Two fundamental issues of product management are whether to pioneer or follow, and how to manage the product over its life cycle.
Order of market entry is very important. In fact, the forecasted market share relative to the pioneering brand is the pioneering brand's share divided by the square root of the order of entry. For example, the brand that entered third is forecasted to have 1/√3 times the market share of the first entrant (Marketing Science, Vol. 14, No. 3, Part 2 of 2, 1995.) This rule was determined empirically.
The pioneering advantage is obtained from both the supply and demand side. From the supply side, there are raw material advantages, better experience effects to provide a cost advantage, and channel preemption. On the demand side, there is the advantage of familiarity, the chance to set a standard, and the choice of perceptual position.
Once a firm gains a pioneering advantage, it can maintain it by improving the product, creating a standard, advertise that it was the first, and introduce a new product in the market that may cannibalize the first but deter other firms from entering.

Consider a firm that owns a fixed capacity of a resource that is consumed in the production or delivery of multiple products. The firm's problem is to maximize its total expected revenues over a finite horizon either by choosing a dynamic pricing strategy for each product, or, if prices are fixed, by selecting a dynamic rule that controls the allocation of capacity to requests for the different products. This paper shows how these well-studied revenue management problems can be reduced to a common formulation where the firm controls the aggregate rate at which all products jointly consume resource capacity. Product-level controls are then chosen to maximize the instantaneous revenues subject to the constraint that they jointly consume capacity at the desired rate. This highlights the common structure of these two problems, and in some cases leads to algorithmic simplifications through the reduction in the control dimension of the associated optimization problems. In addition, we show that this reduction leads to a closed-form solutions of the associated deterministic (fluid) formulation of these problems, which, in turn, suggest several natural static and dynamic pricing heuristics that we analyze asymptotically and through an extensive numerical study. In the context of the former, we show that "resolving" the fluid heuristic achieves asymptotically optimal performance under fluid scaling.

To meet the overall power demands of the electronics market today, semiconductor designers and architects need to step up and deliver systems and chips that are leaner, meaner and greener. By "leaner," I mean that these devices need to be as small in form as possible. By "meaner" I'm referring to overall best performance. And "greener," of course, translates to lower overall power consumption.A common approach to high-level power estimation involves conducting spreadsheet-based analysis that draws on knowledge gained from past projects. Designers also tend to rely on software simulation at the gate level even though they are not getting performance they would need.When engineers make decisions about average and peak power dissipations from the block-level tests, they often "over spec" their system power budget; that is, they select the most conservative estimations possible, so the margin for error tends to fall on the side of lesser consumption rather than more. This in turn can have ripple effects on the entire design, ultimately resulting in packaging and cooling requirements that add significant cost to the final product. While expensive packaging and cooling may reduce the chance of chip failure in the field, better power estimation can achieve the same result at a lower cost, allowing for superior products.