M&SOM Review

Household budgets are tightening up and consumers are increasingly price-sensitive. From apparel, consumer electronics, to video games all alike, companies are competing furiously for the shrinking market share. The competitive environment makes both the frequency and level of discounts continue to increase over recent years.

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Consider the following events. In 2008, Dow Chemical Company imposed an unprecedented 20% increase in prices on consumers—the biggest one-time hike in prices in the company’s history till that point in time. Many sugar mills in India and Brazil, the top two sugar producing countries in the world, were forced to shut down in 2014. What is the common link in these (and many other such) examples? The severe distress these companies faced were brought on by fluctuations in commodity prices—increased energy prices in Dow’s case and depressed sugar prices in the case of the sugar mills.

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Increasingly diversified market preferences have driven companies to offer multiple product variations that provide a similar set of basic functions but differ on various dimensions such as features and prices. Different customer segments use the products differently and consequently value them differently. For example, consider the three microprocessor stock keeping units (SKUs) given in the following table.

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Retailers face a range of dilemmas when setting prices for trendy items like consumer electronics gadgets and fashion apparel. Time-sensitive products often have short selling seasons and long supply lead times, so retailers cannot simply wait until supplies run low and then order more. No inventory replenishment is possible during the compressed selling season. In this case, pricing decision often plays a critical role in matching supply with demand. To determine the right price, it requires sufficient information about the underlying demand. Unlike common household products such as food, beverage, personal hygiene products, and home appliances, which have a long product life cycle and hence quite predictable demand, little historical data are available for the latest cameras, smartphones, or designer bags. Instead, store managers must make educated guesses about demand.

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Before a firm starts manufacturing a new product, it wants to have an accurate forecast of weekly demand over the product’s life cycle. Such a forecast is vital for production and logistics planning. Accurate forecasts are even more critical for products with short life cycles (6 to 12 months) and long lead times (6 to 12 weeks)—a common combination for electronics manufactured overseas—because the lead time constitutes a significant portion of the lifespan and many of the logistics and production decisions need to be made far in advance. However, generating such a forecast is difficult. After all, the product is new.

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The value of flexibility has always been recognized. However, flexibility in manufacturing and service operations has gained a renewed interest in practice due to several factors. Flexibility is considered essential in production settings now, as product variety has exploded.

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Can federal regulators nudge companies to reduce emissions of toxic chemicals by publicly disclosing their hazards? The answer appears to be yes, according to the study “Are Hazardous Substance Rankings Effective?” coauthored by Wayne Fu, Basak Kalkanci, and Ravi Subramanian, published online on May 17, 2018, in Manufacturing & Service Operations Management. The study cross-referenced annual federal data on emissions of chemicals with a biennial report that ranks the relative hazards of hundreds of chemicals.

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Throughout history, infectious diseases—those transmitted from human to human or animal to human—have killed millions of people worldwide. Although substantial progress has been made in recent years with improved access to life-saving drugs, enhanced screening programs, and other targeted interventions, the big three—human immunodeficiency virus (HIV), tuberculosis, and malaria—still cause nearly four million deaths every year

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For ride-sharing services like Uber, or Didi in China, getting the right number of drivers on the road as demand fluctuates throughout the day is key to success. Because these platforms rely on independent drivers who show up for work only when the effective earning rate is high, it is important for the platform to coordinate the right number of drivers to meet dynamic customer demand. Having too few drivers can drive away customers due to long waiting time. At the same time, too many drivers can make more drivers earn less due to idle time.

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