This document discusses using data mining techniques to improve product portfolio management in the food industry. It analyzes dependency modeling, change and deviation detection, and classification techniques. Dependency modeling and classification were found to complement product portfolio management by providing insights into product interdependencies and allowing data-driven product classifications. Change and deviation detection was not useful as it forecasts future data based on historical trends. The study proposes using these techniques alongside traditional metrics-based approaches to achieve a more holistic view of products and make more informed decisions about what to include in a company's portfolio.
Information Management In Pharmaceutical IndustryFrank Wang
Pharmaceutical Industry Information Management Opportunities and Challenges in Research, Development, Clinical, Sales, Marketing, Managed Markets, Manufacturing, Supply Chain and Distribution
Evolving Operational Business Model in Pharmaceutical IndustrySurya Chitra,PhD MBA
The pharmaceutical industry is changing its business and operational models due to various pressures. The current model of vertical integration from research to pharmacy is shifting to a more fragmented model. Key drivers include rising costs, price pressures, increased regulation, and patent expirations. The industry must address these issues to sustain growth and profits. Alternative business strategies are needed due to instability in the current blockbuster drug model, which relies on a few highly profitable drugs to offset research and development costs.
The document discusses opportunities to improve healthcare supply chain management. It notes that supply costs represent up to 40% of hospital expenses. While inventory management has traditionally focused on tracking physical supplies, supply chain management (SCM) aims to optimize the entire process of procuring and delivering supplies from vendors to patients. Capturing the full benefits of SCM requires greater collaboration across groups and more complete data on total product costs. Initial focus areas for hospitals include pricing and contract management, which often provide quick savings through issues like multiple vendors, expired contracts, and price validation.
This document describes a strategic analytics methodology (SAM) that was developed to better integrate data mining and analytics with business decision making. It summarizes a case study where SAM was applied to a telecommunications customer retention project that had previously used the CRISP-DM methodology. The case study found that CRISP-DM provided limited integration with the business, failing to meet objectives or timelines. SAM embedded data mining within the business decision making process to provide more strategic insights and improved results for the retention project. The document outlines the key phases and steps of SAM and compares the outcomes of applying CRISP-DM versus SAM on the case study project.
Strategic management involves determining an organization's long-term goals and plans. It includes analyzing the internal and external environment, formulating strategies, implementing strategies, and evaluating performance. Key aspects of strategic management include identifying the mission and objectives, conducting a SWOT analysis, developing corporate and business-level strategies around areas like growth, stability, and competitive advantage. Current issues involve e-business strategies, customer service strategies, and innovation strategies. Strategic flexibility is the ability to adapt strategies in response to changes in the external environment.
Impact of Inventory Management on the Effectiveness of Supply Chain Managemen...paperpublications3
Abstract: The main objective of the study was to assess factors affecting the effectiveness of supply chain management practices in Kenyan public sector with, specific reference to the Ministry of Finance. The study’s specific objective being; to establish the effect of inventory management on the effectiveness of supply chain management practices. The study adopted a descriptive case research design and the study population comprised of 120 management staff working at the Ministry of finances’ procurement, finance and administration departments. A stratified random sampling technique was employed to select a sample size of 60 respondents. Questionnaires were used as the main data collection instrument. Descriptive statistics data analysis method was applied to analyze numerical data gathered using closed ended questions aided by Statistical Package for Social Sciences (SPSS). Pearson correlation was carried out to establish the relationship between the research variables. Inventory management was also found to have a strong positive correlation with effectiveness of SCM practices (r = 0.915). The study recommend implementation of EOQ inventory management methods for and IT based SCM systems.
Business Development & Licensing concepts, methods and tools. From theory to practice.
Application to the pharmaceutical sector. Guidelines and recommendations
Information Management In Pharmaceutical IndustryFrank Wang
Pharmaceutical Industry Information Management Opportunities and Challenges in Research, Development, Clinical, Sales, Marketing, Managed Markets, Manufacturing, Supply Chain and Distribution
Evolving Operational Business Model in Pharmaceutical IndustrySurya Chitra,PhD MBA
The pharmaceutical industry is changing its business and operational models due to various pressures. The current model of vertical integration from research to pharmacy is shifting to a more fragmented model. Key drivers include rising costs, price pressures, increased regulation, and patent expirations. The industry must address these issues to sustain growth and profits. Alternative business strategies are needed due to instability in the current blockbuster drug model, which relies on a few highly profitable drugs to offset research and development costs.
The document discusses opportunities to improve healthcare supply chain management. It notes that supply costs represent up to 40% of hospital expenses. While inventory management has traditionally focused on tracking physical supplies, supply chain management (SCM) aims to optimize the entire process of procuring and delivering supplies from vendors to patients. Capturing the full benefits of SCM requires greater collaboration across groups and more complete data on total product costs. Initial focus areas for hospitals include pricing and contract management, which often provide quick savings through issues like multiple vendors, expired contracts, and price validation.
This document describes a strategic analytics methodology (SAM) that was developed to better integrate data mining and analytics with business decision making. It summarizes a case study where SAM was applied to a telecommunications customer retention project that had previously used the CRISP-DM methodology. The case study found that CRISP-DM provided limited integration with the business, failing to meet objectives or timelines. SAM embedded data mining within the business decision making process to provide more strategic insights and improved results for the retention project. The document outlines the key phases and steps of SAM and compares the outcomes of applying CRISP-DM versus SAM on the case study project.
Strategic management involves determining an organization's long-term goals and plans. It includes analyzing the internal and external environment, formulating strategies, implementing strategies, and evaluating performance. Key aspects of strategic management include identifying the mission and objectives, conducting a SWOT analysis, developing corporate and business-level strategies around areas like growth, stability, and competitive advantage. Current issues involve e-business strategies, customer service strategies, and innovation strategies. Strategic flexibility is the ability to adapt strategies in response to changes in the external environment.
Impact of Inventory Management on the Effectiveness of Supply Chain Managemen...paperpublications3
Abstract: The main objective of the study was to assess factors affecting the effectiveness of supply chain management practices in Kenyan public sector with, specific reference to the Ministry of Finance. The study’s specific objective being; to establish the effect of inventory management on the effectiveness of supply chain management practices. The study adopted a descriptive case research design and the study population comprised of 120 management staff working at the Ministry of finances’ procurement, finance and administration departments. A stratified random sampling technique was employed to select a sample size of 60 respondents. Questionnaires were used as the main data collection instrument. Descriptive statistics data analysis method was applied to analyze numerical data gathered using closed ended questions aided by Statistical Package for Social Sciences (SPSS). Pearson correlation was carried out to establish the relationship between the research variables. Inventory management was also found to have a strong positive correlation with effectiveness of SCM practices (r = 0.915). The study recommend implementation of EOQ inventory management methods for and IT based SCM systems.
Business Development & Licensing concepts, methods and tools. From theory to practice.
Application to the pharmaceutical sector. Guidelines and recommendations
This document summarizes research on the alignment between business strategy and supply management strategy. The research found that while companies rated supply management strategies as highly important, the implementation of these strategies was often lacking. The strategies considered most important and implemented tended to be "core" supply strategies, while "integrative" strategies requiring collaboration were seen as less important and implemented to a lesser degree. However, these integrative strategies may better support changing business needs like innovation and sustainability. The research thus suggests companies focus on implementing a broader range of supply management strategies, especially integrative ones, to close the gap between the importance of strategies and their implementation.
This document discusses how hospital supply chains can transform from basic models focused on operations to more advanced models that balance costs, efficiency, and quality of care. It identifies three key enablers of transformation: collaborative governance structures involving clinicians, streamlined supply chain processes, and integrated IT systems. A case study highlights how one healthcare system achieved cost savings through strategic sourcing, a distribution center, and technology like barcoding medications.
This position paper answer the following questions: Why is creativity so important? - How to craft a creative strategy? - How to build a creativity-driven organization?
THE STUDY OF PURCHASE INTENTION FOR MEN'S FACIAL CARE PRODUCTS WITH K-NEAREST...ijcsit
Inventory management was a major issue for all the industries. The supplied of products to customers required the readiness of the inventory. This allowed rapid delivery and reduced waiting time for customers so that companies could profit from it. Any stock out or insufficiency would lead to loss of customers because their needs cannot be met. This would hurt firm profitability and market competitiveness. Inventory control was critical to retain liquidity and avoid overstocking. This was also the key to firm's survival and sustainability. To ensure an appropriate level of inventory, it was necessary to manage the inventory levels with sales forecast on an on-going basis. This paper tried to find out its inventory control in order to assisted Company T to improve its inventory control. Firstly, the products offered by Company T are classified into groups. The R programming language was then used to stimulate and forecast future sales of different products. Different techniques were applied to manage the inventory levels according to the results of categorizations and forecasts.; 3.Consolidation of all the product items and grouping them into activity-based classifications; 4.Simulation and forecasting of future sales according to the categorization results; 5. Formulation of different controlled techniques based on the simulations and forecasts, and application of these methods to inventory management.
7 qct optmisation in new product development detailed study on inter-links ...prjpublications
This document summarizes an article about optimizing quality, cost, and time (QCT) in new product development. It discusses how these three objectives are interrelated and often require trade-offs. It proposes using a systematic, analytical approach to quantify the interrelationships between objectives to help with trade-off decision making. This would assess how movement in one objective impacts others. For example, reducing development time could increase costs. The document also discusses using profitability metrics like profitability index, net present value, and payback period to evaluate trade-offs based on long-term profitability. Graphically representing cash flows can help assess trade-offs subject to the specific project's profitability targets.
The study of scope and implementation of lean aspectsprjpublications
The document discusses the scope and implementation of lean aspects in the pharmaceutical industry. It begins with an introduction to lean strategies and their historical use in eliminating waste and improving efficiency. While lean has been successfully adopted in other industries, the pharmaceutical industry has been slow to implement it. The study aims to identify lean management principles that can be applied in the pharmaceutical manufacturing environment to improve quality and productivity while reducing costs. It also discusses conducting surveys of pharmaceutical companies to understand their current quality systems and openness to lean implementation. The goal is to determine how lean principles can enhance processes to achieve very high productivity, short lead times, and exceptional product quality.
Making Key Decisions in New Product Planning When “Perfect” Information is No...Anthony Russell
Presentation given at New Product Planning Summit 2021.
Learning Objectives:
* Review the types of decisions typically made in New Product Planning
* Discuss the nature of information available to support decision-making in New Product Planning
* Review the impact and context of decision-making in New Product Planning
* Review potential approaches to assist in New Product Planning decision-making
An Analytic Network Process Modeling to Assess Technological Innovation Capab...drboon
To handle swift changes in global environment, Technological Innovation Capabilities (TICs) is one crucial and unique strategy to increase firms’ competitiveness. This research proposed a systematic framework of TICs assessment by employing Analytic Network Process (ANP) method for solving the complicate decision-making and assessing the interrelationship among various evaluation factors, whereas the relative important weight data were provided by industrial experts based on pair-wise comparison. With the novel TIC assessment model, high-level managers could easily gain management information to rationalizes the decision-making process based on the most important criteria which affect the firms’ competitive advantages and the highest priority factors which were needed to be handled. The last section also displayed the application of TICs assessment on three Thai automotive parts firms, as case study.
- The document discusses a research project analyzing the use and effectiveness of Total Quality Management (TQM) approaches in the German food industry.
- A survey of 442 food industry companies in Germany was conducted to assess the current status of TQM implementation based on the EFQM Excellence Model, which defines nine key areas.
- The results showed that companies have successfully implemented some TQM requirements like reducing environmental impacts and ensuring organizational structures. However, other areas like collecting employee feedback and communicating societal impacts need improvement.
- Statistical analysis found a positive correlation between TQM implementation and long-term business success, though companies ranked the impact of different TQM areas differently than the statistical results.
ROLE OF SUPPLIER MANAGEMENT PRACTICES IN OPTIMIZATION OF OPERATIONAL PERFORM...muo charles
ROLE OF SUPPLIER MANAGEMENT PRACTICES IN OPTIMIZATION OF OPERATIONAL PERFORMANCE IN TELECOMMUNICATION SERVICE INDUSTRY IN KENYA. A CASE OF SAFARICOM LIMITED KENYA
Analysis of the global pharmaceutical market (2017 - 2023) and of the pharma companies strategic options. Proposition of concepts, methods and tools to craft corporate, business and operational strategies
Lifecycle Management in the Pharmaceutical IndustryAnthony Russell
This document discusses lifecycle management strategies in the pharmaceutical industry. It begins by outlining the key drivers for effective lifecycle management, including the high costs of drug development and need to extend patent protection. Several lifecycle management strategies are then described, such as developing new indications, formulations, delivery methods, or integrating digital health tools. The document emphasizes that lifecycle management planning should begin early and consider factors such as clinical feasibility, regulatory pathways, intellectual property and commercial impacts when selecting strategies.
Building a Business Case for New Product Planning in a Small Company EnvironmentAnthony Russell
Presented at the 2nd New Product Planning Summit by ExL Events in Boston (Dec 8, 2017). An overview of why commercial strategy is needed early in product development. As pressures continue to mount in drug development (crowded markets, payer and access issues, etc.), it is becoming critical to focus on the early stages of drug development. Working early with research and development teams to evaluate the commercial viability of programs will benefit companies of all sizes to maximize their portfolio of therapeutic candidates.
How to Work Effectively with Research Teams in New Product PlanningAnthony Russell
Presented at the 3rd New Product Planning Summit. The presentation was designed to help professionals in New Product Planning to present a case for why commercial strategy input is needed early in the process of developing new therapeutics. The presentation also includes suggested approaches and tools to help with effective engagement with Research teams.
This document summarizes the key findings of a 2005 survey on healthcare supply chain management conducted by HFMA. The survey identified areas of high opportunity to reduce costs, including physician/clinical buy-in, IT investments, and value analysis. Respondents estimated they could reduce supply costs by an additional 2.5% annually by focusing on standardizing items and improving purchasing controls and physician involvement. While most hospitals have standardization initiatives, physician awareness remains low. The document discusses strategies for improving physician buy-in, such as ensuring transparency and a process for evaluating new products.
New Product Planning Strategies to Ensure Organizational Robustness for LaunchAnthony Russell
- New product planning strategies are important for small to medium sized biopharma companies to ensure organizational robustness for product launches.
- Early commercial planning can impact a product's launch success by clearly defining its value proposition and positioning relative to competitors.
- A framework for evaluating new targets and indications involves assessing factors like clinical development feasibility, regulatory pathways, market potential, and risks. Strategic segmentation of patient populations also guides efficient planning.
- Lifecycle management strategies like pursuing new indications can extend revenue and provide competitive barriers to improve return on investment.
Presentation of a methodology to optimize the management of mature brands in the pharma industry. These recommendations are based on desk research, benchmarking study and Smart Pharma Consulting expertise and experience
How and When to Kill a Program in New Product PlanningAnthony Russell
Presented at the 4th New Product Planning Summit in Boston (Dec 2 -3 , 2019). Presentation covers why weak programs should be cut from pharmaceutical and biotech pipelines, what defines a "weak" program, and describes objective methods to evaluate programs to help prioritize assets.
Selection of Best Alternative in Manufacturing and Service Sector Using Multi...csandit
Modern manufacturing organizations tend to face versatile challenges due to globalization,
modern lifestyle trends and rapid market requirements from both locally and globally placed
competitors. The organizations faces high stress from dual perspective namely enhancement in
science and technology and development of modern strategies. In such an instance,
organizations were in a need of using an effective decision making tool that chooses out optimal
alternative that reduces time, complexity and highly simplified. This paper explores a usage of
new multi criteria decision making tool known as MOORA for selecting the best alternatives by
examining various case study. The study was covered up in two fold manner by comparing
MOORA with other MCDM and MADM approaches to identify its advantage for selecting
optimal alternative, followed by highlighting the scope and gap of using MOORA approach.
Examination on various case study reveals an existence of huge scope in using MOORA for
numerous manufacturing and service applications.
which supply chain strategies can guarantee higher manufacturer’s operational...INFOGAIN PUBLICATION
Due to the fact that scientists and practitioners alike have interested on the leveraging manufacturing companies’ operational performance, this research examined which supply chain strategies promise manufacturers higher operational performance. Later on, we clarified whether suitable resources can play an important role in the mentioned causal relationshipsas a moderator and improve the impact of the strategies on operational performance. This study is a descriptive-exploratory research in which primary data was collected from 80 Malaysian manufacturing companies. Bivariate Correlation and Multiple Regression in SPSS was applied for analyzing data. Output showed that many suppliers, few suppliers, and keiretsu network strategies enable manufacturers to achieve satisfactory level of operational performance; but, vertical integration. More importantly, suitable resources can leverage the effect of just vertical integration strategy on operational performance.
An Assessment of Project Portfolio Management Techniques on Product and Servi...iosrjce
The crises of product and service innovation in most organisations due to global competition and
the need for scientific research in the project portfolio management discipline were factors that motivated this
research. The purpose of this study is to investigate how project portfolio management(ppm) contributes to
product and service innovation. A questionnaire was developed to gather data to compare the PPM methods
used, PPM performance and resulting new product success measures in sixty Nigeria organisations in a diverse
range of service and manufacturing industries. The study findings indicated that PPM practices have a greater
impact in the new product and services success rate. Also, business strategy method result in better alignment
of the projects in the portfolio. This conclusion is supported by the 0.630 Pearson correlations at 0.000
significance between percentage of successful products and PPM performance level. The results reveal that for
better innovation outcomes, management should place a priority on developing and improving PPM.
Application of voc translationtools a case studyiaemedu
The document discusses tools for translating the voice of the customer (VOC) into product design. It provides context on high new product failure rates and the importance of understanding customer needs early in the development process. The document then summarizes several VOC translation tools, including Quality Function Deployment (QFD) for translating customer wants into technical specifications, Pugh Matrix for comparing design concepts, Kano analysis for understanding customer preferences, and conjoint analysis for determining ideal product attributes based on customer tradeoffs.
This document summarizes research on the alignment between business strategy and supply management strategy. The research found that while companies rated supply management strategies as highly important, the implementation of these strategies was often lacking. The strategies considered most important and implemented tended to be "core" supply strategies, while "integrative" strategies requiring collaboration were seen as less important and implemented to a lesser degree. However, these integrative strategies may better support changing business needs like innovation and sustainability. The research thus suggests companies focus on implementing a broader range of supply management strategies, especially integrative ones, to close the gap between the importance of strategies and their implementation.
This document discusses how hospital supply chains can transform from basic models focused on operations to more advanced models that balance costs, efficiency, and quality of care. It identifies three key enablers of transformation: collaborative governance structures involving clinicians, streamlined supply chain processes, and integrated IT systems. A case study highlights how one healthcare system achieved cost savings through strategic sourcing, a distribution center, and technology like barcoding medications.
This position paper answer the following questions: Why is creativity so important? - How to craft a creative strategy? - How to build a creativity-driven organization?
THE STUDY OF PURCHASE INTENTION FOR MEN'S FACIAL CARE PRODUCTS WITH K-NEAREST...ijcsit
Inventory management was a major issue for all the industries. The supplied of products to customers required the readiness of the inventory. This allowed rapid delivery and reduced waiting time for customers so that companies could profit from it. Any stock out or insufficiency would lead to loss of customers because their needs cannot be met. This would hurt firm profitability and market competitiveness. Inventory control was critical to retain liquidity and avoid overstocking. This was also the key to firm's survival and sustainability. To ensure an appropriate level of inventory, it was necessary to manage the inventory levels with sales forecast on an on-going basis. This paper tried to find out its inventory control in order to assisted Company T to improve its inventory control. Firstly, the products offered by Company T are classified into groups. The R programming language was then used to stimulate and forecast future sales of different products. Different techniques were applied to manage the inventory levels according to the results of categorizations and forecasts.; 3.Consolidation of all the product items and grouping them into activity-based classifications; 4.Simulation and forecasting of future sales according to the categorization results; 5. Formulation of different controlled techniques based on the simulations and forecasts, and application of these methods to inventory management.
7 qct optmisation in new product development detailed study on inter-links ...prjpublications
This document summarizes an article about optimizing quality, cost, and time (QCT) in new product development. It discusses how these three objectives are interrelated and often require trade-offs. It proposes using a systematic, analytical approach to quantify the interrelationships between objectives to help with trade-off decision making. This would assess how movement in one objective impacts others. For example, reducing development time could increase costs. The document also discusses using profitability metrics like profitability index, net present value, and payback period to evaluate trade-offs based on long-term profitability. Graphically representing cash flows can help assess trade-offs subject to the specific project's profitability targets.
The study of scope and implementation of lean aspectsprjpublications
The document discusses the scope and implementation of lean aspects in the pharmaceutical industry. It begins with an introduction to lean strategies and their historical use in eliminating waste and improving efficiency. While lean has been successfully adopted in other industries, the pharmaceutical industry has been slow to implement it. The study aims to identify lean management principles that can be applied in the pharmaceutical manufacturing environment to improve quality and productivity while reducing costs. It also discusses conducting surveys of pharmaceutical companies to understand their current quality systems and openness to lean implementation. The goal is to determine how lean principles can enhance processes to achieve very high productivity, short lead times, and exceptional product quality.
Making Key Decisions in New Product Planning When “Perfect” Information is No...Anthony Russell
Presentation given at New Product Planning Summit 2021.
Learning Objectives:
* Review the types of decisions typically made in New Product Planning
* Discuss the nature of information available to support decision-making in New Product Planning
* Review the impact and context of decision-making in New Product Planning
* Review potential approaches to assist in New Product Planning decision-making
An Analytic Network Process Modeling to Assess Technological Innovation Capab...drboon
To handle swift changes in global environment, Technological Innovation Capabilities (TICs) is one crucial and unique strategy to increase firms’ competitiveness. This research proposed a systematic framework of TICs assessment by employing Analytic Network Process (ANP) method for solving the complicate decision-making and assessing the interrelationship among various evaluation factors, whereas the relative important weight data were provided by industrial experts based on pair-wise comparison. With the novel TIC assessment model, high-level managers could easily gain management information to rationalizes the decision-making process based on the most important criteria which affect the firms’ competitive advantages and the highest priority factors which were needed to be handled. The last section also displayed the application of TICs assessment on three Thai automotive parts firms, as case study.
- The document discusses a research project analyzing the use and effectiveness of Total Quality Management (TQM) approaches in the German food industry.
- A survey of 442 food industry companies in Germany was conducted to assess the current status of TQM implementation based on the EFQM Excellence Model, which defines nine key areas.
- The results showed that companies have successfully implemented some TQM requirements like reducing environmental impacts and ensuring organizational structures. However, other areas like collecting employee feedback and communicating societal impacts need improvement.
- Statistical analysis found a positive correlation between TQM implementation and long-term business success, though companies ranked the impact of different TQM areas differently than the statistical results.
ROLE OF SUPPLIER MANAGEMENT PRACTICES IN OPTIMIZATION OF OPERATIONAL PERFORM...muo charles
ROLE OF SUPPLIER MANAGEMENT PRACTICES IN OPTIMIZATION OF OPERATIONAL PERFORMANCE IN TELECOMMUNICATION SERVICE INDUSTRY IN KENYA. A CASE OF SAFARICOM LIMITED KENYA
Analysis of the global pharmaceutical market (2017 - 2023) and of the pharma companies strategic options. Proposition of concepts, methods and tools to craft corporate, business and operational strategies
Lifecycle Management in the Pharmaceutical IndustryAnthony Russell
This document discusses lifecycle management strategies in the pharmaceutical industry. It begins by outlining the key drivers for effective lifecycle management, including the high costs of drug development and need to extend patent protection. Several lifecycle management strategies are then described, such as developing new indications, formulations, delivery methods, or integrating digital health tools. The document emphasizes that lifecycle management planning should begin early and consider factors such as clinical feasibility, regulatory pathways, intellectual property and commercial impacts when selecting strategies.
Building a Business Case for New Product Planning in a Small Company EnvironmentAnthony Russell
Presented at the 2nd New Product Planning Summit by ExL Events in Boston (Dec 8, 2017). An overview of why commercial strategy is needed early in product development. As pressures continue to mount in drug development (crowded markets, payer and access issues, etc.), it is becoming critical to focus on the early stages of drug development. Working early with research and development teams to evaluate the commercial viability of programs will benefit companies of all sizes to maximize their portfolio of therapeutic candidates.
How to Work Effectively with Research Teams in New Product PlanningAnthony Russell
Presented at the 3rd New Product Planning Summit. The presentation was designed to help professionals in New Product Planning to present a case for why commercial strategy input is needed early in the process of developing new therapeutics. The presentation also includes suggested approaches and tools to help with effective engagement with Research teams.
This document summarizes the key findings of a 2005 survey on healthcare supply chain management conducted by HFMA. The survey identified areas of high opportunity to reduce costs, including physician/clinical buy-in, IT investments, and value analysis. Respondents estimated they could reduce supply costs by an additional 2.5% annually by focusing on standardizing items and improving purchasing controls and physician involvement. While most hospitals have standardization initiatives, physician awareness remains low. The document discusses strategies for improving physician buy-in, such as ensuring transparency and a process for evaluating new products.
New Product Planning Strategies to Ensure Organizational Robustness for LaunchAnthony Russell
- New product planning strategies are important for small to medium sized biopharma companies to ensure organizational robustness for product launches.
- Early commercial planning can impact a product's launch success by clearly defining its value proposition and positioning relative to competitors.
- A framework for evaluating new targets and indications involves assessing factors like clinical development feasibility, regulatory pathways, market potential, and risks. Strategic segmentation of patient populations also guides efficient planning.
- Lifecycle management strategies like pursuing new indications can extend revenue and provide competitive barriers to improve return on investment.
Presentation of a methodology to optimize the management of mature brands in the pharma industry. These recommendations are based on desk research, benchmarking study and Smart Pharma Consulting expertise and experience
How and When to Kill a Program in New Product PlanningAnthony Russell
Presented at the 4th New Product Planning Summit in Boston (Dec 2 -3 , 2019). Presentation covers why weak programs should be cut from pharmaceutical and biotech pipelines, what defines a "weak" program, and describes objective methods to evaluate programs to help prioritize assets.
Selection of Best Alternative in Manufacturing and Service Sector Using Multi...csandit
Modern manufacturing organizations tend to face versatile challenges due to globalization,
modern lifestyle trends and rapid market requirements from both locally and globally placed
competitors. The organizations faces high stress from dual perspective namely enhancement in
science and technology and development of modern strategies. In such an instance,
organizations were in a need of using an effective decision making tool that chooses out optimal
alternative that reduces time, complexity and highly simplified. This paper explores a usage of
new multi criteria decision making tool known as MOORA for selecting the best alternatives by
examining various case study. The study was covered up in two fold manner by comparing
MOORA with other MCDM and MADM approaches to identify its advantage for selecting
optimal alternative, followed by highlighting the scope and gap of using MOORA approach.
Examination on various case study reveals an existence of huge scope in using MOORA for
numerous manufacturing and service applications.
which supply chain strategies can guarantee higher manufacturer’s operational...INFOGAIN PUBLICATION
Due to the fact that scientists and practitioners alike have interested on the leveraging manufacturing companies’ operational performance, this research examined which supply chain strategies promise manufacturers higher operational performance. Later on, we clarified whether suitable resources can play an important role in the mentioned causal relationshipsas a moderator and improve the impact of the strategies on operational performance. This study is a descriptive-exploratory research in which primary data was collected from 80 Malaysian manufacturing companies. Bivariate Correlation and Multiple Regression in SPSS was applied for analyzing data. Output showed that many suppliers, few suppliers, and keiretsu network strategies enable manufacturers to achieve satisfactory level of operational performance; but, vertical integration. More importantly, suitable resources can leverage the effect of just vertical integration strategy on operational performance.
An Assessment of Project Portfolio Management Techniques on Product and Servi...iosrjce
The crises of product and service innovation in most organisations due to global competition and
the need for scientific research in the project portfolio management discipline were factors that motivated this
research. The purpose of this study is to investigate how project portfolio management(ppm) contributes to
product and service innovation. A questionnaire was developed to gather data to compare the PPM methods
used, PPM performance and resulting new product success measures in sixty Nigeria organisations in a diverse
range of service and manufacturing industries. The study findings indicated that PPM practices have a greater
impact in the new product and services success rate. Also, business strategy method result in better alignment
of the projects in the portfolio. This conclusion is supported by the 0.630 Pearson correlations at 0.000
significance between percentage of successful products and PPM performance level. The results reveal that for
better innovation outcomes, management should place a priority on developing and improving PPM.
Application of voc translationtools a case studyiaemedu
The document discusses tools for translating the voice of the customer (VOC) into product design. It provides context on high new product failure rates and the importance of understanding customer needs early in the development process. The document then summarizes several VOC translation tools, including Quality Function Deployment (QFD) for translating customer wants into technical specifications, Pugh Matrix for comparing design concepts, Kano analysis for understanding customer preferences, and conjoint analysis for determining ideal product attributes based on customer tradeoffs.
Application of voc translationtools a case studyiaemedu
This document provides an overview of tools that can be used to translate customer needs and preferences into product design. It discusses conjoint analysis, which measures customer preferences to help simulate customer reactions to new products. The document also mentions other voice of the customer tools like Kano analysis, Pugh matrix, quality function deployment that can be used in the new product development process to help bridge the gap between marketing and design. It emphasizes the importance of understanding customer desires early in development through market research to improve the success rate of new products.
Competitive Intelligence Analysis Tools For Economic DevelopmemtIntelegia Group
This document provides an overview of 9 competitive intelligence analysis tools: SWOT analysis, TOWS analysis, Boston Consulting Group matrix, competitor profile, GE McKinsey screen matrix, STEEP analysis, Porter's five forces model, product life cycle analysis, and SPACE matrix. For each tool, a brief description is given of its objective and the types of information needed to conduct the analysis. Tips are provided at the end on applying the tools effectively and developing competitive intelligence skills.
A New Model For Balanced Score Cards (BSC)Amber Ford
This document proposes a new 6-perspective balanced scorecard model that adds social/environmental, risk
management, and employee perspectives. It reviews literature on balanced scorecards including traditional
models with financial, customer, internal processes, and learning/innovation perspectives. Kaplan and Norton's
original balanced scorecard model is described. Benefits of balanced scorecards in communicating strategy and
aligning performance measures are outlined.
Here is a revised version of the proposal letter with minor edits:
September 22, 2003
To: Leslie Bickford
From: Diana Ferry DF
RE: Proposal for Final Project
I am writing to request that you accept my topic for the Writing 465 final project. I believe this project has the potential to help alleviate the current parking problem at Winthrop University. Both Jack Allen from Campus Police and Walter Hardin, Associate Vice President for Facilities Management, have expressed interest in my proposed research.
BACKGROUND
Winthrop University is a small public university with an undergraduate enrollment of approximately 5,065 students in the fall of 2002. While the university has a well-organized parking system, it is no
Assignment 3 Case StudyE-Business Strategy and Models in B.docxbraycarissa250
Assignment 3: Case Study
E-Business Strategy and Models in Banks: Case of Citibank
Bank is an institution that deals with money as well as credit. It accepts deposits from the public, makes funds available to those who need then and helps in remittance of money from one place to another (Macesich, George, 2000, p-42). Modern banks today perform a wide range of functions that makes it difficult to give an apt and precise definition of it. One of the famous economists, Crowther had said, a bank “collects money from those who have it to spare or who are saving it out of their incomes, and lends this money to those who require it”. In short, the term bank in modern times refers to an institution that deals with money i.e. accepts deposits and advances loans; has the ability to create credit which basically implies expanding its liabilities as a multiple of its reserves; creates demand deposits and it is a commercial institution that aims at securing profits.
Citibank is a subsidiary of Citigroup. Citibank was founded as City Bank of New York in the year 1918. According to the latest statistics, it is now the third largest bank holding company in the United States by the total assets after Bank of America and JP Morgan Chase. The bank has its retail banking operations spread over more than 100 countries and territories around the world (Harold, Cleveland & Huertas, 1985). Apart from the standard banking transactions, Citibank offers credit cards, insurance and other investment products. Their online services have earned them appreciation from every nook and corner, making them the most successful in the field. The 15 million online users bear testimony to the stated fact. The key people involved in the management of the bank are: Vikram Pandit (CEO), John Gerspach (CFO), Douglas Peterson (COO) and Willliam R. Rhodes, the Chairman.
Strategy literally means the way an action is planned to achieve the desired results. Every company has certain aims that it hopes to conquer. It has a vivid description of what it desires to achieve. The vision statement that company has is an idealized picture which inspires it, energizes its efforts towards directing its actions towards the expected goals (Hambrick and Chen, 2007, p 935-955). Strategic Decision Making, in context of a firm or an organization, is the framing of long term plan of action that aims at resulting in success and profits for the products and services marketed by the company, for instance (Triantaphyllou, 2000, p 320). Strategic decision making is important to outperform the various other competitors in the market. The process of determining appropriate courses of action for achieving organizational objectives and thereby accomplishing organizational purpose is known as Strategy formulation. In today’s era of cut-throat competition in the business environment budget-oriented planning or forecast-based planning methods are insufficient for a large corporation to survive and prosper. The firm ...
This document discusses production and operations management (POM). It defines POM as the management of direct resources, also called the 5 Ps - people, plant, parts, processes, and planning & control systems. POM lies at the heart of business activities and its ultimate objective is to produce a specified product on schedule at minimum cost. POM decisions are classified as strategic, operating, and control decisions. The document also discusses productivity measurement, factors affecting productivity, and the relationship between operations and marketing.
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INTRODUCTION
The increasing discussion about rising healthcare cost is fuelled by reports that General Motors paid more for healthcare than for steel per vehicle in 2004,1 and Starbucks paid more for health insurance than for coffee in 2005.2 The continuing rise in development costs for drugs has increased pressure on R&D organisations to contribute to higher efficiency in the overall process of coming up with new drugs.
In the last few years the industry has made significant efforts to address these challenges3 and to increase the productivity of the drug development process. Some of the initiatives have without doubt led to considerable improvements. Examples are the earlier determination of a drug's toxicology profile and early tests to investigate the suitability of a new drug candidate for oral administration or once a day dosing. The question is no longer how good we are in what we are doing but whether we are doing the right things. Further improvements of the overall process should shift from attempts of enhancing effectiveness to a greater emphasis on the efficiency of the processes applied.
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Value-driven project and portfolio management implies quantitative financial and risk analysis of individual projects and overall portfolios. Such analyses elucidate options for improving the value and risk structure of individual projects on the one hand and therapeutic areas or overall corporate portfolios on the other hand. They are applicable and relevant to companies of any size. Value-driven project and portfolio management is a methodology enabling the alignment of project decisions with corporate strategy and defined business objectives.
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This document provides a summary and analysis of IBM PowerNet's business strategy of partnering with small independent software vendors (ISVs) to develop and sell software and hardware solutions. The strategy targets "white space" customers who have not purchased from IBM in the past 3 years. While revenue increased steadily until 2009, it declined that year due in part to the economic downturn and lack of marketing materials. IBM PowerNet produced new marketing materials, aiming to continue the strategy of engaging new customers and ISVs. The document evaluates whether this strategy is sustainable by analyzing academic literature on strategic capabilities, value-added business partnerships, external market analysis, threats to sustainability, and marketing strategies. It assesses factors like pricing, relationships, value creation
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2. decision whether to keep or to remove a product from the portfolio a closer look into
product interdependencies is desirable. Hence, the aim of this research is to find
patterns by using Data Mining (DM)-techniques (Agrawal, Imieliski, & Swami, 1993;
Kotsiantis & Kanellopoulos, 2006). The main research question can be formulated as
follows:
“To what extent can Business Intelligence technologies such as Data Mining
techniques contribute to Product Portfolio Management in the food industry?”
By selecting DM-techniques based on literature and evaluating those selected DM-
techniques by applying them to a dataset we aim to identify those techniques that
complement a Product Portfolio Management process within a food manufacturing
organization. The remainder of this paper is structured as follows: section 2 provides a
detailed overview of related literature regarding PPM and DM. Section 3 comprises the
used material and method. Section 4 presents a newly developed conceptual classifica-
tion algorithm. Section 5 comprises the results and section 6 concludes with a conclu-
sion and discussion.
Related literature
To provide a clear overview and understanding of the two main concepts on which
this research is based, PPM and DM respectively, we explore the available scientific
literature.
Product portfolio management
Effective portfolio management is vital to successful product innovation and product
development. Portfolio management is about making strategic choices - which markets,
products and technologies an organization will invest in (Cooper, Edgett, & Kleinschmidt,
2001). PPM is part thereof, and focuses especially on which products a company should
include in its portfolio in order to get a maximum return on investment and customer
satisfaction. Cooper et al. (2001) define PPM as “a dynamic decisions process, whereby an
organization’s list of active (new) products (and R&D) projects are constantly updated and
revised”. Bekkers, Weerd, Spruit and Brinkkemper (2010) state that PPM takes a central
role in evaluating which active (new) product (project) should be updated/kept or should
be declined/removed based on certain assessment criteria/tools.
According to Cooper, Edgett and Kleinschmidt (2002) PPM has four main goals
within an organization: (1) Value maximization, (2) Balance, (3) Strategic direction, and
(4) right number of projects (products). With value maximization the goal is to allocate
resources in order to maximize the value of your portfolio. Balance is meant to develop
a balanced portfolio - to achieve a desired balance of projects (products) in terms of a
number of parameters; for example high risk versus low risk and across various mar-
kets, and product categories. Strategic direction is intended to ensure that, regardless of
all other considerations, the final portfolio of projects (products) truly reflects the busi-
ness strategy. Right number of projects (products) ensures that an organization takes
into consideration that there are enough resources available to ensure that there is no
project queue or pipeline gridlock when it comes to the production/development of
new projects (products).
Otten et al. Decision Analytics (2015) 2:4 Page 2 of 25
3. By reviewing the goals of PPM a global message can be derived. The message states
that the selection of the right project (product) is of the upmost importance within or-
ganizations. Selecting the right projects (products) also implies that there are great
negative consequences for an organization when wrong projects (products) are selected
(Cooper & Kleinschmidt, 1995). Such consequences are missed opportunities, missed
revenues, and loss of competitive advantage.
In order to achieve/assess the progress of the four main goals a variety of methods
exists. The first goal value maximization relates to financial value maximization. Finan-
cial affairs dominate portfolio management. Methods used are, for example, Expected
Commercial Value (ECV), Productivity Index (PI) and Return on Investment (ROI)
(Cooper, Edgett, & Kleinschmidt, 2001; Dickinson, Thornton, & Graves, 2001). With
regards to the second goal, balance, a tool which is commonly used is a “scoring
model”. A scoring model comprises a series of factors and quantitative questions per
factor and is popular among decision makers (Archer & Ghasemzadeh, 1999). A vi-
able alternative for determining the product portfolio (product assortment) is the use
of a data mining approach, called Association Rule Mining (ARM), which exposes
the interrelationships between products by inspecting a transactional dataset (Brijs,
Swinnen, Vanhoof, & Wets, 1999; 2004; Fayyad, Piatetsky-Shapiro, & Smyth 1996a,
1996b, 1996c.
With regards to the third goal, strategic direction, two main issues are in play when
an organization desires to achieve strategic alignment with its product portfolio, stra-
tegic fit and spending breakdown respectively. A method for possibly coping with and
achieving alignment between strategy and portfolio is called the “strategic bucket
method”. A strategic bucket can be defined as “money set aside for new product devel-
opment aligned with a particular strategy” (Chao & Kavadias, 2007). For the fourth
goal, the right number of projects, it is all about allocating the right resources to the
right projects. This can be achieved by conducting a resource capacity analysis. . It
quantifies the product’s demand for resources versus the availability of them (Cooper
R. G., 1999; Cooper, Edgett, & Kleinschmidt, 2000). In order to visualize the data ob-
tained by the utilized method(s) per PPM-goal so-called matrices were developed. The
first developed, most widely accepted and implemented visualization is the Boston
Consultancy Group (BCG) matrix (Hambrick, MacMillan, & Day, 1982).
Data mining
Across a wide variety of fields, including marketing and healthcare, data are increas-
ingly being collected and accumulated at a dramatic pace (e.g. (Pachidi, Spruit, &
Weerd 2014); (Spruit, Vroon, & Batenburg, 2014)). Therefore, an urgent need for theor-
ies and tools exists to assist in extracting useful information (knowledge) from these
rapidly growing volumes of digital data (Fayyad et al. 1996a, 1996b, 1996c). Piatetsky-
Shapiro and Frawley (1991) define DM as “the process of nontrivial extraction of impli-
cit, previously unknown and potentially useful information from data in databases.
Vleugel, Spruit, and Daal (2010) notably provide a methodological recipe to help select
the appropriate DM techniques given the properties of the analytical task at hand. The
various purposes of DM can be broadly divided into six categories: (1) Classification;
(2) Regression; (3) Clustering; (4) Summarization; (5) Dependency modeling; (6) Change
and deviation detection (Fayyad, Piatetsky-Shapiro, & Smyth 1996a, 1996b, 1996c).
Otten et al. Decision Analytics (2015) 2:4 Page 3 of 25
4. Classification
Classification is a function that maps (classifies) a data item into one of several prede-
fined classes (Weiss & Kulikowski, 1991). It has been studied substantially in statistics,
machine learning, neural networks, and expert systems. Classification searches for com-
mon properties among a set of objects (data) in a database and classifies them into dif-
ferent classes, which is done according to a pre-defined classification model (Chen,
Han, & Yu, 1996). To build a classification model, a sample database E is treated as the
training set, in which each tuple consist of the same set of multiple attributes as the
tuples in a large database W, and additionally, each tuple has a known class identity
(label) associated with it (Chen, Han, & Yu, 1996). Classification can be very simple
with a statement like “IF x = 1 AND y = 6 THEN z = 2 ELSE z = 3” or one can define a
very advanced classification model based on neural networks (Lippmann, 1988), genetic
algorithms (Goldberg, 1989), and decision trees (Quinlan, 1993). In terms of ability, de-
cision trees are a rapid and effective method for classifying data set entries, and can
provide good decision support capabilities (Aitkenhead, 2008).
Regression
Regression is a function that maps a data item to a real-valued prediction variable
(Fayyad, Piatetsky-Shapiro, & Smyth 1996a, 1996b, 1996c). Regression is a statistical
method with a predictive purpose (Larose, 2005). The main sub regression methods
are: (1) Linear regression, (2) multiple linear regressions, and (3) non-linear regression.
Linear regression involves a response variable Y and a single predictor variableX. In order
to determine the linear regression from Y on X the formula Y = bxyX + axy is used to con-
struct the continuous-valued function. Where bxy is the coefficient and axy is the intercept.
Multiple linear regression involves a response variable Y and more than one predictor
variable. It addresses questions about the relationship between a set of independent
variables and a dependent variable. The general equation is written as Y = b0 + b1X1 +
b2X2 + … + bpXp + e. Each of the X’s is an independent variable, each of the b’s is the
corresponding regression coefficient, and eis the error in prediction for each case.
Non-linear regression is based on the same basic idea as linear regression, namely to
relate a response Y to a vector of predictor variables X = (X1, …, Xk)T
. Nonlinear regres-
sion is characterized by the fact that the prediction equation depends nonlinearly on
one or more unknown parameters. Nonlinear regression usually arises when there are
physical reasons for believing that the relationship between the response and the pre-
dictors follows a particular functional form (Smyth, 2002).
Clustering
Clustering is the division of data into groups of similar objects. Each group, called a
cluster, consists of objects that are similar to one another and dissimilar to objects of
other groups (Berkhin, 2006). Clustering data in a few clusters results in a loss of cer-
tain fine details, but achieves simplification. Clustering can be viewed as a density esti-
mation problem, which is the subject of traditional multivariate statistical estimation
Otten et al. Decision Analytics (2015) 2:4 Page 4 of 25
5. (Scott, 1992). This section elaborates on clustering in a data mining context, which is
characterized by large datasets with many attributes of different types (Berkhin, 2006)
This section elaborates on both the hierarchical- and partitioning-approach. Hierarch-
ical clustering builds a cluster hierarchy or a tree of clusters, also known as a dendro-
gram. The dendrogram has a high degree of similarity with so-called decision trees.
Every cluster node contains child clusters, which allows exploring data on different
levels of granularity (agglomerative, divisive) (Jain & Dubes, 1988). Advantages of hier-
archical clustering are: flexibility with regards to the level of granularity, ease of hand-
ling form of similarity or distance, and its applicability to any attribute type. However,
there are some disadvantages such as vagueness of termination criteria and most
hierarchical algorithms do not revisit clusters once constructed. The basics of hier-
archical clustering include Lance-Williams formula, the idea of conceptual clustering,
classic algorithms like SLINK, COBWEB, and newer algorithms such as CURE and
CHAMELEON (Sibson, 1973; Fisher, 1987; Guba, Rastogi, & Shim, 1998; Karypis,
Han, & Kumar, 1999).
Partitioning clustering algorithms obtain a single partition of the data instead of a
clustering structure, such as the dendrogram, produced by the hierarchical approach
(Jain, Murty, & Flynn, 1999). Partitioning clustering algorithms are subdivided in two
categories. The first is called partitioning relocation clustering and is further classified
into probabilistic clustering, k-medoids methods, and k-means methods (Park, 2009;
Larose, 2005). Such methods concentrate on how well points fit into their clusters. The
second class of algorithms is called density-based partitioning and attempts to discover
dense connected components of data, which are flexible. Density-based connectivity is
used in the algorithms DBSCAN, OPTICS, DBCLASD (Ester, Kriegel, Sander, & Xu,
1996; Ankerst, Breuning, Kriegel, & Sander, 1999; Xu, Ester, Kriegel, & Sander, 1998).
Summarization
The ability to summarize data provides an important method for getting a grasp on the
meaning of the content of a large collection of data. It enables humans to understand
their environment in a manner amenable to future useful manipulation (Yager, 1982) In
the context of data mining the most appropriate definition of summarization is “grasp
and briefly describe trends and characteristics appearing in a dataset, without doing
(explicit) manual ‘record-by-record’ analysis” (Niewiadomski, 2008).
Two main approaches exist to summarize a dataset, numerical summarization (NS)
and linguistic summarization (LS) (Wu, Mendel, & Joo, 2010). NS was the first
summarization technique to be used. NS is concerned with e.g., summarizing statistics,
exemplified by the average, median, minimum, maximum, α-percentile (Kacprzyk &
Zadronzy, 2005). LS provide easier understandable summarized information than NS.
The output it generates is of the form “about half of the sales in summer is of prod-
uct group accessories” (Kacprzyk & Zadronzy, 2005). Over the past years a number of
approaches were introduced for summarizing data in a database and can be catego-
rized in four classes: (1) methods based on unary operators, (2) approaches related
to multidimensional databases, (3) methods based on statistic and symbolic tech-
niques and (4) approaches based on fuzzy set theory (Triki, Pollet, & Ben Ahmed,
2008).
Otten et al. Decision Analytics (2015) 2:4 Page 5 of 25
6. Yager’s approach for linguistic summaries using fuzzy logic with linguistic quantifiers
consists of:
V is a quality (attribute) of interest (i.e. salary in a database of workers)
Y = {y1, .., yn} is a set of objects (records) that manifest quality V (e.g. the set of
workers; hence V(yi) are values of quality for object yi
D = {V(yi), …,V(yn)} is a set of data (the “database” on question)
Basically, given a set of data D, a hypothesis can be constructed which states that any
appropriate summarizer S and any quantity in agreement Q, and the assumed measure
of truth Twill indicate the truth of the statement that Q data items statisfy the state-
ment (summarizer) S. For more information on the actual mathematics and more ex-
amples see the work of Yager (1982).
Dependency modeling
Dependency modeling consists of finding a model that describes significant dependen-
cies between variables (Fayyad, Piatetsky-Shapiro, Smyth 1996a, 1996b, 1996c). In
the context of this research it is the aim to find dependencies between transactions in
large datasets. Therefore, this section will elaborate on a DM-technique called Associ-
ation Rule Mining (ARM) (Agrawal, Imieliski, Swami, 1993).
The task of ARM is to determine and derive a set of association (dependency) rules
in the format “X^
1…^
Xm ➔ Y ^
1 ::::^
Yn “where Xi (for i € {1, …, m}) and Yj (for j € {1, …, n})
are sets of attribute-values, from the relevant data sets in a database (Kotsiantis
Kanellopoulos, 2006).
An example of ARM is market basket analysis and comprises the analysis of cus-
tomer buying habits by finding associations between the different items that customers
place in their ”basket” (Han, Kamber, Pei, 2011). Given the association rule:
Computer ⇒ financial management software support ¼ 2%; confidence ¼ 60%½ Š ð2:2Þ
Two measures manifested, namely, support and confidence. They respectively reflect
the usefulness and certainty of discovered association rules. Given equation 2.2 with a
support of 2 % means that 2 % of all the transactions show that computer and financial
management software are purchased together. A confidence of 60 % means that 60 % of
the customers who purchased a computer also bought the financial management soft-
ware. Association rules are considered interesting if they satisfy both a minimum sup-
port threshold and a minimum confidence threshold, which are determined manually by
users or domain experts (Agrawal, Imieliski, Swami, 1993).
A high value of confidence suggests a strong association rule. However this can be
deceptive because if X and/or Y have a high support, we can have a high value for con-
fidence even when they are independent. Hence, the metric lift was introduced. Lift
quantifies the relationship between X and Y (Ordonez Omiecinski, 1999). In general,
a lift value greater than 1 provides strong evidence that X and Y depend on each other.
A lift value below 1 states that X depends on the absence of Y or vice versa. A lift value
close to 1 indicates X and Y are independent (Ordonez, 2006).
A Commonly used algorithm for mining association rules is APRIORI (Agrawal
Srikant, 1994). APRIORI is an algorithm for mining frequent itemsets for Boolean
Otten et al. Decision Analytics (2015) 2:4 Page 6 of 25
7. association rules. It employs an iterative approach knowas level-wise search, where
k-itemsets are used to explore (k + 1)-itemsets. A second well known algorithm is the
FP-Growth-algorithm for frequent item set mining (Han, Pei, Yin, 2000). The basic
idea of the FP-Growth algorithm can be described as a recursive elimination scheme
(Borgelt, 2005).
Change and deviation detection
Change and deviation detection focuses on discovering the most significant changes in
the data from previously measured or normative values (Fayyad, Piatetsky-Shapiro,
Smyth 1996a, 1996b, 1996c). Change and deviation analysis describes and models regu-
larities or trends for objects whose behavior changes over a period of time. Distinctive
features of such analysis are time-series, and sequence or periodically pattern matching
(Han, Kamber, Pei, 2011).
Time-series is in fact a sequence of data points, measured typically at successive time,
spaced at uniform time intervals. A sub discipline of time-series forecasting deals with
the seasonality component and is called seasonal time series forecasting (Zhang Qi,
2005). Seasonality is a periodic and recurrent pattern caused by factors such as weather,
holidays, repeating promotions, as well as the behavior of economic agents (Hylleberg,
1992). Accurate forecasting of seasonal and trend time-series is very important for ef-
fective decisions in retail, marketing, production, inventory control and other business
sectors (Lawrence, Edmundson, O’Connor, 1985; Makridakis Wheelwright, 1987).
A well-known algorithm for time-series modeling is the autoregressive integrated mov-
ing average (ARIMA) and is the most widely used to forecast future events/figures. The
major limitation of ARIMA is the pre-assumed linear form of the model (Zhang P. G.,
2003). Furthermore, forecasting accuracy is known to be problematic in cases of high
demand uncertainty and high variance within demand patterns (Maaß, Spruit, Waal
2014).
Based on the limitations of the ARIMA models with regards to the lacking capability
of capturing nonlinear patterns a need exists for an approach that can cope with the
aforementioned limitations. Recently Artificial Neural Networks (ANN) received much
attention (Hill, O'Connor, Remus, 1996; Balkin Ord, 2000). The major advantage of
ANN compared to ARIMA models is their flexible nonlinear modeling capability (Zhang
P. G., 2003). Additionally, no specific assumptions need to be made about the model and
the underlying relationship is determined through data mining (Zhang Qi, 2005).
Pattern matching in a data mining context, is the act of checking some sequence of
tokens for the presence of constituents of same pattern. In contrast to pattern recogni-
tion, the match usually has to be exact. The patterns generally have the form of se-
quences or tree structures (Zhu Takaoka, 1989). Several pattern matching algorithms
where developed over time such as the KPM algorithm, BM algorithm and RK algo-
rithm (Zhu Takaoka, 1989).
Selected data mining techniques
Three data mining techniques appear to be the most suited, classification, dependency
modeling, and change and deviation detection respectively. Reflecting on PPM a useful
Otten et al. Decision Analytics (2015) 2:4 Page 7 of 25
8. tool is to classify products in quadrants (i.e. BCG-matrix, DPM-matrix) which relates
to the idea of classification.
The second option, dependency modeling, and in particular association rule mining,
is a viable option for discovering associations between products in large transactional
datasets and has been proven useful for retailers when deciding on promotions and
shelve arrangements. A third possible category is change and deviation detection, in
particular seasonal time series forecasting.
Material and methods
Material
The material used for evaluating the selected data mining techniques is a transactional
dataset comprising 12 months of sales statistics. The transactional dataset was provided
by a case study organization which produces poultry for retailers in the Benelux.
The dataset (fact table) used for determining the applicability of the selected data
mining techniques comprises 7186349 records of transactional data generated between
2011-01-01 and 2011-12-31. The dataset is stored in a MSSQL-database. Table 1 pro-
vides an overview of the most significant attributes within the dataset. The column
“type” can obtain the values “PK”, “FK”, “M” which stands for PrimaryKey, ForeignKey,
and Metric respectively.
The attribute with type “FK” are foreign keys, which are related to primary keys in
each dimension table. The dimension tables are at our disposal and can be used for
possible data understanding and data preparation. The dimension tables are “Customer_
Base”, “Article_Base”, “Article_ProductGroup”, “Article_MainProductGroup”.
The data quality of the initial dataset is found to be satisfactory with regards to data
types and completeness. No inconsistencies, errors, missing values or noise was found
in the dataset. However, three issues require attention:
1. The presence of articles which have multiple article numbers (i.e. 303125 and
15303125).
Table 1 Dataset – attributes
Attribute Data type Description Type
(PK/FK/M)
FA076_LS_DATUM Datetime (yyyy-mm-dd) Date of actual transaction PK
FA076_BS_NR Integer Transaction number PK
FA076_ADR_NR Integer Customer number FK
FA076_ART_NR Integer Article number PK/FK
FA076_WG Integer Article product group number FK
FA076_OWG Integer Article main product group number FK
FA076_KG_MENGE Decimal Sales volume in KG M
FA076_BS_MENGE INT Ordered items M
FA076_PE_MENGE INT Sales volume in items M
FA076_LI_MENGE INT Delivered items M
FA076_UPD_DATUM Datetime (yyyy-mm-dd) The actual date when a record has been
updated by the system
M
FA076_REC_NR INT Auto-increment number per record per order PK
Otten et al. Decision Analytics (2015) 2:4 Page 8 of 25
9. 2. The presence of main product groups which are not of concern to the product
portfolio such as “crates”, “packaging”, “labels”, “diverse” and “clothing”.
3. The presence of product groups with large fluctuations in sales volume (unit or
KG) per periodic interval.
Table 2 provides the scripts used for resolving the issues mentioned above.
Method
For evaluation of the selected data mining techniques with regards to their applicability
in a PPM-context the CRISP-DM framework is used (Chapman, et al., 2000). It com-
prises six stages which are described in Table 3.
Per data mining technique the stages data preparation, modeling, and evaluation are
elaborated on. The stage Deployment is elaborated on in the section “Deployment”. Note
that we omit the CRISP-DM-stage “data understanding” per data mining technique due
to the fact that we described and explored the initial dataset in the previous section.
Calculation
Based on the literature survey, it was found that a common visualization tool used in a
PPM-context is a portfolio matrix. A possibility for automatically placing products in
the product portfolio in a certain quadrant of a matrix is classification. After exploring
various classification techniques (i.e. classification rules and decision trees) it was found
that none of the algorithms were able to plot and visualize the classified products on a
portfolio matrix. In addition, none of the algorithms are able to combine outcomes of data
mining techniques (ARM) and explicit available metrics. Hence, we propose the following
algorithm for classifying, plotting and visualizing products in the product portfolio:
The proposed algorithm is used for classifying, plotting, and visualizing the strength
of the association between X andY; based on Lift, and the SalesVolumeInKG of Y. This
aid in determining whether or not X should be kept in the product portfolio or be re-
moved from it. In Table 4 the parameters for the algorithm are presented.
Results
This section presents the results of the data mining technique evaluation of depend-
ency modeling, change and deviation detection, and classification respectively.
Otten et al. Decision Analytics (2015) 2:4 Page 9 of 25
10. Dependency modeling
With regards to dependency modeling, a sub technique in the context of this research
is selected. This sub technique is called Association Rule Mining (ARM) and seeks to
identify associations between articles among transactions.
Data preparation
In order to perform ARM on a dataset it is inevitable that data in the initial dataset is
selected, cleansed, restructured and formatted. ARM requires a dataset with the struc-
ture and format such as presented in Table 5. Where “TransactionID” represents the
unique transaction in the dataset and “In” presents a unique article in the dataset which
can obtain the values 1 and 0. Where 1 indicates that the article’s presence is true;
whereas Zero indicates that the article’s presence is false for a specific transaction.
Table 3 CRISP-DM framework - stages
Stage Description
Business understanding phase Focuses on understanding the project objectives and requirements from a
business perspective, converting this knowledge into a data mining problem
definition and a preliminary plan is designed to achieve the objectives
(Chapman, et al., 2000)
Data understanding phase The initial data collection and proceeds with activities to get familiar with the
data, discover quality problems and insight (Chapman, et al., 2000)
Data preparation phase Covers all activities needed to construct the final data set (Chapman, et al., 2000).
Modeling phase The selection and application of modeling techniques. Typically, several
techniques are available for the same data mining problem type. Some
techniques have specific requirements regarding the structure of the data.
Therefore, going back to the data preparation phase is often necessary
(Chapman, et al., 2000).
Evaluation phase The evaluation of the created data mining models, before actually deploying
them. By thoroughly evaluating and reviewing the steps used to create the
model it is determined if the model achieves the business objectives. If not,
a new iteration of the modeling phase is started (Chapman, et al., 2000).
Deployment phase The implementation of the results found by the data mining model within the
supplied data set via tools such as reports or dashboards (Chapman, et al., 2000)
Table 2 SQL-functions - scripts
Issue Script
Issue 1 select CONVERT(int,RIGHT((CONVERT(varchar,article_nr))),6))
FROM table_name
WHERE len(article_nr) = 6 and
FA076_OWG = 1
Issue 2 SELECT * from table_name
WHERE
FA076_OWG mainproductgroup_nr and
FA076_OWG
mainproductgroup_nr and
....
FA076_OWG mainproductgroup_nr
Issue 3 SELECT productgroup_nr, DATEPART(YEAR, FA076_LS_DATUM),
DATEPART(MONTH,FA076_LS_DATUM), SUM(FA076_KG_MENGE)
FROM table_name
WHERE FA076_WG = productgroup_nr
GROUP BY productgroup_nr, DATEPART(YEAR, FA076_LS_DATUM),
DATEPART(MONTH, FA076_LS_DATUM)
ORDER BY productgroup_nr, DATEPART(YEAR, FA076_LS_DATUM),
DATEPART(MONTH, FA076_LS_DATUM)
Otten et al. Decision Analytics (2015) 2:4 Page 10 of 25
11. Modeling
With regards to ARM we have chosen the APRIORI-algorithm for deriving association
rules by means of the arules-package (Hahsler, Grun, Hornik, 2005). The model de-
pends on two main parameters as presented in Table 6.
For the analysis we set the parameters Min_support = 0.1 (10 %) and a Min_confidence =
0.1 (10 %). In order to derive any meaning with regards to the usefulness of an association
rule, the metrics support and confidence do not suffice. Hence, the metric lift is used to
determine the usefulness of an association rule.
Evaluation
In order to evaluate the correctness, usefulness and best approach of the selected data
mining technique we apply the constructed model on 3 variations in the dataset:
Analysis #1; we analyze the whole target dataset (containing all 12 product groups
belonging to the main product group 1);
Analysis #2; we analyze each product group over a whole year;
Analysis #3; we analyze a product which is marked for analysis in the case study
due to it performing under 250 kg of sales volume per week.
Analysis #1 – whole dataset
The APRIORI-algorithm (min_support = 0.1; min_confidence = 0.1) derived 2526 associ-
ation rules from the target dataset, presented in Table 7. We found that the majority of
the rules is plotted between a support of 0.1 – 0.2, and a confidence of 0.27 – 0.98.
Additionally, we found, the larger the support, the lower the confidence for an associ-
ation rule.
After cross-referencing the items presented in the association rules with the data in
the dimension-table “Article_Base”, it was found that all items belonged to product
group 1 and 2. The cause hereof is the fact that the majority of the records are distrib-
uted among product group 1 and a relative large proportion of the records in product
Table 4 Classification data mining - parameters
Parameter Description
MAX(LIFT) The maximum derived LIFT value from top 10 AssocRule dataset
MIN(LIFT) The minimum derived LIFT value from top 10 AssocRule dataset
MAX(SalesVolumeInKG) The maximum derived SalesVolumeKG value from the Y-items in the top 10 AssocRule
dataset
MIN(SalesVolumeInKG) The minimum derived SalesVolumeKG value from the Y-items in the top 10 AssocRule
dataset
Table 5 ARM - dataset structure
TransactionID I1 I2 I.. In
1 1 0 … 1
…. … … … …
N 1 1 … 0
Otten et al. Decision Analytics (2015) 2:4 Page 11 of 25
12. group 2. Figure 1 depicts the visualization of the top 10 association rules, where the
size of each vertex indicates the support value and the color indicates the value of the
metric lift.
Analysis #2 – dataset(s) per product group
For analysis 2 we generated separate datasets per product group. Each separate dataset
comprises 12 months of transactional data. Per dataset (12 in total) we applied the
APRIORI-algorithm (min_support = 0.1; min_confidence = 0.1). An excerpt of the ana-
lysis output is presented in Table 8.
Analysis #3 – dataset per underperforming article (item)
For the third analysis, we use the following approach:
1. Identify an article (item) which performs under the so-called “threshold-value”
2. Generate a dataset comprising all transactional data in which the identified article is
included
3. Apply the APRIORI-algorithm on the generated dataset for deriving association
rules
4. Analyze the derived association rules with regards to the identified article.
Step 1 The following underperforming articles were identified (Table 9):
683 articles were identified, which do not meet the threshold value in a given week.
However, just merely selecting one of these articles could also cause unreliable results.
Therefore, we selected articles which chronologically (periodical interval) over time, did
not meet a pre-defined threshold value.
Step 2 Per article (405245, 701024), a dataset is generated comprising all the transac-
tional data of which the specific article is part of. Each dataset is stored in a *. CSV con-
forming to the structure 5.
Table 6 Association rule mining - parameters
Parameter Data type Description
Min_support decimal (0,2) Min_support means that x% of all the transactions under analysis
show that X and Y are purchased together
Min_confidence decimal (0,2) Min_confidence means that x% of the customers who purchased
X also purchased Y.
Table 7 Dataset results
Dataset Value
Size 7186349 records
Product groups 1,2,3,4,5,6,7,8,9,10,11,12
Months 1,2,3,4,5,6,7,8,9,10,11,12
Transactions 385100
Min_support 0.1
Min_confidence 0.1
Otten et al. Decision Analytics (2015) 2:4 Page 12 of 25
13. Step 3 Per article-dataset the APRIORI-algorithm is applied. In Table 10, per article-
dataset, the number of association rules is presented, together with the parameter-
settings for min_support and min_confidence. The parameter-settings were left to their
default values.
Step 4 We visualized the derived association rules per article dataset by means of the
metric lift. In order to do so we created a subset of the association rules, which only con-
tained the article under investigation and the highest possible lift-scores for 10 rules.
Visualization
Dataset – 405245
The subset is created by selecting all left-hand-side item sets (X) which contain article
405245 and a lift higher than 1. This resulted in a subset of 911 association rules with
support ranging from 0.1003 – 0.5206, confidence ranging from 0.8 – 0.9827, and lift
ranging from 1.193 – 4.844. In order to reduce the association rules we selected only
those association rules with a minimal lift value of 4.062, which is the value of the lift
metric at rule 10 when sorting them in ascending order. Figure 2 depicts the
visualization of the top 10 association rules. The size of each vertex resembles the
size of support and the color represents the lift.
Dataset – 701024
The subset is created by selecting all left-hand-side item sets (X) which contain article
701024 and a lift higher than 1. This resulted in a subset of 1103 association rules with
Graph for 11 rules
303339
303378
304425
304437
304466
314501
314502
314505
size: support (0.101 - 0.11)
color: lift (4.958 - 5.748)
Fig. 1 Visualization - association rules
Otten et al. Decision Analytics (2015) 2:4 Page 13 of 25
14. support ranging from 0.1008 – 0.8853, confidence ranging from 0.8 – 0.9737, and lift
ranging from 0.9599 – 4.3651. In order to reduce the association rules we selected only
those association rules with a minimal lift value of 3.68, which is the value of the lift
metric bat rule 10 sorting them in ascending order. Figure 3 depicts the visualization of
the top 10 association rules. The size of each vertex resembles the size of support and
the color represents the lift.
Change and deviation detection
With regards to change and deviation detection, a sub technique in the context of this
research is selected. As mentioned in section 2.3.5, this sub technique is called time
series analysis and seeks to predict future events based on data points recorded over
equally spaced points in time.
Data preparation
In order to perform Time Series Analysis (TSA) on a dataset it is inevitable that data in
the initial dataset is selected, cleansed, restructured and formatted. TSA requires a dataset
with the structure and format such as presented in Table 11. Where “TimeStamp” is the
point in time at which the measurement was observed and “ObservedValue” represent the
observed value at that specific point in time.
Modeling
We have chosen to use the forecast-package, which is freely available (Hyndman
Khandakar, 2007). The forecast package comprises approaches for automatic forecasting
such as exponential smoothing and ARIMA-modeling. For our model creation we use the
Table 9 Excerpt of underperforming articles
Underperforming articles
Year Week ItemID SalesVolumeKG
2010 1 405245 14,690
2010 8 403953 96,410
2010 52 305944 53,565
Table 8 Analysis #2 - dataset
Dataset – product group 1 Value
Size 1731411
Transactions 361832
#Association rules 92
Support 0.1029 – 0.2760
Confidence 0.2972 – 0.9761
Lift 1.836 – 2.893
Dataset – product group 12
Size 3158
Transactions 1110
#Association rules 50
Support 0.3306 – 0.5414
Confidence 0.6571 – 0.9980
Lift 1.701 – 2.339
Otten et al. Decision Analytics (2015) 2:4 Page 14 of 25
15. exponential smoothing approach. The model depends on the parameters presented in
Table 12 for forecasting purposes.
The forecast package deals with the computation of the aforementioned parameters.
Hence, no further elaboration on the values given to the parameters is provided. The
parameters are used for smoothing out the time series dataset properly.
Table 10 Association rules - Article dataset
Dataset Value
Dataset – 405245
Min_support 0.1
Min_confidence 0.8
#ofAssociationRules 21241
Dataset – 403953
Min_support 0.1
Min_confidence 0.8
#ofAssociationRules 3882
Dataset – 701024
Min_support 0.1
Min_confidence 0.8
#ofAssociationRules 48248
Graph for 10 rules
403231
403241
404322
404343
404411
404425
404466
405245
405944
503117
505794
size: support (0.112 - 0.177)
color: lift (4.062 - 4.844)
Fig. 2 Visualization - association rules - article 405245
Otten et al. Decision Analytics (2015) 2:4 Page 15 of 25
16. Evaluation
In order to evaluate the correctness, and usefulness of the selected data mining tech-
nique the exponential smoothing model is applied on the most detailed level; the article
level. By forecasting the SalesVolumeInKG and determining whether, over time, it ex-
ceeds the threshold value, we determine whether change and deviation detection is
complementary as a data mining technique to the PPM-process.
For analysis purposes we aimed to identify articles which were consequently under-
performing, we choose to use two earlier identified article and identify one article that
had data for each of the 24 months. Considering the threshold value from the con-
ducted case study, which was 250kg on a weekly basis, the threshold value was scaled
up by a factor of 53 and divide by 12, resulting in 1104,167 kg per month. We identi-
fied article 605494 as an underperformer with regards to the scaled up threshold value
and the presence of 24 months of data.
Graph for 10 rules
303187
303188
303189
303927
701024
701026
701027
size: support (0.142 - 0.221)
color: lift (3.68 - 4.365)
Fig. 3 Visualization - association rules - article 701024
Table 11 TSA - Dataset structure
TimeStamp Observed value
yyyy-mm-dd 340,798
N 600,145
Otten et al. Decision Analytics (2015) 2:4 Page 16 of 25
17. Article – 405245
For the application of TSA on article 405245, we extracted SalesVolumeInKG from the
extended dataset, grouped per month and year, meeting the criteria of the newly de-
fined threshold value. This resulted in a dataset comprising 9 sequential months in the
year 2010. We transformed the derived dataset to a timeseries-object with the param-
eter frequency = 12 because we were dealing with data points observed per monthAfter
applying the exponential smoothing-approach it was found that it did not generated ad-
equate results as depicted in Fig. 4.
Article – 403953
For the application of TSA on article 403953, we extracted SalesVolumeInKG from the
extended dataset, grouped per month and year, meeting the criteria of the newly de-
fined threshold value. This resulted in a dataset comprising 15 sequential months in
the year 2010 and 2011. We transformed the derived dataset to a timeseries-object with
the parameter frequency = 12 because we were dealing with data points observed per
month. After applying the exponential smoothing-approach it was found that it did not
generated adequate results as depicted in Fig. 5.
Analysis – 605494
For the application of TSA on article 605494, we extracted SalesVolumeInKG from the
extended dataset, grouped per mondht and year, meeting the criteria of the newly de-
fined threshold value. This resulted in a dataset comprising 24 sequential months in
years 2010 and 2011. We transformed the derived dataset to a timeseries-object with
the parameter frequency = 12 because we were dealing with data points observed per
month. After applying the exponential smoothing-approach it was found that it gener-
ated adequate results as depicted in Fig. 6.
Table 12 Time Series Analysis – parameters
Parameter Data type
Alpha Decimal (2,4)
beta Decimal (2,4)
Phi Decimal (2,4)
Fig. 4 Visualization - article 405245 - forecast
Otten et al. Decision Analytics (2015) 2:4 Page 17 of 25
18. Classification
As mentioned earlier we examined the capabilities of existing classification algorithms.
However, those algorithms were lacking with regards to visualization of classified data.
Hence, we evaluate the earlier presented conceptual BIPPMClass-algorithm.
Data preparation
Classficiation requires a dataset with the structure and format such as presented in
Table 13.
Where “recordID” is the unique identifier of the record, “PredictorAttrib#” is an attri-
bute by which the classification algorithm seeks to identify to what class a record belongs,
and “ClassLabel” is the attribute which indicates to what class a record is classified.
Modeling
With regards to the data mining model for classifying, plotting and visualizing each
product on a portfolio matrix we use the earlier proposed algorithm.
Evaluation
For the evaluation of the proposed algorithm we’ve chosen the top 10 association rules
of articles 405245 and 701024. In order to evaluate if the algorithm is useful as a
visualization tool we’ve adopted the BCG-matrix (Hambrick, MacMillan, Day, 1982).
Based on the BCG-matrix we’ve constructed a product matrix, depicted in Figs. 7 and 8.
The product matrix comprises 3 classes, “Keep”, “FurtherAnalysis”, and “Remove”,
Fig. 5 Visualziation - article 403953 - forecast
Fig. 6 Visualization - article 605494 - forecast
Otten et al. Decision Analytics (2015) 2:4 Page 18 of 25
19. respectively. The dominant metric is sold kilograms. Therefore, even if there is a strong
association with an article (Y), but the SalesVolumeInKG of Y is low, then the article
should be classified as “Remove”.
Analysis – 405245
For the analysis of article 405245 we’ve derived the top 10 association rules. Additionally,
for all unique item Ys in the top10-dataset, the (maximum) lift and SalesVolumeInKG
were retrieved with an MSSQL-query, resulting in the datasets “Lift-values” and “Sales
VolumeInKG-values”. By combining the data of datasets “LIFT-values” and “SalesVolume
InKG-values”, “Dataset – 405245 – Item Y’s” resulted. The associated articles were plotted
on the product matrix by means of coordinates(x,y) = (Lift,SalesVolumeInKG), as depicted
in Fig. 7.
Based on the COUNT-results, it can be concluded that the majority of the items (4
in total) are clustered in Q2 and Q3. Keeping in mind the dominant metric is SalesVo-
lumeInKG it is clear that article 405245 has an above average associations with three
products but two of them generate low “SalesVolumeInKG”. Hence, article 405245 can,
according to the data and proposed algorithm, be classified in Q2 and therefore re-
moved from the product portfolio.
Table 13 Classification - dataset structure
RecordID PredictorAttrib1 PredictorAttrib2 PredictorAttrib3 ClassLabel
1 12 41 8 Class1
N 76 8 12 Class2
Fig. 7 Portfolio matrix - 405245
Otten et al. Decision Analytics (2015) 2:4 Page 19 of 25
20. Analysis – 701024
For the analysis of article 701024 we’ve derived the top 10 association rules. Additionally,
for all unique item Ys in the top Ten-dataset, the (maximum) lift and SalesVolumeInKG
were retrieved with an MSSQL-query, resulting the datasets “Lift-values” and “Sales
VolumeInKG-values” respectively. By combining the data of datasets “LIFT-values” and
“SalesVolumeInKG-values”, “Dataset – 405245 – Item Y’s” resulted. The associated arti-
cles were plotted on the product matrix by means of coordinates(x,y) = (Lift,SalesVolume
InKG), as depicted in Fig. 8.
Based on the COUNT-results it can be concluded that the majority of the items (4 in
total) are clustered in Q1 and Q4. However, due to SalesVolumeInKG being the domin-
ant metric and article 2 and 3 each have a SalesVolumeInKG of 13286.900 and
12006.000, it is appropriate to classify article 701024 in quadrant 1. Hence, article
701024 is kept in the product portfolio.
Deployment
In order to provide the decision makers within a PPM-process with the information
generated by the selected data mining techniques we propose a deployment-template
(concept) called “ARTICLE REPORT”. The ARTICLE REPORT comprises the concepts
PRODUCT PERFORMANCE, ANALYSIS RESULTS and PRODUCT MATRIX found
in the BIPPM-method. In Fig. 9 the proposed layout of the concept ARTICLE REPORT
is depicted.
The ARTICLE REPORT comprises a data and visualization section per integrated
concept. On the left hand side the data section is presented to the decision maker with
actual statistics. On the right hand side the visualization section is presented to the
Fig. 8 Product matrix - 701024
Otten et al. Decision Analytics (2015) 2:4 Page 20 of 25
21. decision maker. The visualization per integrated concept is based on the data residing
in the data section of the ARTICLE REPORT.
Discussion
With regards to the scope of this research, the investigation of the applicability of data
mining techniques in a product portfolio management context, the selected data min-
ing techniques (classification, dependency modeling, and change and deviation detec-
tion) were empirically validated by applying them to a transactional dataset comprising
12 months of sales statistics.
Change and deviation detection, in particular seasonal time series forecasting, was
found to be of no added value in a product portfolio management context. The algorithm
used, exponential smoothing, forecasts data based on historical data. The forecasted data
points did not exceed the defined threshold value due to the fact that the algorithm calcu-
lates future data points based on available historical data. Hence, if a product
Fig. 9 Article report
Otten et al. Decision Analytics (2015) 2:4 Page 21 of 25
22. consequently underperforms with regards to a certain criteria, the exponential
smoothing-algorithm will forecast future data points of the same magnitude. Thefore,
they do not exceed the defined threshold value. However, despite the fact that seasonal
time series analysis does not provide useful input in a product portfolio management con-
text, it could prove to be useful in other organizational processes such as procurement or
sales.
With regards to dependency modeling and classification, both provide useful input for a
product portfolio management. In the context of this research, analysis outcome shows
that the best approach is to analyze each product separately. It was also determined that
deriving associations on a whole dataset (comprising all articles and product groups) or a
constructed dataset per product group yields unacceptable association rules.
Zooming in on classification, it was found that existing algorithms did not contain a
visualization step for plotting products on a portfolio matrix. Hence, a new conceptual
algorithm for classifying and plotting items on a portfolio matrix was presented. The
validation of the algorithm was done by hand. No prototype was developed. However,
the results of the application of the algorithm with regards to classification and
visualization are promising. However, this algorithm needs to be validated in practice
on several datasets comprising transactional data and association rules. In addition, it is
desirable to perform more case studies. Furthermore, the scaling of the X- and Y-axis
in a (portfolio) matrix is arbitrary. Anyone and everyone are free to determine the def-
inition and thereby scaling of the X- and Y-axis on a matrix. If done improperly, the in-
formation and outcome the algorithm generates could be false and misinterpreted,
which could lead to erroneous decision making in a product portfolio management
context. Hence, other visualization options should be explored also.
Conclusions
For the evaluation of the selected data mining techniques three analysis approaches
were executed, (1) whole dataset analysis, (2) separate datasets per identified product
group, (3) separate dataset per product up for analysis.
The first data mining technique to be was association rule mining (dependency mod-
eling). With regards to the first analysis approach it was found that the parameters c.q.
thresholds of the association rule mining-algorithm, APRIORI, are greatly influenced
by the size of the dataset and the distribution of records among each product group.
The first analysis approach yielded unacceptable results. The association between prod-
ucts derived; with both thresholds (support and confidence) at a value of 0.1 all
belonged to product group 1 and 2. After inspection of the dataset it was clear that the
majority of the records were distributed among product group 1 and 2. Therefore, by
taking into account the parameters and using the first analysis approach, the APRIORI-
algorithm only derived associations for products residing in product group 1 and 2.
By following the second analysis approach in combination with the APRIORI-
algorithm, associations were identified between products which do not belong to product
group 1 and 2. However, a limitation occurred due to the fact that each product group
has its own transactional dataset. the inability of this analysis approach is to derive cross-
product group association rules. This limitation could lead to misinterpretation of results
and result in wrongful decision making. Hence, the second analysis approach is classified
as undesirable.
Otten et al. Decision Analytics (2015) 2:4 Page 22 of 25
23. The third analysis approach used a dataset comprising all transactions and thereby
transactional data in which the product under analysis occurred. Three products were
selected for evaluating association rule mining in combination with this approach. By
analyzing each article separately, it was found that this analysis approach coped with
the limitation of cross-product group-association rule generation. It derived association
rules between products that belonged to different product groups. Hence, this approach
and the data mining technique association rule mining are classified as desirable and
applicable in a product portfolio management context. Evidently, for the second and
third data mining technique, , the product based approach was used.
For seasonal time series forecasting the exact same three products used in the associ-
ation rule mining evaluation were used. In order to properly analyze this data mining
technique, the transactional dataset provided by the case study organization was ex-
tended to 24 months of transactional data, residing in a two year time-period (2010
and 2011). For evaluating this data mining technique the exponential smoothing algo-
rithm was selected. Applying the exponential smoothing algorithm on each of the three
dataset yielded undesirable results. For the proper functioning of the exponential
smoothing algorithm it is required that in each period (in this case year) per observed
data point (in this case months) data are present. For each article this was not the case,
which resulted in failed forecasts (flat lines) or partial forecasts (three months in ad-
vance). In order to properly evaluate the exponential algorithm, two articles were de-
rived from the dataset comprising 24 months of data observed per similar data point
per period. By applying the exponential smoothing algorithm it was able to properly
forecast future data points. However, the exponential smoothing algorithm forecasts
data points based on historical data. The articles and thereby the generated datasets
were selected by means of the threshold value. After inspecting the forecasted data
points, none of the data points exceeded the pre-defined threshold value. Concluding
on the evaluation of seasonal time series forecasting, it is safe to say that, in a product
portfolio management context, the technique is not applicable.
With regards to the data mining category classification, it was desirable to not only
classify but in addition also visualize product classification by using a portfolio matrix
(i.e. BCG-matrix). However, none of the available classification algorithms support
visualization of data. Hence, during this research, a new classification algorithm was
proposed. The algorithm was applied on datasets which combined association rule
mining metrics with the SalesVolumeInKG-metric. By plotting the products with which
the product under analysis is most strongly associated, one is able to classify and
visualize the product under analysis on a portfolio matrix. The results are promising
with regards to classification and visualization possibilities. However, more refinement
and testing of the algorithm is needed.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
SO made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of
data; has been involved in drafting the manuscript. MS supervised the research; made substantial contributions to
conception and design of data analysis and interpretation of data; has been involved in revising the manuscript
critically for important intellectual content and co-authored the paper. RH secondary advisor of the research; has been
involved in revising the manuscript critically for important intellectual content and co-authored the paper. All authors
read and approved the final manuscript.
Otten et al. Decision Analytics (2015) 2:4 Page 23 of 25
24. Author details
1
Department of Information and Computer Sciences, Utrecht University, Princetonplein 5, Utrecht, The Netherlands.
2
Faculty of Management, Science and Technology, Open University, Valkenburgerweg 177, Heerlen, The Netherlands.
Received: 23 March 2015 Accepted: 11 May 2015
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