Competitor comparison

In what is called the Transaction Section, the questionnaire seeks detailed comparisons with competitors on various aspects of the relationship. Also, since Acme serves end-customers through distributors, it seeks information on their effectiveness. The data refines the industry-level evaluations in the Strategic Section. It moves from an overall industry perspective down to the level of individual competitors. So it should provide Acme with a good idea of the most effective competitors. The mechanics of using a quartile approach are similar to those in the Strategic Section. 

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Competitive benchmarking. A specific competitor to competitor comparisons for the product or function of interest. 

Competitor comparison Acme rankings in technology, service, and cost categories relative to specific competitors. 

Competitor comparison. Acme rankings in specific areas relative to specific competitors. 

Detailed economics of individual industrial processes, including SCP processes, are usually regarded as confidential, out of fear that publication may lend advantage to competitors. In addition, economy of scale rule generally applies (that is as the production capacity increases, the cost of the product decreases), so that direct comparisons can only be made between systems of similar capacity. Some economic data on SCP processes have been published and are presented in the Resource Material at the end of this chapter. You should appreciate that the data are outdated by more than a decade, during which time substrate costs will have varied relative to each other, and technology will have improved. This means that the comparative costs presented in Table 4.13, for example, may not be now as presented there. Nevertheless the data presented do provide an outline of the economics of SCP production. The processes referred to in the Resource Material are not necessarily those mentioned in the text and so you may find some differences in detail. 

Figure 5. Comparison of specific displacement of 10 nM tritiated saxitoxin (A) or 10 nM tritiated PbTx-3 ( ) by unlabeled competitor saxitoxin or brevetoxin, respectively, in rat brain synaptosomes. IC q in each case is 5-10 nM.

In dentistry, silicones are primarily used as dental-impression materials where chemical- and bioinertness are critical, and, thus, thoroughly evaluated.546 The development of a method for the detection of antibodies to silicones has been reviewed,547 as the search for novel silicone biomaterials continues. Thus, aromatic polyamide-silicone resins have been reviewed as a new class of biomaterials.548 In a short review, the comparison of silicones with their major competitor in biomaterials, polyurethanes, has been conducted.549 But silicones are also used in the modification of polyurethanes and other polymers via co-polymerization, formation of IPNs, blending, or functionalization by grafting, affecting both bulk and surface characteristics of the materials, as discussed in the recent reviews.550-552 A number of papers deal specifically with surface modification of silicones for medical applications, as described in a recent reference.555 The role of silicones in biodegradable polyurethane co-polymers,554 and in other hydrolytically degradable co-polymers,555 was recently studied. 

The standard screening approach when several active molecules have been identified is pharmacophore mapping followed by 3D database searching. This approach assumes that the active molecules have a common mode of action and that features that are common to all of the molecules describe the pharmacophoric pattern responsible for the observed bioactivity. This is a powerful technique but one that may not be applicable to the structurally heterogeneous hits that characterize typical HTS experiments or sets of competitor compounds drawn from the public literature. In such cases, it is appropriate to consider approaches based on 2D similarity searching and we present here a comparison of approaches for combining the structural information that can be gleaned from a small set of reference structures. 

The steady-state luminescence of Pr + in minerals was found only in scheel-ite, where the hne near 480 nm has been ascribed to this center (Gorobets and Kudrina 1976) and possibly in fluorite (Krasilschikova et al. 1986). The luminescence of Pr in minerals is difficult to detect because its radiative transitions are hidden by the stronger lines of Sm in the orange range of 600-650 nm, Dy " " in the blue range of 470-490 nm and Nd in the near IR (870-900 nm). In order to extract the hidden Pr lines time-resolved luminescence was applied. The fact was used that Pr " usually has a relatively short decay time compared to its competitors Dy ", Sm " and Nd, especially from the Po level. In order to correct identification of Pr " lines in minerals several of them were synthesized and artificially activated by Pr (Fig. 5.5). Besides, comparison has been made with CL spectra of synthetic minerals artificially activated by Pr (Blank et al. 2000). 

Also, comparisons demonstrating the superior biocidal activity (MKC ppm) of a particular manufacturer s product, against competitors materials, may fail to identify the actual activity of each product used or the operating pH employed. Such comparisons make relative reviews of true activities and cost-effectiveness impossible, and are thus useless. 

ESPS remains a computationally intensive strategy, though not prohibitively so on the scale of its competitors One explicit comparison (in the case hard-sphere crystals) indicates that ESPS and NIRM deliver similar precision for similar compute resource [34]. 

The starting point for an advanced SHV orientation is a clear understanding of the company s capital market valuation. This may appear an obvious point, but in our experience many companies rarely go beyond tracking TRS in comparison with competitors and talking regularly to analysts and I-banks. There is more to it than that chemical companies need to develop a superior understanding of the drivers of their capital market valuation and potential gaps to their own best estimate of fundamental value. 

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