netrashetty
Netra Shetty
Bristol-Myers Squibb (NYSE: BMY), often referred to as BMS, is a pharmaceutical company, headquartered in New York City. The company was formed in 1989, following the merger of its predecessors Bristol-Myers and the Squibb Corporation. Squibb was founded in 1858 by Edward Robinson Squibb in Brooklyn, New York, while Bristol-Myers was founded in 1887 by William McLaren Bristol and John Ripley Myers in Clinton, New York (both were graduates of Hamilton College).
Lamberto Andreotti became the company's CEO on May 4, 2010. Former CEO James M. Cornelius remains chairman of the Board of Directors.
Bristol-Myers Squibb manufactures prescription pharmaceuticals in several therapeutic areas, including cancer, HIV/AIDS, cardiovascular disease, diabetes, hepatitis, rheumatoid arthritis and psychiatric disorders. Its mission is to "discover, develop and deliver innovative medicines that help patients prevail over serious diseases."
BMS' primary R&D sites are located in Princeton, New Jersey (formerly Squibb) and Wallingford, Connecticut (formerly Bristol-Myers), with other sites in Hopewell and New Brunswick, New Jersey, and in Braine-l'Alleud, Belgium, and Tokyo.
A major restructuring involves focusing on the pharmaceutical business and biologic products along with productivity initiatives and cost-cutting and streamlining business operations through a multi-year program of on-going layoffs. As another cost-cutting measure Bristol-Myers also reduced subsidies for health-care to retirees and plans to freeze their pension plan at the end of 2009.[citation needed]
In November 2009, Bristol-Myers Squibb announced that it was "splitting off" Mead Johnson Nutrition by offering BMY shareholders the opportunity to exchange their stock for shares in Mead Johnson. According to Bristol-Myers Squibb, this move is expected to further sharpen the company's focus on biopharmaceuticals.
Operations research achieved acclaim during World War II as a multidiscipline, scientific approach to solve war-related operational problems. An operations research team might be made up of a psychologist, a medical doctor, a mathematician, and a historian, for example.
Operations research investigations followed rigorous scientific protocols and used mathematical concepts and methods. One famous story of operations research success during the war involved an analysis of Allied bombers returning from bombing missions over Europe. The military analyzed the location of shrapnel damage and bullet holes in returning bombers, to identify where to place additional armor on aircraft.
Operations researchers were brought in at the last minute to do a “confirmatory” analysis, but they recommended that additional armor be placed on bombers everywhere except the places with damage or bullet holes! The operations researchers realized that analyzing damage to returning bombers involved a sampling error: It was the bombers that did not return that needed extra protection -- and they needed it in the most vulnerable places (the places not damaged on the returning bombers).
The power of this multidiscipline, scientific attack on problems was proven again and again during the war. After the war, the promise and practice of operations research moved into industry. Ford Motor Co. hired 10 young U.S. Army Air Force officers to bring advanced operations methods to Ford. This group, led by Robert McNamara (later U.S. Secretary of Defense), soon earned the title of “Whiz Kids’’ within Ford. This team transformed the managerial systems and methods at Ford and helped publicize the benefits of operations research and quantitative analysis.
During the 1950s and 1960s operations research (and management science, a synonymous term) methods spread rapidly throughout U.S. industry, primarily in very large corporations. In the 1980s and 1990s operations research and management science (OR/MS) continued to grow, fueled by smaller, more powerful computers, the increasing availability of relatively low-cost software, and the profusion of analytical methods and models.
However, despite the great promise of advanced quantitative methods, the ultimate potential of OR/MS methods has never been fully realized. Some of the reasons are corporate budget cutting over the years, lack of senior management understanding and support, and corporate emphasis on short-term tactical decisions over long-term optimal solutions, The utilization of OR/MS methods sinks to its nadir in the marketing domain, despite the development in recent decades of a branch of OR/MS devoted to marketing (i.e., marketing science).
Before exploring the application of OR/MS to marketing, some definition and explanation might be useful. Most analysts define OR/MS to mean the application of the scientific method and advanced analytics to the solution of business problems. OR/MS almost always involves building a mathematical model of some business process or system. There is an objective function; that is, a mathematical definition of the object or thing to be optimized (to maximize profits or sales revenue, or minimize costs, typically). Mathematical formulae are developed to define the relationships among the variables. Algorithms and heuristics are used to seek optimal solutions.
There are probabilities and probability distributions of relevant events. Stochastic processes (or random variations) are incorporated into these models, and often constraints or limits are imposed on some variables and/or solutions. Virtually all OR/MS methods can be characterized as optimization techniques, and many involve simulation methods. The goal is to find optimal solutions, given a set of variables, constraints and probabilities.
OR/MS offers a varied and robust analytical toolkit. Some of the widely used OR/MS techniques include linear programming, nonlinear programming, dynamic programming, integer programming, Markov chain analyses, structural equation modeling, Monte Carlo simulations, network flow models, transportation models, inventory models, decision tree analyses, queuing theory, game theory, and Bayesian statistics. These models and methods can answer profound marketing questions. Some examples:
1.Optimal Restaurant Density.
Let’s suppose a restaurant chain (or some other type of retail chain) would like to know “the number of units (retail stores) to build in a particular DMA (designated marketing area) to maximize return on total investments within that DMA.’’ At first this might seem like a simple, straightforward task, but an optimization model would need to consider the following variables across DMAs:
a. Warehousing, distribution and supply chain costs
b. Managerial efficiency, overhead and related costs
c. Operating costs (labor, utilities, taxes, etc.)
d. Advertising efficiencies (the more restaurants, the bigger the ad budget)
e. Media advertising costs
f. Positioning, marketing strategy, and advertising themes and messages
g. Promotion efficiencies (the more restaurants, the bigger the budget)
h. The breadth and type of menu
i. The size and seating capacity of each restaurant and unit sales
j. DMA economic variables (employment, discretionary income, etc.)
k. Competitive variables (number and mix of competitive restaurants)
l. Demographic variables and trends
m. Pricing power and price elasticity
n. Real estate and construction costs
o. Employee training and sharing efficiencies among the restaurants
p. Liquor laws and liquor consumption
As the number of possible variables above suggests, deriving the maximum return on investment (ROI) solution for a given DMA is complicated. The relevant variables must be identified and quantified. All of the data must be organized into a pristine analytical database, across multiple DMAs, with several years of historical data. This data must be analyzed to derive formulae and build algorithms, and then the ultimate model must be assembled, calibrated, tested, and applied to determine the number of units that would maximize return on total investment for each DMA.
In the example above, a nonlinear integer programming optimization model with stochastic and dynamic components would most likely be recommended, but many other quantitative approaches are available. Once implemented, such a model could add millions of dollars to the bottom line of a major restaurant chain (or other type of retail chain).
The fifth stage, prototype development, is the first stage where new product costs begin to escalate. Because of this, many companies have placed greater emphasis on the first four stages and reduced the proportion of new products that reach the prototype stage from about 50 percent to around 20 percent. At this stage the concept is converted into an actual product. A customer value perspective during this phase means the product is designed to satisfy the needs expressed by consumers. Firms may use quality function deployment (QFD) as they develop the prototype. QFD links specific consumer requirements such as versatility, durability, and low maintenance with specific product characteristics (for example, adjustable shelves, a door-mounted ice and water dispenser, and touch controls for a refrigerator). The customer value perspective requires the new product to satisfy customer needs and meet desired quality levels at specified production costs.
Test marketing tests the prototype and marketing strategy in simulated or actual market situations. Because of the expense and risks associated with actual test markets, marketers use them with caution. Products that test poorly are pulled back and reconceptualized or discarded.
Commercialization, the final stage, is when the product is introduced full scale. The level of investment and risk are highest at this stage. Consumer adoption rates, timing decisions for introduction, and coordinating efforts with production, distribution, and marketing should be considered.
FACTORS INFLUENCING NEW
PRODUCT DEVELOPMENT
The seven-step process assumes a definite beginning and end. However, studies suggest that what goes on before and after new products are introduced is as important as the process itself. Organizational structure, leadership, and team building influence the speed and efficiency with which new products are introduced. Structure influences efficiency, autonomy, and coordination. New product innovation requires structure that optimizes direction and guidance. Structure that facilitates internal information exchange, decision making, and materials flow is essential. A "fast-cycle" structure allows more time for planning and implementing activities to gain competitive advantage. This type of structure also cuts costs because production materials and information collect less overhead and do not accumulate as work-in-process inventory. Autonomy refers to the amount of decision making allowed at lower levels of management. The coordination of the engineering, product design, manufacturing, and marketing functions in the new product development process is vital.
Leadership influences strategy, culture, and the firm's overall ability to undertake new product development. Top management can demonstrate involvement in the development process by providing career advancement for entrepreneurial skills and encouraging broad employee participation. Clarity and vision are crucial to ensuring that new product ideas are good strategic fits for the company. The degree to which leadership allows trial and error and promotes individual initiative positively influences the development of new products. This acceptance of risk and support for an entrepreneurial spirit within the organization are crucial in order for innovation to flourish. New products emerge in a variety of ways and their development does not always proceed in rational and consistent manners. It is necessary for leadership to view the process as iterative and dynamic, and to foster adaptation and flexibility. Management flexibility and responsiveness to change also are needed. This type of leadership is particularly important to the project manager who must coordinate and integrate the various parts of the new product development process so that a coherent system emerges that produces a product with compelling value. Initiative encourages creativity and problem-solving skills.
Teams provide mechanisms for breaking down functional biases created by a strict adherence to structure. The amount of interdepartmental conflict in the organization, the social cohesion among team members, and the frequency and directionality of interdepartmental communication influence team building. Through shared understanding of the objectives and purposes of the project, as well as the tasks required in the development process, teams can shape the project and influence how work gets done in the organization.
Lamberto Andreotti became the company's CEO on May 4, 2010. Former CEO James M. Cornelius remains chairman of the Board of Directors.
Bristol-Myers Squibb manufactures prescription pharmaceuticals in several therapeutic areas, including cancer, HIV/AIDS, cardiovascular disease, diabetes, hepatitis, rheumatoid arthritis and psychiatric disorders. Its mission is to "discover, develop and deliver innovative medicines that help patients prevail over serious diseases."
BMS' primary R&D sites are located in Princeton, New Jersey (formerly Squibb) and Wallingford, Connecticut (formerly Bristol-Myers), with other sites in Hopewell and New Brunswick, New Jersey, and in Braine-l'Alleud, Belgium, and Tokyo.
A major restructuring involves focusing on the pharmaceutical business and biologic products along with productivity initiatives and cost-cutting and streamlining business operations through a multi-year program of on-going layoffs. As another cost-cutting measure Bristol-Myers also reduced subsidies for health-care to retirees and plans to freeze their pension plan at the end of 2009.[citation needed]
In November 2009, Bristol-Myers Squibb announced that it was "splitting off" Mead Johnson Nutrition by offering BMY shareholders the opportunity to exchange their stock for shares in Mead Johnson. According to Bristol-Myers Squibb, this move is expected to further sharpen the company's focus on biopharmaceuticals.
Operations research achieved acclaim during World War II as a multidiscipline, scientific approach to solve war-related operational problems. An operations research team might be made up of a psychologist, a medical doctor, a mathematician, and a historian, for example.
Operations research investigations followed rigorous scientific protocols and used mathematical concepts and methods. One famous story of operations research success during the war involved an analysis of Allied bombers returning from bombing missions over Europe. The military analyzed the location of shrapnel damage and bullet holes in returning bombers, to identify where to place additional armor on aircraft.
Operations researchers were brought in at the last minute to do a “confirmatory” analysis, but they recommended that additional armor be placed on bombers everywhere except the places with damage or bullet holes! The operations researchers realized that analyzing damage to returning bombers involved a sampling error: It was the bombers that did not return that needed extra protection -- and they needed it in the most vulnerable places (the places not damaged on the returning bombers).
The power of this multidiscipline, scientific attack on problems was proven again and again during the war. After the war, the promise and practice of operations research moved into industry. Ford Motor Co. hired 10 young U.S. Army Air Force officers to bring advanced operations methods to Ford. This group, led by Robert McNamara (later U.S. Secretary of Defense), soon earned the title of “Whiz Kids’’ within Ford. This team transformed the managerial systems and methods at Ford and helped publicize the benefits of operations research and quantitative analysis.
During the 1950s and 1960s operations research (and management science, a synonymous term) methods spread rapidly throughout U.S. industry, primarily in very large corporations. In the 1980s and 1990s operations research and management science (OR/MS) continued to grow, fueled by smaller, more powerful computers, the increasing availability of relatively low-cost software, and the profusion of analytical methods and models.
However, despite the great promise of advanced quantitative methods, the ultimate potential of OR/MS methods has never been fully realized. Some of the reasons are corporate budget cutting over the years, lack of senior management understanding and support, and corporate emphasis on short-term tactical decisions over long-term optimal solutions, The utilization of OR/MS methods sinks to its nadir in the marketing domain, despite the development in recent decades of a branch of OR/MS devoted to marketing (i.e., marketing science).
Before exploring the application of OR/MS to marketing, some definition and explanation might be useful. Most analysts define OR/MS to mean the application of the scientific method and advanced analytics to the solution of business problems. OR/MS almost always involves building a mathematical model of some business process or system. There is an objective function; that is, a mathematical definition of the object or thing to be optimized (to maximize profits or sales revenue, or minimize costs, typically). Mathematical formulae are developed to define the relationships among the variables. Algorithms and heuristics are used to seek optimal solutions.
There are probabilities and probability distributions of relevant events. Stochastic processes (or random variations) are incorporated into these models, and often constraints or limits are imposed on some variables and/or solutions. Virtually all OR/MS methods can be characterized as optimization techniques, and many involve simulation methods. The goal is to find optimal solutions, given a set of variables, constraints and probabilities.
OR/MS offers a varied and robust analytical toolkit. Some of the widely used OR/MS techniques include linear programming, nonlinear programming, dynamic programming, integer programming, Markov chain analyses, structural equation modeling, Monte Carlo simulations, network flow models, transportation models, inventory models, decision tree analyses, queuing theory, game theory, and Bayesian statistics. These models and methods can answer profound marketing questions. Some examples:
1.Optimal Restaurant Density.
Let’s suppose a restaurant chain (or some other type of retail chain) would like to know “the number of units (retail stores) to build in a particular DMA (designated marketing area) to maximize return on total investments within that DMA.’’ At first this might seem like a simple, straightforward task, but an optimization model would need to consider the following variables across DMAs:
a. Warehousing, distribution and supply chain costs
b. Managerial efficiency, overhead and related costs
c. Operating costs (labor, utilities, taxes, etc.)
d. Advertising efficiencies (the more restaurants, the bigger the ad budget)
e. Media advertising costs
f. Positioning, marketing strategy, and advertising themes and messages
g. Promotion efficiencies (the more restaurants, the bigger the budget)
h. The breadth and type of menu
i. The size and seating capacity of each restaurant and unit sales
j. DMA economic variables (employment, discretionary income, etc.)
k. Competitive variables (number and mix of competitive restaurants)
l. Demographic variables and trends
m. Pricing power and price elasticity
n. Real estate and construction costs
o. Employee training and sharing efficiencies among the restaurants
p. Liquor laws and liquor consumption
As the number of possible variables above suggests, deriving the maximum return on investment (ROI) solution for a given DMA is complicated. The relevant variables must be identified and quantified. All of the data must be organized into a pristine analytical database, across multiple DMAs, with several years of historical data. This data must be analyzed to derive formulae and build algorithms, and then the ultimate model must be assembled, calibrated, tested, and applied to determine the number of units that would maximize return on total investment for each DMA.
In the example above, a nonlinear integer programming optimization model with stochastic and dynamic components would most likely be recommended, but many other quantitative approaches are available. Once implemented, such a model could add millions of dollars to the bottom line of a major restaurant chain (or other type of retail chain).
The fifth stage, prototype development, is the first stage where new product costs begin to escalate. Because of this, many companies have placed greater emphasis on the first four stages and reduced the proportion of new products that reach the prototype stage from about 50 percent to around 20 percent. At this stage the concept is converted into an actual product. A customer value perspective during this phase means the product is designed to satisfy the needs expressed by consumers. Firms may use quality function deployment (QFD) as they develop the prototype. QFD links specific consumer requirements such as versatility, durability, and low maintenance with specific product characteristics (for example, adjustable shelves, a door-mounted ice and water dispenser, and touch controls for a refrigerator). The customer value perspective requires the new product to satisfy customer needs and meet desired quality levels at specified production costs.
Test marketing tests the prototype and marketing strategy in simulated or actual market situations. Because of the expense and risks associated with actual test markets, marketers use them with caution. Products that test poorly are pulled back and reconceptualized or discarded.
Commercialization, the final stage, is when the product is introduced full scale. The level of investment and risk are highest at this stage. Consumer adoption rates, timing decisions for introduction, and coordinating efforts with production, distribution, and marketing should be considered.
FACTORS INFLUENCING NEW
PRODUCT DEVELOPMENT
The seven-step process assumes a definite beginning and end. However, studies suggest that what goes on before and after new products are introduced is as important as the process itself. Organizational structure, leadership, and team building influence the speed and efficiency with which new products are introduced. Structure influences efficiency, autonomy, and coordination. New product innovation requires structure that optimizes direction and guidance. Structure that facilitates internal information exchange, decision making, and materials flow is essential. A "fast-cycle" structure allows more time for planning and implementing activities to gain competitive advantage. This type of structure also cuts costs because production materials and information collect less overhead and do not accumulate as work-in-process inventory. Autonomy refers to the amount of decision making allowed at lower levels of management. The coordination of the engineering, product design, manufacturing, and marketing functions in the new product development process is vital.
Leadership influences strategy, culture, and the firm's overall ability to undertake new product development. Top management can demonstrate involvement in the development process by providing career advancement for entrepreneurial skills and encouraging broad employee participation. Clarity and vision are crucial to ensuring that new product ideas are good strategic fits for the company. The degree to which leadership allows trial and error and promotes individual initiative positively influences the development of new products. This acceptance of risk and support for an entrepreneurial spirit within the organization are crucial in order for innovation to flourish. New products emerge in a variety of ways and their development does not always proceed in rational and consistent manners. It is necessary for leadership to view the process as iterative and dynamic, and to foster adaptation and flexibility. Management flexibility and responsiveness to change also are needed. This type of leadership is particularly important to the project manager who must coordinate and integrate the various parts of the new product development process so that a coherent system emerges that produces a product with compelling value. Initiative encourages creativity and problem-solving skills.
Teams provide mechanisms for breaking down functional biases created by a strict adherence to structure. The amount of interdepartmental conflict in the organization, the social cohesion among team members, and the frequency and directionality of interdepartmental communication influence team building. Through shared understanding of the objectives and purposes of the project, as well as the tasks required in the development process, teams can shape the project and influence how work gets done in the organization.
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