Perspectives on Various Issues of Interest

Do the Math

ROMANCE MATHEMATICS

Smart man + smart woman = romance
Smart man + dumb woman = affair
Dumb man + smart woman = marriage
Dumb man + dumb woman = pregnancy


OFFICE ARITHMETIC

Smart boss + smart employee = profit
Smart boss + dumb employee = production
Dumb boss + smart employee = promotion
Dumb boss + dumb employee = overtime


SHOPPING MATH

A man will pay $2 for a $1 item he needs.
A woman will pay $1 for a $2 item that she doesn't need.


GENERAL EQUATIONS & STATISTICS

A woman worries about the future until she gets a husband.
A man never worries about the future until he gets a wife.
A successful man is one who makes more money than his wife can spend.
A successful woman is one who can find such a man.


HAPPINESS

To be happy with a man, you must understand him a lot and love him a little.
To be happy with a woman, you must love her a lot and not try to understand her at all.


LONGEVITY

Married men live longer than single men, but married men are a lot more willing to die.


PROPENSITY TO CHANGE

A woman marries a man expecting he will change, but he doesn't.
A man marries a woman expecting that she won't change, and she does.


DISCUSSION TECHNIQUE

A woman has the last word in any argument. Anything a man says after that is the beginning of a new argument.

Wait Times at Hospital's Emergency Rooms

An article published in Wall Street Journal reported that emergency-room patients are waiting ever longer to see a doctor, a potentially dangerous development as rising numbers of uninsured and underinsured Americans turn to ERs for medical care, researchers said. The median wait for adults rose to about 30 minutes in 2004 -- meaning half waited more and half waited less -- from 22 minutes in 1997, a 36% increase, according to a study scheduled for publication today by the journal Health Affairs. Heart-attack patients, meanwhile, had a median wait time of 20 minutes in 2004, up 150% from eight minutes in 1997.

Black, Hispanic and urban patients can expect to spend more time in waiting rooms than rural or white patients, according to the study, which was conducted by seven doctors affiliated with Harvard University and the Cambridge Health Alliance.

The study "is not surprising; it's worrisome," said Robert A. Lowe, a physician and director of the Oregon Health & Science University's Center for Policy & Research in Emergency Medicine, who wasn't involved in the research. "What it reflects is that as access to care outside the emergency department deteriorates, the emergency department is one of the few safety nets through which everyone has access to care." An increase in the number of ER visits, plus a decline in the number of emergency rooms, likely has contributed to the wait, researchers said.

Andrew Wilper, an internist and lead author of the study, said it isn't clear why wait times increased more rapidly for heart-attack patients. As fewer ERs were left to treat very sick patients -- including trauma victims -- "perhaps heart-attack patients became relatively less urgent," he said.

Not all emergency-room waits were equal. While white patients typically waited about 24 minutes to see a doctor during the years studied, black and Hispanic patients could expect to wait slightly more than a half hour. In addition, women could expect to wait about 5% longer than men, even under similar circumstances.

Disparities remained when researchers adjusted for other circumstances, such as a patient's age or a hospital's location. The researchers examined whether race and sex affected triage decisions -- addressing how quickly patients need a doctor's attention -- and found no evidence for it. The disparity could be the result of "little decisions along the way" by other ER staff, Dr. Wilper said, or else hospitals that serve minorities may have longer wait times generally.

Among the study's other findings: Urban hospitals had double the wait times of rural ones; the Northeast's were longer than other regions'; and nonprofit-hospital visitors waited about 13% longer than those at privately owned facilities.

Source: Francis, Theo, “Wait Times Lengthen at Emergency Rooms,” Wall Street Journal, January 18, 2008.

The Issue of Readmissions in Hospitals

Hospitals are taking steps to prevent the most common risk to patients after discharge: landing back in the hospital due to complications that could have been prevented with better follow-up care.

A revolving door of readmissions is driving up costs for hospitals and causing needless harm to patients, especially elderly people with multiple chronic diseases. Nearly 18% of Medicare patients admitted to a hospital are readmitted within 30 days of discharge, accounting for $15 billion in spending, according to the Medicare Payment Advisory Commission, the independent federal body that advises Congress on Medicare. As a result, readmission rates are coming under increasing scrutiny from regulators, insurers, employers and quality-measurement groups, who are considering methods to tie payment to lower readmissions.

"We have to start paying attention to people's needs beyond the hospital door," says Mary Naylor, a professor at the University of Pennsylvania's School of Nursing. She has conducted a number of clinical trials on a model to help older adults with complex care needs after they are discharged. "The experience of multiple hospitalizations can take a devastating toll on the human psyche and the quality of life for patients and their caregivers," she says.

There are about five million readmissions a year in U.S. hospitals, with approximately a third occurring within 90 days of discharge, according to the Institute for Healthcare Improvement, a Boston-based nonprofit. But with so-called transitional-care programs, which follow patients for varying periods of time at home, as many as 46% of readmissions could be prevented, says Pat Rutherford, an IHI vice president.

The institute is working with hospitals to reduce readmissions. Its programs include: identifying patients at risk for return, scheduling follow-up doctor's appointments before patients are discharged, sending nurses to patients' homes within a few days of discharge, monitoring patients at home, and educating patients and families on how to adhere to medication schedules and self-care regimens. Part of the problem is that hospitals aren't paid to coordinate care once a patient leaves. But that may change: Large managed-care groups and insurers are now experimenting with programs to cover such services.

After patients who may be at high risk for readmission are discharged from Kaiser's San Francisco Medical Center, nurses visit them -- cutting through red tape, if need be, to get them quickly into doctors' appointments. "We form trusting relationships with the patient, and we know what to look for and what could get them back in the hospital," says Jill Murray, a nurse in the transitional-care program.

Source: Landro, Laura,  “Keeping Patients from Landing Back in Hosptal,” Wall Street Journal, December 12, 2007.

Academic Leader Program - Guadalajara, Mexico

Last October (October 10th, 2008), as a part of the Academic Leader program organized by Tecnologico de Monterrey, Guadalajara, I made a presentation to forty managers and executives of some of the leading organizations, such as PepsiCo, Sanmina-SCI, Foxconn and IBM. A brief report of the academic leader program was created by Tec de Monterrey, Guadalajara.

Overall, I discussed the critical issues associated with forward and reverse flow of products, services, information and funds as a part of efficient and responsive supply chain management. I highlighted that given the increase in the price of oil (at the time of the presentation), the savings in cost to manufacture in China as compared to Mexico has fallen from 35% to nearly 16% due to the increased transportation costs. Notwithstanding the fluctuation in oil price, the geographical proximity to the U.S. market presents inimitable opportunities for Mexico. I emphasized that it is important to take advantage of this opportunity by investing in technology and at the same time by developing talent (i.e. skilled workforce) in the areas of engineering, design, technology, manufacturing and supply chain management.

Selling Recalled Product in the Local Market

An article published on January 3, 2008 in Wall Street Journal reported that in the U.S. and Europe, a wave of recalls of Chinese-made goods over the second half of 2007 led to heavy news coverage and a coordinated effort by regulators and the toy industry to pull dangerous products off the shelves. In China, it's a different story. The nation has no comprehensive rules governing recalls and no system for tracking injuries from defective products. Together with a thriving trade in black-market products, that means some of the goods that have caused alarm in the West are still available to Chinese consumers.

"For low-value goods, such as many of the toys involved in recent recalls, a foreign toy company may have little incentive to monitor production on-site," said Soonhong Min, a professor of marketing and supply chain management at the University of Oklahoma's Price College of Business. "After a recall, the [factory that made the toy for the foreign company will] try its best to recover money by distributing the defective products in the local market. That is nothing new."

In August, China launched a four-month product-safety initiative, including new regulations dealing specifically with food and toy recalls. The government is currently considering comprehensive product-safety legislation that would include establishing a database for recalled products and a system for tracing products and their components through the supply chain.

"We want to make the recall of defective products more systematic and more institutionalized," said Liu Zhaobin, an official at China's top product-quality watchdog, the General Administration of Quality Supervision, Inspection and Quarantine, or Aqsiq. But he adds that "businesses need to take more social responsibility."

Companies like Mattel and Mega Brands hire third-party companies to destroy recalled goods, either at the factory or after they are collected. But "unless you're very diligent about destroying the toys, the toys could re-enter the market," said Frederick Felman, chief marketing officer for MarkMonitor.

Mattel depended on its Chinese distributor, Midway Trading Co., to inform over 1,000 subdistributors about the recalls. Mattel said it made other efforts to inform consumers about recalls, including sending out a statement, publishing recall information online and translating documents into Chinese.

Some sellers of Mattel toys said they received incomplete information about recalls.

Source: Spencer, Jane and Canaves, Sky and Casey, Nicholas,  “Toys Recalled in the U.S. are Still for Sale in China,” Wall Street Journal, January 3, 2008.

The Integrated Role of Airlines and Airports on Delays

An article published on January 22, 2008 in Wall Street Journal reported that analysis of extreme-delay data from last year shows clear patterns: Certain airlines and certain airports were more prone than others to long delays before takeoff and very late arrivals. Even certain flights had repeated trouble with very long delays.

Airlines say some flights simply end up at the end of long lines more often than others because they try to depart at the airport's busiest time. But it can be more than just unfortunate timing. Airline operations managers can juggle departures because of factors like which crews may run out of duty time first, which planes are more urgently needed for their next scheduled flight, which flights have the most high-dollar business travelers who they least want to be canceled, and which flights are headed to destinations with late-night curfews -- where airlines can be fined for flights arriving after a certain hour.

Another factor that affects extremely long sitting: Airlines are less likely to cancel flights that move on to other hubs in their systems rather than turn around and go back to the same airport they just left. If a plane is going out and back to the same hub, canceling the trip just impacts those two flights. But if the aircraft is scheduled to move on to numerous cities, canceling that flight could disrupt many more customers. As a result, aircraft that aren't scheduled for "turns" are more likely to sit and wait long periods before the flight gets canceled.

Extreme delays are rare, but the problem escalated dramatically last year, becoming the focus of Congressional hearings and legislation. The number of flights with taxi-out times of more than three hours totaled 1,598 for the first 11 months of 2007, according to the Bureau of Transportation Statistics. (The agency hasn't released December data yet.) That's a 23.4% increase over the entire year of 2006, and December 2007 clearly added to the total for 2007 with widespread delays over the holiday season.

Airlines with highest percentage in 2007 of flights delayed 45 minutes or more: Atlantic Southeast: 15.1%, JetBlue: 13.8%, American: 13.6%.

Airlines with the lowest percentage of flights delayed 45 minutes or more: Hawaiian: 1.69%, Frontier: 6.10%, Southwest: 6.13%.

Highest average length of delays: JetBlue: 67 minutes, Mesa: 64 minutes, ExpressJet: 64 minutes, Atlantic Southeast: 62 minutes, American: 61 minutes

Lowest average delay: Frontier: 43 minutes, Southwest: 48 minutes, Northwest: 49 minutes, US Airways: 49 minutes, Hawaiian: 50 minutes.

Source: McCartney, Scott, “Extreme Delays: Which Flights Are,” Wall Street Journal, January 22,2008.

Innovation Oriented Operations Strategy

Industry growth and technological change interact to create alternative environments with varying levels of dynamism and complexity requiring realignment of operations strategy. My co-author and I investigate the increasing rate of technological change and emphasize an urgent need to include innovation as a competitive priority (along with cost, quality, delivery and flexibility) to proactively adapt operations strategy to fit changing environment.  It is also necessary for managers to ensure a fit between their competitive priorities and the development of supporting structures/infrastructures to ensure effective implementation of competitive operations strategy.

 An industry ecosystem requires that competitors find viable business models and strategies that can effectively reach new markets and address evolving customer needs. The interaction between industry lifecycle and rate of technological change requires that operations managers constantly adapt their operations strategy to evolving industrial ecosystems.  Innovation is an important competitive priority to transform operations strategy and capabilities and to adapt to the requirements of new ecosystems. In this paper, we present a conceptual framework that links the elements of operations strategy with four industry ecosystems. Specifically, we highlight the need for competitive priorities, structural choices and infrastructural choices that fit a specific quadrant of industry ecosystem.

We contend that a firm must manage the transformation of its operations strategy as industry life cycles and rates of technology change. We present a stage-based model for operations strategy that allows it to transform from one industrial ecosystem to another. The proposed framework in this study provides one blueprint for a more proactive role for operations management in helping an organization adapt to changing industrial ecosystems.

The stage-based typology presented in the paper should aid practice in adapting to different industry ecosystems.  The model presents clear directions in terms of how capabilities need to be developed in order to maintain a strategic fit throughout the evolution of industry and technology contexts. The innovation competitive priority is essential for such adaptations. In particular, when managers recognize innovation as an element of operations strategy, they can devise methods to proactively contribute towards an organization’s competitive future by managing the fit between various competitive priorities, structural choices and infrastructural choices. In sum, this theoretical study adds to the rich literature on operations strategy and highlights the need for explicit inclusion of innovation as a proactive priority for transforming operations strategy.

 

STAGE-BASED MODEL

Source: Nair, A. and Boulton, W. R. 2008. Innovation Oriented Operations Strategy Typology and Stage-based Model.  International Journal of Operations & Production Management, Vol. 28, Iss. 8, 748-771.

 

Patterns underlying related and unrelated collaborative ventures

Collaborative ventures—both equity-based partnerships as well as project-based alliances—have dominated the international business scene over the past two decades. In a recent study my co-authors and I investigated the patterns of related and unrelated collaborative venture formation. Using a large database of over 90,000 collaborative ventures formed during the 1985–2001 period, this study clusters collaborative ventures on the basis of the industry group and home country relatedness of the collaborating partners. Self-organizing map technique within neural network methodology is used to accomplish this objective. The clusters obtained from the self-organizing map form the basis for developing taxonomy of collaborative ventures in which the neurons underlying clusters are classified based on the country of origin and industry affiliations of the collaborating partners and the collaborative venture. The distinguishing characteristics of the clusters and the taxonomy help augment our current understanding of the formation of collaborative ventures.

Overall, there are seven distinct clusters of collaborative ventures. These clusters constitutes of code-vectors obtained from the self organizing map approach within neural network technique.

Industry Effects

None of the identified clusters have partnering firms and the collaborative ventures all belonging to distinct industries. At the same time we find that none of the clusters have all firms from the same industry sector. However, in all clusters at least two firms (among the two partners and the collaborative venture) belong to the same industry. On further examination, we observe that in one of the clusters (cluster 6) the two collaborating firms belong to the same industry. In the remaining clusters (1, 2, 3, 4, 5, 7), the collaborative venture is formed in the industry of one of the partners. Cluster 1 comprises of collaborative ventures that are in the services industry. Among the code vectors, two have partner firms belonging to the same industry group (high-technology industry), the remaining code vectors have one of the partnering firms in services the other representing either high-technology projects or discrete manufacturing industry group. In cluster 2, the collaborative ventures belong to projects and services industry group. The collaborative ventures in the services industry are formed either by the partners who are both in services industry or when one of the partnering firm is in services industry and the other is in the projects industry group. The collaborative ventures in the projects industry are similarly formed either when both partners are from projects industry or when one of the partners is from projects industry and the other is from the financial industry group.

Cluster 3 represents collaborative ventures in the services and natural resources industry group. While the collaborative venture in the natural resources industry group is formed when both partners are from natural resources industry group; the collaborative venture in the services industry group is formed either when the two partners are from financials industry group or when one is from financial industry group and the other is from the services industry group. In cluster 4, the collaborative ventures belong to projects, high technology and process manufacturing industry groups. In the collaborative venture in projects industry group, either the two partners are from services industry group or one is from services and the other is from financial industry group. High-technology collaborative ventures comprise of two partners either both from financials industry group or both from services industry group. High-technology collaborative ventures are also formed when one partner is from services and the other is from either financials industry group or high technology industry group. The process manufacturing collaborative ventures in this cluster are formed when one of the collaborating partners is from process manufacturing industry group and the other is from either trade or natural resources industry group.

In cluster 5, the collaborative ventures are formed in high technology or discrete manufacturing industry groups. The collaborative venture in high-technology industry group comprise of one collaborating partner from high technology and the other from either financial or services industry group; collaborative venture in discrete manufacturing is formed when one of the collaborating partners is in discrete manufacturing industry group and the other is in the trade industry group. In cluster 6, the collaborating partners come from the same industry group and form a collaborative venture in the same industry group. The cluster consists of process manufacturing and high-technology industry groups. Finally, in cluster 7 the collaborative ventures in process manufacturing and discrete manufacturing are formed when both the collaborating partners also belong to these industry groups; meanwhile the collaborative venture in services industry group is formed by the collaborating partners in discrete manufacturing and services industry groups. We observe that industry groups—consumer goods (industry category code 2), miscellaneous manufacturing (industry category code 6) and other (industry category code 11)—are not captured in any of the code vectors suggesting that a cogent underlying structure involving these industries could not be established.

Home Country Effects

Similar to the observation in industry effects, none of the clusters have collaborating partners and collaborative venture, all belonging to different home countries. However, unlike in industry effects analysis, clusters 2 and 6 have all code vectors with the collaborating partners and the collaborative ventures belonging to the same industry. In cluster 1, the partners are from North America or Japan and the collaborative venture is formed in North America, Japan or is Supranational. Cluster 2 comprises of partnering firms from Australasia, North Asia and North America forming a collaborative venture in their respective regions. In cluster 3, one code vector has collaborating partners from North America and South America with the resulting collaborative venture being formed in South America. The remaining code vectors comprise of collaborative ventures that are formed in the country of origin of the collaborating partners (i.e. Australasia, North America and North Asia).

In cluster 4, similarly, except for two code vectors in which the collaborating partners from Japan form a collaborative venture in North Asia and in which collaborating partners from Australasia and North America form a collaborative venture in Australasia, other code vectors are characterized by the collaborating partners and the collaborative venture belonging to the same country (Australasia, North America and Japan). In cluster 5, one-code vector represent a collaborative venture formed in Australasia that is formed by collaborating partners in North America and Australasia. The other two collaborative ventures are formed in North America and Japan by collaborating partners in the same countries. In cluster 6, the code vectors are characterized by collaborating partners and the collaborative venture all belonging to the same country (North America, Japan and North Asia). Finally, in cluster 7, firms in Western Europe collaborate with firms in North America, North Asia and South America to form collaborative ventures in North America, Western Europe, North Asia and South America.

Source: Nair, A., Hanvanich, S. and Cavusgil, T. 2007. “An exploration of the patterns underlying related and unrelated collaborative ventures using neural network: Empirical investigation of collaborative venture formation in data spanning 1985-2001,” International Business Review, Vol. 16, Iss. 6, 659-686.