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International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 6, November - December (2013) © IAEME

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INTEGRATED BIZ TO BIZ PREDICTIVE COLLABORATION

PERFORMANCE EVALUATION FRAMEWORK

Anantha Keshava Murthy1, R Venkataram

2, S G Gopalakrishna

3

1Associate Professor, Dept of ME, EPCET, Bangalore, India,

2Director Research, EPCET, Bangalore, India,

3Principal, NCET, Bangalore, India,

INTRODUCTION

Collaboration between Supply Chain partners have been covered extensively in the strategic

management literature [01]. As a fact, several research surveys have shown that the core of supply

chain management and Biz to Biz supply chains and performance evaluation systems enabling the

development of a process predictive collaborative performance evolution and decision making which

has leading collaborative capabilities is the process improvement at the inter-enterprises level [02].

Some researchers have examined the theoretical implications of supply chain collaboration through

unilateral supply policies [03]. Some recent studies are interested in a better characterization of the

collaborative supply chain.

In recent times, most competitiveness improvements have concentrated on performance

measurement (PM) systems in many organizations. It has been recognized in terms of the

interrelationship [04]. PM has often pointed out the KPI improvement on the individual financial

aspects without concerning the collaboration [05]. It is due time to change PM with the innovation in

terms of long-term collaborative value. The performance tendency forecasting was based on the

learning system from the in-deep experiences of the supply chain managers and experts rather

than just only comparison of historical data. Some of the interested aspects were proposed to be

added in PM system such as: trust degree between partners, degree of information system sharing,

long-term orientation and involvement of the partners. There are many approaches to construct the

most suitable PM for one’s own supply chain; still, the small number of researches about future

performance planning capacity can provide the right direction after measurement instead of how they

evaluate the previous KPI results.

One of the major approaches to analytics is to identify the impending change in trend in any

Key Performance Indicators (KPIs) before it accelerates. This kind of early warning systems are very

important and will be useful in various scenarios like vendor management for services, service

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quality Enhancement etc., However, using only a scalar value to compare multiple series, rank them

and project the series to the future is not appropriate, even though practically it is possible.

Recently, many firms are exposed to a sophisticated environment which is constituted by open

markets [06], globalization of sourcing, intensive use of information technologies, and decreasing in

product lifecycles. Moreover, such a complexity is intensified by consumers who are becoming

increasingly demanding in terms of product quality and service. It means globalization has increased

firms’ internationalization, shifting them from local to global markets and with increasing

competitiveness [07]. Furthermore, the dynamic environment (consisting of competitors, suppliers’

capacity, product variability and customers) complicated the business process. To that end, many

enterprises are often forced to cooperate together within a Supply Chain (SC) by forming a virtual

enterprise which is a network of agents typically consisting of suppliers, manufacturers, distributors,

retailers and consumers. Previous research [08] posits that SC can be considered as a network of

autonomous and semi-autonomous business entities associated with one or more family related

products.

Consumers (i.e., end or industrial) often require different types of products & services, ranging

from ordering batches to maintaining final products. This process needs suppliers to manage their

demand chain activities which are often based on customer demand [09]. Previous research in this area

posits the importance of internet-based tools in aiding this process [10]. It has been reported that a

number of challenges have to be faced while fulfilling demand management through supply chain

collaboration.

The ever changing market with prevailing volatility in business environment with constantly

shifting and increasing customer expectations is causing two types of timeframe based uncertainties

that can affect the system. They are: (i) short term uncertainties and (ii) long term uncertainties. Short

term uncertainties include day-to-day processing variations, such as cancelled/rushed orders,

equipment failure etc. Long term uncertainties include material and product unit price fluctuations,

seasonal demand variations. Understanding uncertainties can lead to planning decisions so that

company can safeguard against threats and can avoid the affect of uncertainties. As a result, any

failure in recognizing demand fluctuations often hold unpredictable consequences such as losing

customers, decrease in the market share and increasing in costs associated with holding inventories

[11].

In order to achieve competitive advantage, manufacturers are forced to rely on the agile supply

chain capabilities in the contemporary scenario of changing customer requirements and expectation as

well as with the changing technological requirements. SC integration often is considered as a vital tool

to achieve competitive advantage [12]. Previous research proved the implementation difficulty due to

certain factors such as lack of trust among partners and depending solely on technology.

Consideration to People, Processes and Technology, BI analytics and PM initiative from the

perspective of three groups of participants, are Analysts, Users and IT staff.

Analysts define and explore business models, mine and analyze data and events, produce

reports and dashboards, provide insights into the organization’s performance and support the decision-

making processes. With a deep understanding of business issues and related performance measures

and good communications, a tricky balance to achieve. Technological trends in collaboration and

social software, combined with trends in the business world for more transparent and fact-based

decision making, will lead to a new style of decision support model and system that will give further

leverage to the work of analysts . It will be necessary to put in collaborative processes and

infrastructure to help analysts get their analytical insights consumed more broadly by the user

community and to have their analysis available and/or embedded in other business and analytic

applications .



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Users “consume” the information, analysis and insight produced by applications and tools to

make decisions or take other actions that help the enterprise achieve its goals. Some users may be

more than just consumers, such as the top executives who will help craft the performance metric

framework. Users may also include operational workers, in addition to executives and managers. The

users determine how well BI, analytics and PM initiatives succeed. Considering users’ requirements

from several perspectives:

• What roles do they need to play in analytic, business and decision processes? For example,

finance executives responsible for managing corporate budgets and plans will need different

analytic applications from the operations manager of a highly automated manufacturing

environment.

• What metrics, data and applications do they have and/or need? Analytic applications turn data

into the information the users need to make the appropriate decisions and support their

management processes. And every user wants timely, relevant, accurate, and consistent data and

analysis, but each user may define those terms differently and need data from different domains,

one seeking product data, another focusing on customer data, and so on.

• How do the metrics and needs change over time? Any of the factors that determine a user’s

needs at a given moment can change at any time, including business strategy, processes, roles,

goals and available data . Even if all these factors remain the same, the insights delivered to

users will lead them to ask new questions.

IT Enablers, who help design, build and maintain the systems that users and analysts use (see

Note 1). Traditional IT roles such as project managers, data and system architects, and developers

remain important. But BI, analytics and PM initiatives require more than simply building applications

to fit a list of requirements. Those applications also have to deliver business results. Users have to

want to use them. They have to support analytic, business and decision processes. Thus, IT enablers

need business knowledge and the ability to work collaboratively outside their traditional area of

expertise.

With the growing number of large data warehouses [13] for decision support applications,

efficiently executing aggregate queries is becoming increasingly important. Aggregate queries are

frequent in decision support applications, where large history tables often are joined with other tables

and aggregated. Because the tables are large, better optimization of aggregate queries has the

potential to result in huge performance gains. Unfortunately, aggregation operators behave

differently from standard relational operators like select, project, and join. Thus, existing rewrite

rules for optimizing queries almost never involve aggregation operators. To reduce the cost of

executing aggregate queries in a data warehousing environment, frequently used aggregates are often

pre computed and materialized. These materialized aggregate views are commonly referred to as

summary tables. Summary tables can be used to help answer aggregate queries other than the view

they represent, potentially resulting in huge performance gains. However, no algorithms exist for

replacing base relations in aggregate queries with summary tables so the full potential of using

summary tables to help answer aggregate queries has not been realized.

In this research work an attempt has been made to develop an integrated Biz to Biz supply

chain predictive performance evaluation system for analyzing the multidimensional data, ranking the

time series and predicting the time series to the future. The idea was to have systematic manners to

predict future collaborative performance using a crosstab query to provide the transposed time series



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data for selected dimensions, similarity search module which will rank the time series data and a

prediction module based on moving average/regression forecasting model.

BACKGROUND AND LITERATURE REVIEW

In this section, tried to explore some basic concepts and the literature which is mostly related

and essential to my work such as: supply chain management, Bullwhip effect, Collaborative CRM

processes, knowledge management, Key finding of analytics and performance management

framework, Overview of KPI analysis methodology, Data warehouse and analytical processing, Data

mining, Performance measurement, Optimizing Aggregations, Autoregressive Integrated Moving

Average , Granger causality test as an exploratory tool, etc.

Supply Chain Management Supply Chain Management focuses on managing internal aspects of the supply chain. SCM is

concerned with the integrated process of design, management and control of the Supply Chain for the

purpose of providing business value to organisations lowering cost and enhancing customer

reachability. Further, SCM is the management of upstream and downstream relationships among

suppliers to deliver superior value at less cost to the supply chain as a whole. Many factors such as

globalization and demand uncertainty pressures forced companies to concentrate their efforts on core

business [14]. A process which leads many companies to outsource less profitable activities so that

they gain cost savings as well as increased focus on core business activities. As a result, most of these

companies have opted for specialization and differentiation strategies. Moreover, many companies are

attempting to adopt new business models around the concept of networks in order to cope with such a

complexity in making planning and predicting [15]. The new changes in business environment have

shifted the concentration of many companies towards adopting mass-customization instead of mass-

production. Further, it derives the attention of many companies to focus their effort on markets and

customer value rather than on the product [16]. From International Journal of Managing Value and

Supply any single company often cannot satisfy all customer requirements such as fast-developing

technologies, a variety of product and service requirements and shortened product lifecycles. Creating

such new business environments have made companies look to the supply chain as an ‘extended

enterprise’, to meet the expectations of end-customers.

Bullwhip Effect In a Supply Chain (SC), the uncertainty market demands of individual firms are usually driven

by some macro-level, industry-related or economy-related environmental factors. These are

individually managed demand forecasts and are causing SC to become inefficient in three ways: (i)

supply chain partners invest repeatedly in acquiring highly correlated demand information which

increases the overall cost of demand forecasting (ii) the quality of individual forecasts is generally

sub-optimal, since individual companies have only limited access to information sources and limited

ability to process them, it results in less accurate forecasts and inefficient decision making (iii) firms

vary in their capability to produce good quality forecasts.

The phenomenon of bullwhip effect is related to SCM. It is often considered as magnified and varied

order volumes observed at upstream nodes in Supply Chain. [17]. However the term bullwhip was

used by Procter & Gamble managers which observed the increase of variability of vendors and

distributors orders (with respect to the customer demand) through an empirical observation.



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Strategies to Counteracting Bullwhip Effect Companies can reduce uncertainty by having information shared along the whole supply chain

providing the complete information related to customer demand at each stage. Other counteracts of

bullwhip effect include channel alignment [for e.g. alignment of Point-Of-sale (POS), with Electronic

Data Interchange (EDI)] and operational efficiency (for e.g., everyday low price) [18].

Collaborative CRM Processes CRM entails all aspects of relationships a company has with its customers [19] from initial

contact, presales and sales to after-sales, service and support related. Collaboration between firms can

improve the involved intra-organizational business processes. The identification and definition of

collaborative CRM core processes is still ambiguous. Collaborative business processes that can be

found in literature are marketing campaigns, sales management, service management, complain

management, retention management, customer scoring, lead management, customer profiling,

customer segmentation, product development, feedback and knowledge management.

Knowledge Management Business benefits of these investments included transactional efficiency, internal process

integration, back-office process automation, transactional status visibility, and reduced information

sharing costs. While some of the enterprise started to think of in the direction of acquiring and

preserving the knowledge, the primary motivation for many of these investments was to achieve better

control over day-to-day operations.

The concept of knowledge management: Just like knowledge itself, knowledge management is

difficult to define [20]. However, is believed that defining what is understood by knowledge

management may be somewhat simpler than defining knowledge on its own. The idea of

‘management’ gives us a starting point when considering, for example, the activities that make it up,

explaining the processes of creation and transfer or showing its main goals and objectives without the

need to define what is understood by knowledge. Consequently, in literature there are more ideas and

definitions on knowledge management than just on knowledge, although these are not always clear as

there are numerous terms connected with the concept.

KPI Analysis Methodology To improve supply chain management performance in a systematic [21] way, I propose a

methodology of analyzing iterative KPI accomplishments. The framework consists of the following

steps (see Fig.1). First, the managers identify and define KPIs and their relationships. Then, the

accomplishment costs of these KPIs are estimated, and their dependencies are surveyed. Optimization

calculation (e.g., structure analysis, computer simulation) is used to estimate the convergence of the

total KPI accomplishment cost, and to find the critical KPIs and their improvement patterns. Then the

performance management strategy can be adjusted by interpreting the analysis results. The following

sections discuss the details of this methodology. Identifying KPI and model their relationships,

Managers in supply chains usually identify KPIs according to their objective requirements and

practical experiences. But to get a systematic or balanced performance measurement, they often turn

[22] to some widely recognized models, such as BSC and SCOR.



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Fig.1- A research framework of improving supply chain KPIs accomplishment

Conventional wisdom tells us a few things about establishing key performance indicators. It

goes something like this: Determine corporate goals. Identify metrics to grade progress against those

goals. Capture actual data for those metrics. Jam metrics into scorecards. Jam scorecards down the

throats of employees. Cross fingers. Hope for the best.

A Data Warehouse and Analytical Processing

Construction of data warehouses, involves data cleaning and data integration [23]. This can

be viewed as an important pre-processing step for data mining. Moreover, data warehouses provide

analytical processing tools for the interactive analysis of multidimensional data of varied

granularities, which facilitates effective data mining. Furthermore, many other data mining functions

such as classification, prediction, association, and clustering, can be integrated with analytical

processing operations to enhance interactive mining of knowledge at multiple levels of abstraction.

Subject-oriented: A data warehouse [24] is organized around major subjects, such as customer,

vendor, product, and sales. Rather than concentrating on the day-to-day operations and transaction

processing of an organization, a data warehouse focuses on the modelling and analysis of data for

decision makers. Hence, data warehouses typically provide a simple and concise view around

particular subject issues by excluding data that are not useful in the decision support process.

Integrated: A data warehouse is usually constructed by integrating multiple heterogeneous sources,

such as relational databases, at les, and on-line transaction records. Data cleaning and data

integration techniques are applied to ensure consistency in naming conventions, encoding structures,

attribute measures, and so on.



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A data cube: A data cube [25] allows data to be modelled and viewed in multiple dimensions. It is

defined by dimensions and facts. In general terms, dimensions are the perspectives or entities with

respect to which an organization wants to keep records. For example, consider a sales data from

XYZ company and data warehouse in order to keep records of the store's sales with respect to the

dimensions time, item, branch, and location.

Fact table: Sales (Facts are numerical measures Ex; Sales Amount, Number of Units Sold) Fact table

contains the names of the facts, or measures, as well as keys to each of the related dimension tables.

Dimensions Tables: Time, item, branch and location, (These dimensions allow the store to keep

track of things like monthly sales of items, and the branches and locations at which the items were

sold).

Data Mining (DM) Data mining has been broadly utilized and accepted in business and production during the

1990s [26]. Currently, data mining is made of use not only in businesses but also in many different

areas in supply chain and logistics engineering. A few examples are demand forecasting system

modelling, SC improvement roadmap rule extraction, quality assurance, scheduling, and decision

support systems. The data mining techniques can normally be categorized into four sorts i.e.,

association rules, clustering, classification, and prediction. At the turn of century, the decision

makings were used in production management to choose the suitable and agile solutions in real

production. Data warehouse systems allow for the integration of a variety of application systems.

They support information processing by providing a solid platform of consolidated, historical data

for analysis. E.

Performance Measurement (PM) On the other research rivers, PM context [27] comprised of the multi-criteria decision

attribute (MCDA) are most commonly accepted for use. The classifications are as follows,

hierarchical techniques, deployment approaches, scoring method and objective programming. For

example, performance improvement of the selection of freight logistics hub in Thailand was

developed by coordinated simulation. K. A. Associates [28] figured out that PM, among

collaborative SC networks, is vital for management. There have been many certain attempts to

deploy and explore AI and data mining techniques to make up for the typical techniques in optimizing

PM in SCM with a better development roadmap.

Optimizing Aggregations Viewing aggregation as an extension of duplicate eliminating (distinct) projection provides

very useful intuition for reasoning about aggregation operators inquiry trees. Rewrite rules for

duplicate-eliminating projection often can be used as building blocks to derive rules for the more

complex case of aggregation. In addition to the intuition obtained by viewing aggregation as

extended duplicate-eliminating projection, modelling both with one operator makes sense from an

implementation point of view. Typically, in existing query optimizers both aggregations and

duplicate eliminating projections are implemented in the same module. Presentation made to a set of

query rewrite rules for moving aggregation operators in a query tree. Other authors have previously

given rewrite rules for pulling aggregations up a query tree and for pushing aggregations down a

query tree [29]. My work unifies their results in a single intuitive framework, and using this

framework can derive more powerful rewrite rules. I present new rules for pushing aggregation

operators past selection conditions (and vice-versa) and show how selection conditions with

inequality comparisons can cause aggregate functions to be introduced into or removed from a



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query tree. Also presented rules of coalescing multiple aggregation operators in a query tree into a

single aggregation operator, and conversely, rules for splitting a single aggregation operator into

two operators.

Autoregressive Integrated Moving Average (ARIMA) ARIMA model was introduced by Box and Jenkins (hence also known as Box-Jenkins

model) in 1960s for forecasting a variable [30]. ARIMA method is an extrapolation method for

forecasting and, like any other such method, it requires only the historical time series data on the

variable under forecasting. Among the extrapolation methods, this is one of the most sophisticated

methods, for it incorporates the features of all such methods, does not require the investigator to

choose the initial values of any variable and values of various parameters a priori and it is robust to

handle any data pattern. As one would expect, this is quite a difficult model to develop and apply as

it involves transformation of the variable, identification of the model, estimation through non-linear

method, verification of the model and derivation of forecasts.

Granger Causality Test as an Exploratory Tool Testing for causality in the sense of Granger involves using statistical tools for testing

whether lagged information on a variable u provides any statistically significant information about

the variable y. If not, then u does not Granger-cause y. The Granger Causality Test [31] compares the

residuals of an Auto Regressive Model (AR Model) with the residuals of an Auto Regressive

Moving Average Model (ARMA). If the Granger Causality Index (GCI) g is greater than the

specified critical value for the F−test, then reject the null hypothesis that u does not Granger-cause y.

As g increases, the p−value3 associated with the pair ({u (k)}, {y (k)}) decreases, lending more

evidence that the Null Hypothesis is false. In other words, high values of g are to be understood as

representing strong evidence that u is causally related to y [32].

Analytics and Performance Management Framework

This framework defines the people, processes and technologies [33] that need to be integrated

and aligned to take a more strategic approach to business intelligence (BI), analytics and performance

management (PM) initiatives.

• Most organizations use a combination of vendors, products and services to provide BI, analytics

and PM solutions.

• Successful managers recognize the diversity and interrelationships of the analytic processes

within the enterprise and can address the needs of a diverse set of users without creating silos .

• A strategic view requires defining the business and decision processes, the analytical processes,

as well as the processes that define the information infrastructure independently from the

technology.

• The PM, technology and complexity of skills associated with the strategic use of BI, analytics

and PM increases dramatically as the scope of the initiative widens across multiple business

processes.

• There is no single or right instantiation of the framework; different configurations can be

supported by the framework based on business objectives and constraints.

Proposals,

• Use this framework to develop a strategy to surface key decisions, integration points, gaps,

overlaps and biases that business and program managers may not have otherwise prepared for .



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• A portfolio of BI, analytic and PM technologies will be needed to meet the diversity of

requirements of a large organization.

• Seek the advice from program management specialists so as to balance investments across

multiple projects and consider bringing BI, analytics and PM initiatives within a formal program

management framework.

OBJECTIVE AND METHODOLOGY

The Purpose or objective of the research is ‘’to develop an integrated Biz to Biz supply

chain predictive collaboration performance evaluation Framework, based on multiple decision

making’’.

The methodology is based on multiple decision making, They are - (a) an analytical query

system based on an advanced column store database server, which provides the aggregated time

series data from a star schema data mart, Classification and Regression Tree and K-means, is

shown in Fig. 2. (b) a time series ranking module which ranks the time series with an adoptive

algorithm and (c) a prediction module, which provides a simple but effective parametric model

building capabilities. C and RT and K-Means model for Biz to Biz collaborative performance

prediction was performed based on the two data source. One was the training and other one testing

set. In this, both worst and best relationship types were selected at the collaborative performance

learning data files to demonstrate this framework potential. Before C and RT model construction,

Pearson feature selection was applied to identify the significant inputs which have an effect on

collaborative performance score. Next, the model components using these files were defined by the

general configuration from domain experts concerning their related SC context. Finally, the results

were interpreted by the domain expert to forecast the overall collaborative performance and plan

their collaborative performance improvement direction.

Fig. 2- The Methodology of B to B - SC Predictive Collaborative Performance Evaluation Model



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Building Dimensions Tables A dimension table, for example a table Item - contain the attributes item name, brand, and

type. Consider simple 2-D data cube which is, in fact, a table or spreadsheet for sales data items sold

per quarter in the city of Surat .

The Table-1 to represent a 2-D View of sales data for XYZ company according to the

dimensions time and item, at where the sales are from branches located in the city of Surat. The

measure displayed is Rupees sold.

Viewing things in 4-D becomes tricky. However, I can think of a 4-D cube as being a series

of 3-D cubes, as shown in Fig. 3. If we continue in this way, we may display any n-D data as a series

of (n_1)-D ‘’cubes". The important thing to remember is that data cubes are n-dimensional, and do

not confine data to 3-D.

Table-1: A 2-D View of sales data for XYZ company

Fig. 3 - A 3-D data cube representation of the data in Table- 2

Above Fig. 3 represents a 3-D data cube representation of the data in Table-2, according to the

dimensions time, item, and location individually for each location. The measure displayed is Rupees

sold for each location.

Fig. 4 - A 3-D data cube representation of the data in Table 2 combined



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Above Fig. 4 represents a 3-D data cube representation of the data in Table-2, according to

the dimensions time, item, and location. The measure displayed is Rupees sold. The 0-D cuboid

which holds the highest level of summarization is called the apex cuboid. The apex cuboid is

typically denoted by all.

Fig. 5: Lattice of cuboids, making up a 4-D data cube

Above Fig. 5 represents, Lattice of cuboids, making up a 4-D data cube for the dimensions

time, item, location, and supplier. Each cuboid represents a different degree of summarization. Stars,

Snowflakes, and fact constellations: schemas for multidimensional databases The entity-relationship

data model is commonly used in the design of relational databases,

A multidimensional data model : A compromise between the star schema and the Snowflake schema

is to adopt a mixed schema where only the very large dimension tables are normalized. Normalizing

large dimension tables saves storage space, while keeping small dimension tables un normalized may

reduce the cost and performance degradation due to joins on multiple dimension tables. Doing both

may lead to an overall performance gain. However, careful performance tuning could be required to

determine which dimension tables should be normalized and split into multiple tables.

Fact constellation: Sophisticated applications may require multiple fact tables to share dimension

tables. This kind of schema can be viewed as a collection of stars, and hence is called a galaxy

schema or a fact constellation.

Fig. 6: Fact constellation schema of a data warehouse for sales and shipping



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Examples for defining star, Snowflake, and fact constellation schemas : A relational query languages

like SQL is to be used to specify relational queries [34], a data mining query language can be used to

specify data mining tasks. In particular, we examine an SQL-based data mining query language

called DMQL which contains language primitives for defining data warehouses and data marts.

Language primitives for specifying other data mining tasks, such as the mining of concept/class

descriptions, associations, classifications, and so on, will be introduced in Chapter .

Data warehouses and data marts can be defined using two language primitives, one for cube

definition and one for dimension definition. The cube definition statement has the following syntax.

define cube.., {cube_name} [{dimension_list}] : {measure_list the dimension definition statement has

the following syntax.define dimension.., {dimension_ name} as ({attribute or sub_dimension list})

Examples to define the star, snowflake and constellations schemas of Examples 2.1 to 2.3 using

DMQL. DMQL keywords are displayed in sans serif font.

Finally, a fact constellation schema can be defined as a set of interconnected cubes. Below is

an example. Example 2.6 The fact constellation schema of Example 2.3 and Fig.6 is defined in

DMQL as follows.

define cube.., sales [time, item, branch, location]: rupees sold =

sum(sales in rupees),units sold = count(*)

define.., dimension time as (time_key, day, day of week, month, quarter, year)

define.., dimension item as (item_key, item name, brand_type, supplier_type)

define.., dimension branch as (branch_key, branch_name, branch_type)

define.., dimension location as (location_key, street, city, state, country)

define.., cube shipping [time, item, shipper, from_location, to_location]: rupees cost =

sum(cost in rupees), units shipped = count(*)

define.., dimension item as item in cube sales

define.., dimension shipper as (shipper_key, shipper_name, location as location in cube

sales, shipper_type)

define.., dimension from_location as location in cube sales

define.., dimension to_location as location in cube sales

A define cube statement is used to define data cubes for sales and shipping, corresponding to

the two fact tables of the schema of Example 2.3. Note that the time, item, and location dimensions

of the sales cube are shared with the shipping cube. This is indicated for the time dimension, for

example, as follows. Under the define cube statement for shipping, the statement ‘’define dimension

time as time in cube sales" is specified. Instead of having users or experts explicitly define data cube

dimensions, dimensions can be automatically generated or adjusted based on the examination of data

distributions. DMQL primitives for specifying such automatic generation or adjustments are also

possible.

Data Preparation and analyze a dyadic relation After the data set of relationship between enterprise and its direct customer questionnaire

gathering following R. Derrouiche et al., [35] Model’s in Fig.7. to analyze a dyadic relation and to

evaluate its performance, the attribute ranking algorithm using information gain based on ranker

search was calculated for the two types of relationships.



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Fig. 7: Model to analysis relations and to evaluate its performance

Sub-KPI impact results from the attribute ranking algorithm: These results are shown in Fig.8. In

addition, the questionnaire from R. Derrouiche et al., able to characterize collaborative relation

between two or more partners in a supply chain, evaluating their related performances

accordingly. The former level is the common perspective as follows: relation climate, relation

structure, IT- used and relation lifecycle and the later level consists of the perceived satisfaction of

the relation and its perceived effectiveness.

Fig. 8: The sub-KPI impact results from the attribute ranking algorithm using information gain,

based on ranker search

These represent the macro view of model. For example, the macro view of relation climate

has six micro views, and each micro view has also two sub-micro views.

Next, the data cleaning and input-output format following C&RT and K-Means structure was

conducted to prepare the learning data. Primary impact of each sub-KPI (i) from each relationship

type (j) was calculated from equation 2. Then weight definition was performed according to

equation 2.

(1)



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C&RT Decision Tree of predictive collaborative performance construction The simple additive weight of each sub macro view was constructed using the weight from

the results of primary impact from Fig.9. At this stage, the collaborative performance was also

calculated as the future response of C&RT model with 7 as the maximum tree depth. Then, Pearson

feature selection was applied to identify the significant inputs which have an effect on collaborative

performance score. The significant inputs are the inputs which pass the 95 percentage of confidence

interval (red line).

Preparation algorithm for computing the ranking of time series A simple pattern based approach has been used in this research work to compare the time

series data [36]. The ranking of time series is done through automated sorting of patterns. In

order to sort the time series values, the spread of each series is computed and compared with the

spread of all the series. Large variances suggest a very different development, while small

variances indicate a similar development pattern. Since the values for each series are very

different, it is not possible to compare the series values directly. In order to make the series

comparable, the series will be normalized, by dividing the individual values of the series by

series mean. Once the data is normalized, square of a sum of differences of individual values in

the time series with that of the overall mean vector values is computed, which results in scalar

values for each series. Ranking of these series of scalars will provide statistically valid ranks for

the time series. The algorithm for comp uting the ranking of time series is shown in Fig. 10.

Fig. 10: An algorithm for computing the ranking of time series

Preparation algorithm for computing the ranking of time series

A standard approach for model forecasting is to use techniques like ARIMA or using

neural networks. However several problems limit their usefulness when dealing with analysis in a

practical situation. Some of them are like –

• Simple models have proved to be effective in replicating complex models like ARIMA on

time series forecasting [37]

• Non-parametric models like Kernel regression though simple require human evolution

which limits the usage in a dynamic setting like interactive analytics.



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• Though non-parametric methods like neural networks show promising results their

computational complexity is prohibitive. The prediction of future value of a KPI is an

important function in analytics. In this research we adopt the models proposed by Toskos

and integrate with my analytical system. The equations used is, k-th moving average:

(2)

The details of the derivations and comparison of the effectiveness of these models with

standard as well as best ARIMA models are discussed in Toskos. For the sake of abbreviation the

proposed set of model using kth series are referred to as KMV (Kth series Moving average

Variants). Actual algorithm is given below in Fig. 11.

Fig. 11: Prediction with KMV model.

This analysis can be done in two modes: manual and automatic. In both the modes the

process remains the same, only the space in which the analysis is carried out will differ. In

manual mode, user will select the dimensions of interest. However in the automatic mode a pre

defined structure for hierarchical analysis is followed. The actual process consists of

• Selection of dimensional values and facts

• Forecasting the KPIs using KMV model

• Ranking the time series of predicted values

Collaborative Process Performance Management

Proposed Collaborative Key Performance Indicators In this Research, we first proposed collaborative Key Performance Indicators (cKPIs) which

could be used to measure collaboration of multiple manufacturing partners based on the SCOR

standard model. cKPIs were developed to consolidate important KPIs of individual partners. They

were calculated from the values of KPIs leveraging the collaborative processes. The SCOR model

provides several levels of performance metrics in supply chain, which are good candidates for

collaboration performance indicators. Also, we developed a modified sigmoid function to reflect and



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check the characteristics of Service Level Agreements (SLA), which are often contracted between

participating companies. To achieve the synthetic satisfaction of collaboration results, the modified

sigmoid function can partially overcome the limits of the desirability functions which are used to

combine multiple responses into one response from 0 to 1.

Collaboration Performance Indicators Wheelwright and Bowen presented SCM performance indicators including cost, quality,

delivery period, and flexibility. Min and Park introduced performance measurement of supply chain

and systemized the measures by using the SCOR model [38].

Desirability Function: Desirability functions convert satisfaction of measured values into values of 0

to 1. Kim and Lin proposed a non-linear desirability function based on an exponential function [38].

The desirability can be calculated from response variables as follows:

(3)

It is assumed that the response variables are classified into LTB (Lager-The-Better), STB

(Small-The-Better), and NTB (Normal-The-Best). To measure desirability of three types of response

variables, z value is first calculated by using the response value Y with the maximum, minimum, and

target response values, noted Ymax, Ymax, and T, respectively .

(4)

Unfortunately, it has difficulty in adopting the desirability function as a performance measure

of supply chain due to two reasons. First, typical performance indicators do not have Ymax, Ymin,

and T values, or the values are not nearly meaningful. Second, the desirability function cannot reflect

the criteria of performance indicators, which are often used in contracts with partners, so-called SLA.

From the reasons, we devised a new desirability function for the collaboration in Section 4.

Methodology used for Collaborative Process Performance Management The framework of collaborative process performance management collaboration process

monitoring and reporting, and collaborative performance indicator that combines KPIs of individual

companies. includes development of cKPIs, real-time process performance analysis. cKPI is the

Development of cKPIs: The product data is often shared in collaborative manufacturing

environment. However, performance indicators and their improvement are generally managed only

within individual companies. To overcome the limits, we propose the notion of cKPIs for

collaboration performance management. If all parties in the collaboration effectively measure and

manage the shared cKPIs in their manufacturing collaboration, they can continuously improve and

strengthen competitiveness of their collaboration for their common goals.

(1) Real-time collaboration process monitoring and reporting: Based on the derived cKPIs, real-time

collaboration can be monitored and reported to maintain their ongoing collaboration processes.

(2) Collaborative process performance analysis: It provides functions of process analysis to mutually

improve the performances of the partners in collaboration by analyzing the measured values of

cKPIs.



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Supply Chain Performance Management SCOR model provides a reference of supply chain processes and the metrics. It contains five

generic processes for supply chain (plan, source, make, deliver and return), and it also provides

structured performance indicators for each process. In the model, supply chain performances are

measured to balance a high level of performance indicators, which include reliability, flexibility and

responsiveness, cost, and asset. SCOR model designs the supply chain by adjusting overall goals of

supply chain and measures performance [35]. SCOR model suggests the process reference model

hierarchically Level 1 to 3. For example, Level 2 includes forty performance indicators. In this

research, we derived cKPIs of manufacturing collaboration processes from the performance

indicators of Level 2 in SCOR model version 8.

Table- 2: cKPIs derived from metrics in SCOR model

Table-3: cKPIs and Their Calculation



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Desirability Function of cKPI SLA describes the level of service quality. If the service provider does not satisfy the

agreement, penalty is imposed to the provider on the basis of SLA. In this research, a modified

sigmoid function is introduced to reflect sensitivity around critical value s, which is the criterion of

service level described in the agreement.

Logistic or Gompertz function is generally used to represent the sigmoid function as follows:

(5)

(6)

In this research, It is considered the logistic function because the function does not require

Ymax and Y min values and it can also transform the desirability to the values from 0 to 1. Focusing on

critical value s of SLA, developed a new desirability function to differentiate affection when is the

criteria over and criteria down.

Total Performance Satisfaction Measure

To measure performance of collaboration from the proposed cKPI, we gathered parameters of

performance indicators and the measured values of each partner. Table-5 shows the values of

Table -4: KPI and their values of partners in example collaboration

KPIs, which are used to calculate cKPI.

Table -5: cKPIs and the satisfaction of example collaboration



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Table-2 shows the progress of calculating the values of cKPIs and measuring the satisfaction

through interviews to experts. The values of cKPIs are calculated from those of KPIs in Table-4 by

using the equations in Table-2. About total performance satisfaction of manufacturing collaboration

using desirability function derived from section 4.1. Finally, the values of satisfaction functions of

cKPIs are calculated to obtain the satisfaction of the collaboration D by considering weights wi. The

values weights can be acquired by interviews to the experts.

(7)

For the example process, the satisfaction of manufacturing collaboration is calculated to 0.720.

RESULTS AND DISCUSSION

From Integrated (BiztoBiz) predictive collaboration performance evaluation

Here we tried to analyse the outcome of in macro level from C&RT Decision Tree model

and Performance Clustering based on K-Means Construction.

C&RT Decision Tree model performance analysis

The learning dataset of C&RT Decision Tree comes from the prepared dataset; the training

set makes up 80 percentage, performance prediction capacity of this model was expressed in terms

of the error of the predictive output. The mean absolute error of and testing set is 0.027; it can be

regarded as the error acceptance from domain experts assumptions.

C&RT Decision Tree model deployment On the practical deployment, the domain users tried on it as follows:

• Prepare their collaborative performance data according the input-output model format and

then feed it into the model.

• Put performance improvement scenarios and analyze the result in terms of real usage

feasibility and how to take advantage from the KPI sensitivity analysis, related to their

expected collaborative performance.

• Analyze the impact of sub-KPIs to their expected collaborative performance.

• Form the sub-KPI improvement planning based on the model result and their long term

strategy.

For instance, one of domain experts exercised his performance improvement road map, in

which It has been profound a research on the case study of BiztoBiz-SC. The assumption stated

most of the best relation types merely started in the maturity phase of the product life cycle.

Between the two partners, they take effort to develop the cooperation climate in the long-term

orientation with a very high participation. These come from the high degree of engagement and

commitment, compatibility and solidarity, power exerted and confidence. Besides, it can improve

satisfaction with their partner. To prove his assumption, the statement of this assumption was

converted to the estimated value of sub-KPIs and then put all of its values to the C&RT decision tree

model.

As a result, it is found that the predictive collaborative performance value is a very high

value, which is greater than 75 percent of collaborative performance from the main improvement

condition (engagement and commitment > 3.425, compatibility and solidarity > 3.084, power

exerted > 4.253 and confidence > 4.245); it is corresponding to his assumption.



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Performance Clustering based on K-Means Construction Before performing the K-Means algorithm, the number of clusters was set to three. The

percentage membership volume in each cluster was 42,14 and 55 percent.

Outcome from the ranking of time series module The trend i.e. time series can have some prominent patterns which are of interest to

business analysts. Some of them are like, Vary considerably over the past few periods, Increase

greatly, Drop drastically, Increase greatly and then drop drastically, Perform differently than the

total trend, A typical comparison of the time series is given in The following graph-2 is the

outcome of time series ranking module which ranks the time series with an adoptive algorithm for

the quarterly sales chosen for 5 years. the red colour represent the typical patterns of average trend,

where as the Series 1 to 6 represent the sales pattern from past five years from various quarters.

Graph-1: Typical pattern of trend in time series for sales

The following graph is the outcome of time series ranking module of quarterly unit sales

chosen for 5 years similar to the one explained above for variations in patterns. the red colour

represent average

Graph-2: Typical pattern of trend in time series for unit of sales

trend, where as the Series 1 to 6 represent the sales pattern from past five years from various

quarters.



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Conclusion Proposed Collaborative Key Performance Indicators The existing studies on performance management mainly focus on tasks of a single company

or outsourcing in viewpoint of a client company. Such performance measures cannot be adopted for

the collaborative work since performance indicators often have conflicts between service providers

and clients. As a result, we tried to propose a methodology of measuring the collaborative

performance and the satisfaction of manufacturing collaboration.

In this research, the SCOR model is used to derive cKPIs, which are calculated from the

KPIs of each partner. And, the desirability function of measuring satisfactions of cKPIs is devised

by combining the logistic function and the exponential function so that Ymax or Ymin of cKPI do not

need to be considered. Finally, a method of obtaining the satisfaction of collaboration is proposed

from the values of cKPIs. The proposed methodology of calculating the collaborative performances

and the satisfaction of collaboration can be utilized for the purpose of maintaining and improving the

collaboration which is performed by multiple partners.

CONCLUSION

In the present work, the integrated application between B to B supply chain performance

evaluation systems, data mining and multi-criteria decision attribute techniques, developing

predictive collaborative performance evaluation model and performance clustering model. After the

methodology implementation and deployment, the results prove the model advantage in terms of

long term planning based on expected performance. Moreover, this proposed model that allows

users to combine human perception and judgment with C&RT decision tree of predictive

collaborative performance related with their SC context. It results from the innovation decision

making, concerning the changing in terms of computational result and human instinct. There are

some certain limitations such as the users have to know a some data mining and multi-criteria

decision attribute techniques. This work is focused on a quantitative approach on how managers

consider the role of each sub-KPI and its impact on the long term collaborative performance.

To illustrate it on dashboard or simplified graphics is another way to communicate among the

invokers from the different units. Moreover, some of uncertainty factors from the SC environment or

even management should be covered using Fuzzy MCDA to be handled in the first step before

predictive collaborative performance evaluation model construction. On the other hand, the PM

framework might be added of some measurement metrics from the SCOR model.

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