Product Description
As optimization techniques have developed, a gap has arisen between the people devising the methods and the people who actually need to use them. Research into methods is necessarily long-term and located usually in academic establishments; whereas the application of an optimization technique, normally in an industrial environment, has to be justified financially in the short term. The gap is probably inevitable; but there is no need for textbooks to reflect it. Tea… More >>

Optimization in Industry: Optimization techniques

Product Description
Introduces recent advances and developments in iterative methods for solving Markovian queing and manufacturing problems, presenting future directions for research as well. Features numerous exercises and fully-worked examples. For students, practitioners and researchers in applied mathematics, scientific computing and operational research. DLC: Queing Theory. … More >>

Iterative Methods for Queuing and Manufacturing Systems

View full post on ScienceDaily: Mathematical Modeling News

View full post on ScienceDaily: Mathematical Modeling News

Difference between business intelligence and business performance management.

• BPM (Business performance management) contains the concept of a control or feedback loop that helps guide the business towards its goals.
• BI (Business intelligence) may provide the analytics to help the business set those goals and to monitor progress towards them.

BPM enhances processes by creating better feedback loops. Continuous and real-time reviews can help to identify and eliminate problems before they grow. BPM’s forecasting abilities help companies take corrective action in time to meet earning projections. Forecasting is characterized by a high degree of predictability which is put into good use to answer what-if scenarios.

BPM can help in risk analysis and in predicting outcomes of merger and acquisition scenarios and in planning to overcome potential problems.

Methodologies

Methodologies for implementing BPM.

• Six sigma
• Balanced scorecard
• Activity based costing (ABC)
• Total Quality management (TQM)
• Integrated strategic measurement

Areas where BPM is used

1. customer-related numbers:
   1. new customers acquired
   2. status of existing customers
   3. attrition of customers (including breakup by reason for attrition)
2. Turnover generated by segments of the customers – possibly using demographic filters
3. Outstanding balances held by segments of customers and terms of payment – possibly using demographic filters
4. Collection of bad debts within customer relationships
5. Demographic analysis of individuals (potential customers) applying to become customers, and the levels of approval, rejections and pending numbers
6. Delinquency analysis of customers behind on payments
7. Profitability of customers by demographic segments and segmentation of customers by profitability.
8. Campaign management
9. Real-time dashboard on key operational metrics
   1. Overall equipment effectiveness
10. Click stream analysis on a website
11. key product portfolio trackers
12. Marketing channel analysis
13. Sales data analysis by product segments
14. Call center metrics

 Strategic Solutions used for effective implementation of BPM

• OLAP — online analytical processing,   
• Score card, dash board and data visualization
• Data warehouses
• Document warehouses
• Text mining
• DM — data mining
• BPO — business performance optimization
• EIS — executive information systems
• DSS — decision support systems
• MIS — management information systems

 Challenges when implementing a BPM program

• Goal-alignment queries: one must first determine the short- and medium-term purpose of the program. What strategic goal(s) of the organization will be addressed by the program? What organizational mission/vision does it relate to? A hypothesis needs to be crafted that details how this initiative will eventually improve results / performance.
• Baseline queries: current information-gathering competency needs assessing. Do we have the capability to monitor important sources of information? What data is being collected and how is it being stored? What are the statistical parameters of this data, e.g., how much random variation does it contain? Is this being measured?
• Cost and risk queries: someone should estimate the financial consequences of a new BI initiative. It is necessary to assess the cost of the present operations and the increase in costs associated with the BPM initiative. What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning.
• Customer and stakeholder queries: determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers / stakeholders will benefit directly from this initiative? Who will benefit indirectly? What quantitative / qualitative benefits follow? Is the specified initiative the best way to increase satisfaction for all kinds of customers, or is there a better way? How will customer benefits be monitored? What about employees, shareholders, and distribution channel members?
• Metrics-related queries: information requirements need operationalization into clearly defined metrics. One must decide what metrics to use for each piece of information being gathered. Are these the best metrics? How do we know that? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available?
• Measurement methodology-related queries: one should establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. What methods will be used, and how frequently will data be collected? Are there any industry standards for this? Is this the best way to do the measurements? How do we know that?
• Results-related queries: someone should monitor the BPM program to ensure that it meets objectives. Adjustments in the program may be necessary. The program should be tested for accuracy, reliability, and validity. How can it be demonstrated that the BI initiative, and not something else, contributed to a change in results? How much of the change was probably random?

Product Description
Integer programming (discrete optimization) is best used for solving problems involving discrete, whole elements. Using integer variables, one can model logical requirements, fixed costs, sequencing and scheduling requirements, and many other problem aspects. Whether it’s taught in OR graduate programs or in math or computer science programs; in courses called “Integer Programming,” “Combinatorial Optimization,” “Combinatorial Optimization and Integer Programming”… More >>

Logic and Integer Programming

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In real-world problems related to finance, business, and management, mathematicians and economists frequently encounter optimization problems. In this classic book, George Dantzig looks at a wealth of examples and develops linear programming methods for their solutions. He begins by introducing the basic theory of linear inequalities and describes the powerful simplex method used to solve them. Treatments of the price concept, the transportation problem, and matrix … More >>

Linear Programming and Extensions

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The methodological development of integer programming has grown by leaps and bounds in the past four decades, with its main focus on linear integer programming. However, the past few years have also witnessed certain promising theoretical and methodological achievements in nonlinear integer programming. These recent developments have produced applications of nonlinear (mixed) integer programming across a variety of various areas of scientific computing, engineering,… More >>

Nonlinear Integer Programming

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Deals with the estimation of natural resources using a Monte Carlo methodology. Includes a set of tools to describe the morphological, statistical and stereological properties of spatial random models. Discusses the geostatistical simulation techniques in such a specific way. … More >>

Geostatistical Simulation: Models and Algorithms