all have high factor loadings on the same factor (there is a mathematical relation between the loadings and alpha). If you delete items that load on the wrong factor, you promote discriminant validity.

2. Scale validation. When we have developed tests, we can factor analyze a series of test to see whether they conform to the expected pattern or relations. This is, of course, relevant for construct validation. We expect to see that test that purport to measure the same construct should load on the same factor, and that different factors should emerge for different constructs.

Lots of people have factor analyzed MTMM matrices. A matrix that conforms to the Campbell and Fiske criteria will show factors that correspond to traits. Method variance will show up as method factors. Messy factors correspond to other measurement problems.

kartik prakash (FIN grp-6)

13139

Factor analysis

Factor analysis is a statistical method used to describe variability among observed variables. It is a collection of methods used to examine how underlying constructs influence the responses on a number of measured variables. 

There are basically two types of factor analysis: Exploratory and Confirmatory.

• Exploratory factor analysis (EFA) attempts to discover the nature of the constructs influencing a set of responses.
• Confirmatory factor analysis (CFA) tests whether a specified set of constructs is influencing responses in a predicted way.

Factor analyses are performed by examining the pattern of correlations (or covariance’s) between the observed measures. Measures that are highly correlated (either positively or negatively) are likely influenced by the same factors, while those that are relatively uncorrelated are likely influenced by different factors.

Scree plot: The Cattell scree test plots the components as the X axis and the corresponding Eigenvalues as the Y-axis. A scree plot shows the sorted eigenvalues, from large to small, as a function of the eigenvalue index.

A Scree Plot is a simple line segment plot that shows the fraction of total variance in the data.

The Scree Plot has two lines: the lower line shows the proportion of variance for each principal component, while the upper line shows the cumulative variance explained by the first N components. The principal components are sorted in decreasing order of variance, so the most important principal component is always listed first.

Group: Marketing 5

Author : Sanandan Atrey

Conjoint Analysis - Study of trade -offs and preferences

Conjoint analysis, also called multi-attribute compositional models or stated preference analysis, is a statistical technique. Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors. It is concerned with understanding how people make choices between products or services or a combination of product and service, so that businesses can design new products or services that better meet customers’ underlying needs.

The analysis also helps us make models to predict the preferences of the customers, using the existing data. One such exercise we did in class was to give preference/rank/rate/score to the quality levels we wish to have in our spouses. Many variables were discussed which added to 600 odd options which were then reduced to 8 and finally 18 options. These options thus can be used to predict the preferences of the various participants. This technique can thus be used to predict the preferences and thus design new product or services.

Using Conjoint Analysis, the value that individuals place on any product is equivalent to the sum of the utility they derive from all the attributes making up a product. Further, it assumes that the preference for a product and the likelihood to purchase it is in proportion to the utility an individual gains from the product. There are three phases in the analysis of conjoint data: collection of trade-off data through a questionnaire, statistical analysis of the data, and market simulation.

Conjoint analysis applies a complex form of analysis of variance to a respondent’s choice task data to calculate a utility for each level of each attribute. These are basically index numbers which measure how valuable or desirable a particular feature is to the respondent. The idea is each respondent’s choice tasks reveal something about the relative utility that he or she has for each feature. Features which a respondent is reluctant to give up from one choice task to another are judged to be of higher utility to that respondent than features which are quickly given up.

A respondent’s “utility” is a measurement of his or her relative strength of preference for each level of each attribute of the research company. The respondent’s utilities are estimated using a “least squares updating” algorithm. Initial estimates of utilities are based on the respondent’s rank orders of preference and his or her ratings of attribute importance. Estimates are updated following each trade-off task, and the initial estimates have decreasing influence as the interview progresses.

The final estimates are true least squares, with the same weight being applied to each of the respondent’s answers. Utilities scaled in this way are ideal for predicting the likelihood of acceptance; they can be very misleading when reported in the aggregate or for comparing segments. For these purposes, utilities are re-scaled in such a way that the sum of the differences between the maximum and minimum level of each attribute equals the number of attributes times 100. This method assures that all survey respondents’ utilities are equally rated in reports and analyses.

The best way to interpret utilities involves analysis of the gaps between utility levels within an attribute. This “gap” or range between utility levels within an attribute indicates that the survey participants see greater importance between certain attribute levels than between other attribute levels.

Thus this analysis can be used for finding opportunities to shrink product lines, testing whether additional products cannibalize or add to preference, and uncovering segments and aligning products with their preferences.

Author:- Shweta Bhosale(13106)
Group :- Operations 3

Conjoint Analysis
Conjoint analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced. Conjoint analysis is a less expensive and more flexible way to address these issues.
Let’s understand Conjoint Analysis with the help of a simple example.
Suppose we want to buy a laptop. We know from experience that it has 3 important product features – Weight, Battery Life and Price. We further know that there is a range of feasible alternatives for each of these features, for instance
Weight Battery Life Price
3kg 5 years Rs 20,000/-
5kg 4 years Rs 35,000/-
6kg 2 years Rs 40,000/-

Obviously, the market’s “ideal” laptop would be
Weight Battery Life Price
3kg 5 years Rs 20,000/-
and the “ideal” laptop from a cost of manufacturing perspective would be:
Weight Battery Life Price
6kg 2 years Rs 40,000/-
Now the problem here would be that it would be quite easy to sell the first laptop whereas no one would buy the second one. The most viable product would lie somewhere in between. This is what Conjoint Analysis lets us find out.
A traditional research project might start by considering the rankings for Weight and Battery life
Fig 1
Rank Weight Rank Battery Life
1 3 1 2
2 5 2 4
3 6 3 5
This type of information doesn’t tell us anything that we didn’t already know about which laptop to buy.
Now consider the same two features taken conjointly. The two figures below show the rankings of
the 9 possible products for two buyers assuming price is the same for all combinations

Fig 2 : Buyer 1
Weight Battery Life
5 years 4 years 2 years
3 kg 1 2 4
5 kg 3 5 6
6 kg 7 8 9

Fig 3 : Buyer2
Weight Battery Life
5 years 4 years 2 years
3 kg 1 3 6
5 kg 2 5 8
6 kg 4 7 9

Both buyers agree on the most and least preferred laptop but as we can see from their other choices, Buyer 1 tends to trade-off Battery life for Weight, whereas Buyer 2 makes the opposite trade-off.
The knowledge we gain in going from Figure 1 to Figures 2 and 3 is the essence of conjoint analysis
Next, we figure out a set of values for Weight and a second set for Battery Life so that when we add these values together for each laptop they reproduce Buyer 1's rank orders. Now, we figure out the trade-offs Buyer 1 is willing to make between Battery life and price.
Finally, we get a complete set of values (referred to as “utilities”) that capture Buyer 1's trade-offs.
We use this information to determine which laptop to buy based on the estimates of preferences of the buyers.

The three steps--collecting trade-offs, estimating buyer value systems, and making choice
predictions-- form the basics of conjoint analysis.

Group – Marketing 3
Author of the Article – Sahil Kotru & Aruna Iyer(13056)

Conjoint Analysis

Conjoint analysis, also called Multi-attribute Compositional Models or Stated Preference Analysis, is a statistical technique that originated in mathematical psychology. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is not to be confused with the theory of conjoint measurement.

In conjoint analysis, the respondent may be asked to arrange a list of combinations of product attributes in decreasing order of preference. Once this ranking is obtained, a computer is used to find the utilities of different values of each attribute that would result in the respondent’s order of preference. This method is efficient in the sense that the survey does not need to be conducted using every possible combination of attributes. The utilities can be determined using a subset of possible attribute combinations. From these results one can predict the desirability of the combinations that were not tested.

The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worth’s) can be used to create market models that estimate market share, revenue and even profitability of new designs.

Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors.

It has become common practice to present the trade-offs as a choice exercise (where the respondent simply chooses the most preferred alternative from a selection of competing alternatives - particularly common when simulating consumer choices) or as a constant sum allocation exercise (particularly common in pharmaceutical market research, where physicians indicate likely shares of prescribing, and each alternative in the trade-off is the description a real or hypothetical therapy).

When there are many attributes, experiments with Conjoint Analysis include problems of information overload that affect the validity of such experiments. The impact of these problems can be avoided or reduced by using Hierarchical Information Integration.

Advantages

§ Estimates psychological tradeoffs that consumers make when evaluating several attributes together

§ Measures preferences at the individual level

§ Uncovers real or hidden drivers which may not be apparent to the respondent themselves

§ Realistic choice or shopping task

§ Able to use physical objects

§ If appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation

Disadvantages

§ Designing conjoint studies can be complex

§ With too many options, respondents resort to simplification strategies

§ Difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features

§ Respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to

§ Poorly designed studies may over-value emotional/preference variables and undervalue concrete variables

§ Does not take into account the number items per purchase so it can give a poor reading of market share

Author: Krunal Patel

Group: Marketing - Group 4