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free java barcode reader api Effect of Variation in Software
Effect of Variation Generating Code 39 Extended In None Using Barcode printer for Software Control to generate, create Code 3/9 image in Software applications. Code 39 Extended Recognizer In None Using Barcode scanner for Software Control to read, scan read, scan image in Software applications. The derivations above do not include the impact of variation in demand, setup times, processing times per unit and reject rates These phenomena can be approximated by formulas from Queuing Theory, but due to their limited range of application, we prefer to calculate the effects of variation using computer Discrete Event Simulations Paint Code 39 In C# Using Barcode encoder for VS .NET Control to generate, create Code 39 Extended image in .NET framework applications. Encode Code 39 In .NET Framework Using Barcode maker for ASP.NET Control to generate, create ANSI/AIM Code 39 image in ASP.NET applications. Steps beyond the Simplified Complexity Equation
Code39 Generator In Visual Studio .NET Using Barcode drawer for Visual Studio .NET Control to generate, create Code39 image in .NET framework applications. Paint ANSI/AIM Code 39 In VB.NET Using Barcode printer for .NET framework Control to generate, create Code 3/9 image in Visual Studio .NET applications. In 8, we discussed examples of how the Complexity Equation can be used to estimate the impact of process or product improvements, such as the reduction in the number of different offering or reduction in average setup time, all other things being held constant But what if you want to know the impact of reducing only the number of lowvolume products via commonization or outsourcing, etc The derivation becomes a bit more complicated, but the math itself remains in the realm of the second year of High School algebra We start by divide the offering into two groups (highvolume vs lowvolume) using Pareto analysis The highvolume group will constitute about 20% of the total number of offerings, and have an average demand of DH per offering per unit of time The lowvolume offerings (which constitute about 20% of total demand, and 80% of the number of offerings) will have an average of DL demand We will use the same nomenclature as above: NH = number of different offering in the highvolume group, etc (It doesn t matter if the split is 70/30, we will first derive a completely general expression for process cycle efficiency We will then apply it to the case of breaking the product line into two groups, then as many as you like Remember though, or goal is to increase Process Cycle Efficiency, not build models) EAN128 Printer In None Using Barcode drawer for Software Control to generate, create UCC128 image in Software applications. Painting Barcode In None Using Barcode maker for Software Control to generate, create bar code image in Software applications. Appendix
Code 128 Creator In None Using Barcode encoder for Software Control to generate, create Code 128C image in Software applications. Code 39 Generation In None Using Barcode encoder for Software Control to generate, create Code 3/9 image in Software applications. To keep the math simple, we will return to the original derivation earlier in this Appendix The addition of quality and process complexity complicates the formulas2 We start with the definition of Process Cycle Efficiency (numbers correspond to equation numbers from the patent applications): Barcode Printer In None Using Barcode printer for Software Control to generate, create bar code image in Software applications. UPC Code Drawer In None Using Barcode generation for Software Control to generate, create UPC Symbol image in Software applications. Process Cycle Efficiency = Valueadd Time Total Lead Time
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DataMatrix Drawer In ObjectiveC Using Barcode generation for iPad Control to generate, create Data Matrix image in iPad applications. EAN13 Supplement 5 Encoder In Java Using Barcode creation for Java Control to generate, create UPC  13 image in Java applications. 1 = N (16) The total lead time is, from Little s law: Total lead time
Total Number of Things in Process Completion Rate
(17) The components of this equation can be denoted as: Total Number of Things in Process
TIP
Completion rate
(18, 19) From the Patent3 using the usual rule for matrix multiplication: (TIP1 TIP2 TIPN )= 2 i =1 1 ( X1  D1P1 ) D 2 P1 (1  X2  D 2 P2 ) D1P2 S (2A + 1)(D1 D 2 D N ) i D P  D 2 PN 1 N D N P2 (1 XN DN PN ) D N P1
(20) (20) This assumes that each of the N products has unique demand, processing time, etc So now lets apply this to the problem we posed, breaking the product line into two segments, with NH being the number of offerings with high volume demand DH high volume and NL the number of low volume products, etc: (1  XH  NHDHPH )  NLDLPH (TIPH TIPL) = 2(SH + SL )(2A + 1)(DH DL)  NHDHPL (1  XL  NLDLPL (21) Conquering Complexity in Your Business
Now any 2x2 matrix can be inverted according to the rule
(22) Thus we have: a b 1 d  b c d = ad bc  c a
(23) 1 (1  XL  NLDLPL) NHDHPL (  NHDHPH )  NLDLPH 1 (  NHDHPL (1  NLDLPL) = (  NHDHPH )(1  NLDLPL)  (NHDHPL)(NLDLPH) NLDLPH (1  XH  NHDHPH ) 1 We can now substitute (23) into (21) and compute TIPH and TIPL, use these values in (19), (17) and finally compute PCE in (15) A 2x2 analysis is often an easy first cut at the problem, because it captures a lot that is currently hidden from management It allows you to vary the number of lowvolume parts NL and compute the impact on Process Cycle Efficiency From the complexity matrix you can decide whether an initiative related to commonization or pruning (reduction of NL) or a lean initiative in reducing setup time (SH and/or SL) will be most effective in reducing PCE Given the cost data from the Complexity Value Stream Map, you can estimate just what chunks of nonvalueadd cost could be removed Equation (9) in the patent allows us to have a Processing Time per unit PH for the high volume and a separate PL for the low volume products, and in fact we would expect PH<PL, because of the volume and repetition differences, and similarly SH<SL Now if you believe that you need to break the demands into more than two groups to more closely mirror your offering, the math isn t any more difficult its just bigger, and gets beyond the realm of pencil and paper The problem of inverting an NxN matrix in a reasonable amount of time used to require mainframe computers However, ExcelTM now has solvers with add on packages that make virtually all practical problems within the reach of PentiumTM PC

