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A little digging for Quick Wins One of the things you ll likely discover when you investigate complexity is that multiple products or services use separate processes, parts, steps, etc, when in fact they could be using the same processes, parts, steps, etc This principle of having multiple offerings share how they are created is called commonality In banking, for example, it may be that the only thing separating two different loan processes is one or two forms; the rest could be identical But if the two types of loans are handled by different work units, you ll have a lot of duplicated effort Similarly, two products or product families may share many parts or components or process steps, but be handled separately A problem arises because often times similar products or services in your business may appear through this analysis to be destroying value because you re doing all the work separately But they could be made economically profitable if you make adjustments so that the different offerings share process steps, components, tasks, forms, etc (this reduces the number of activities, A, in the Complexity Equation, and other factors that affect PCE) A substructure analysis can help you identify whether such underlying separateness is causing low EP figures, and therefore identify offerings that are salvageable even though they may have performed poorly in all of the other analyses

3-7-2 A Sorting Method The sorting methods of interest to us here are external sorts, where the amount of data is greater than be sorted in memory Most external sorts are merging sorts, where partially sorted sequences are successively combined, until only one sorted sequence remains A merge-sort sorts n records in O(n log n) operations It consists of two phases, sort and merge The merge phase consists of multiple merge passes, as shown in Fig 3-39 The merge-sort operates on sets of records placed into large working bu ers in memory of size Bs , which we call sort bu ers, often much larger than the typical bu er size B used in prior analyses The sort bu ers will be written and read onto working les, each bu er will correspond to a sort block on le As seen below, the performance of a merge-sort improves when the sort blocks are large The constraint on Bs is the available real memory A two-way merge-sort algorithm requires four sort blocks, so that the sort blocks may be made as large as a quarter of available memory (as shown in Fig 3-39), once all systems software, sorting programs, and other bu ers have been accounted for

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TC Display Shows current play time location within the current Element (not shown at top above the Menu and Title buttons) Menu Button The Menu button executes any Menu call connection attached to this Track, or executes the Resume-to-play function when sitting on a menu (once a Track has been played) Title Button The Title button executes any Title connection programmed into the Disc Connections In the Simulator, it does NOT execute any return-to-play function, as many DVD players WILL do Skip Reverse* Skips to the previous chapter

The Sort Phase We begin by reading in data records from le blocks of the source le, lling a sort-block bu er A bu er will contain Bfrs = Bs /R records Once a bu er has been lled with records, it is sorted in memory, using any reasonable internal sort algorithm which is stable (A stable algorithm keeps records with equal keys in the same order that there were submitted [Knuth73S ) If we have a fast internal sort algorithm, the time to sort the record can be overlapped with the time needed to ll a second sort bu er from the source le This requirement is not easily met, but since the sort phase of the merge-sort is not the dominant cost we will assume this condition to be true When the rst sort block has been sorted, and the second sort block has been lled, we start three processes in parallel: 1 The rst sort bu er is written onto merge le i, initially i = 1 2 The second sort bu er is internally sorted An extra bu er is available if needed 3 A third sort bu er is lled from the source le

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The processing of the source records is repeated for i = 2, , i = m, where m is the number of merge les to be created Then i is reset to 1, and the processing continues until the source le is empty and each of the m merge les contain about n/m records More precisely the merge les will contain up to n/m/Bfrs sort blocks; les labeled i > 1 may contain only n/m/Bfrs sort blocks The number of merge les m may vary from 2 to about 10 Having many merge les permits multiple les to be read or written in parallel The amount of

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