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the employee manual is important for laying out such payroll policies as when time sheets are due, how much vacation and sick time can be taken, when the payroll week ends, business hours, employment classifications, handling of severance pay, and how errors in pay are dealt with Since the employee manual should be considered a legal document that employees can use to assert their rights against the company, it is best to consult with a labor lawyer or local employers trade association to expand upon an initial rough draft of an employee manual It is also possible to have a consultant write the entire manual Once complete, employees should be required to sign a document stating that they have received and read the manual This document should be stored in their employee files Establish performance metrics Once he initial department setup is completed, the payroll manager should develop an interest in the department s level of performance This calls for the establishment of metrics, such as the time required to complete a payroll, the number of payroll transaction errors requiring fixes, or the total department cost divided into the total number of company employees The exact metrics used will vary by company, depending on the circumstances A key point is to ensure that the same calculation is used to track metrics from period to period, so the metrics are comparable over time Create management reports Most payroll department reports are based on the basic reports generated either by the in-house payroll software or the supplier to whom payroll processing is outsourced However, the payroll summary and register do not always supply sufficient information Consider generating additional reports, such as the names of those people not yet using direct deposit, or the specific types and number of payroll transaction errors created in each payroll cycle The payroll manager can use this sort of operational information to improve the performance of the department Create a department budget During the initial rush to create a payroll system and produce the first payroll, a planning feature like a budget is usually ignored However, after a few months have passed and one has a better idea of the company growth rate and related payroll processing requirements, it is time to create one This can easily be contained within one or two pages, itemizing major expense categories by month Typically, there are only two expense drivers for the payroll department The first is whether or not the payroll processing task will be outsourced and the second is the total number of company employees expected to be serviced by the payroll department Both expense drivers have a major impact on the number of people working in the payroll department, and the payroll expense is usually the largest department expense Schedule an internal auditor controls review Another task that usually waits until the initial department setup is done is a controls review by an independent group, typically either the internal audit department or a group of independent certified public accountants The purpose of this review is to ensure.

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length is 3k (Figure 52(c)) For any C a k with k > 1, k > C can be chosen so that sl(S) = 3k > 3C = C sl(Sopt ) In particular, list scheduling with start time minimization does not even guarantee a schedule length shorter than the sequential time of a task graph Still, numerous experiments have demonstrated that list scheduling produces good schedules for task graphs with low to medium communication (eg, Khan et al [103], Kwok and Ahmad [111], Sinnen and Sousa [177]) 512 With Dynamic Priorities The simple list scheduling outlined in Algorithm 9 establishes the schedule order of the nodes before the actual scheduling process During the node scheduling in the second part, this order remains unaltered, so the node priorities are static To achieve better schedules, it might be bene cial to consider the state of the partial schedule in the schedule order of the nodes Changing the order of the nodes is equivalent to changing their priorities, hence the node priorities are dynamic It remains that the node order must be compatible with the precedence constraints of the task graph, which is obeyed if only free nodes are scheduled A list scheduling algorithm for dynamic node priorities is given in Algorithm 11 Algorithm 11 List Scheduling Dynamic Priorities (G = (V, E, w, c), P) 1: Put source nodes {ni V : pred(ni ) = } into set S 2: while S = do S only contains free nodes 3: Calculate priorities for nodes ni S 4: Choose a node n from S; S S n 5: Choose a processor P for n 6: Schedule n on P 7: S S {ni succ(n) : pred(ni ) Scur } 8: end while Instead of iterating over the node list, Algorithm 11 calculates in each step the priority of all free nodes and only then selects a node and a processor for scheduling After the scheduling of node n, the set S of free nodes is updated with the nodes that became free by the scheduling of n (line 7) Obviously, this approach has higher complexity in general than the simple list scheduling presented in Algorithm 9, because the priority of every node is determined several times Algorithm 11 is a generalization of the simple list scheduling, which becomes apparent by considering line 3 If this line is moved to the beginning of the algorithm and the priorities are calculated for all nodes, not only for the free nodes, the simple list scheduling is btained This becomes even more distinct in Section 513, where the construction of node lists is explained Choosing Node Processor Pair Some algorithms dynamically compute the priorities of the free nodes by evaluating the start time (see Eq (51)) for every free.

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rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods We believe that before you get into the details of data governance, it is essential for executives to specify their data requirements and to know their data Then the IT specialists can convene the collaboration needed to manage the details The smart data approach and a SOE model adoption should be incorporated directly into the organization s governance process, with the objective of maximizing SOE s value for internal transformation efforts within any organization A smart data strategy toward viewing costs, at the beginning of a software project, would make a difference in cost saving outcomes and optimizing performance Case Study Example: An Empirical Study of the Cobb Douglas Production Function Properties of Software Development Effort An example of breaking from tradition with improved performance results, by dopting a smart data strategy toward costs, was shown in the paper entitled An Empirical Study of the Cobb Douglas Production Function Properties of Software Development Effort [1] In this case study, we examine whether software development effort exhibits Cobb Douglas functional form with respect to team size and software size [2] This is also an example of how smart data is engineered with methodology and associated algorithms in such a way that policies and business rules are implemented with a higher state of automation, and planning, problem solving, decision making, and predicting are more autonomic such as in self-regulation We empirically test these relationships using a real-world software engineering data set containing over 500 software projects The results of our experiments indicate that the hypothesized Cobb Douglas function form for software development effort with respect to team size and software size is true We also nd as increasing returns to scale relationship between software size and team size with software development effort There are over a dozen software effort estimation models and none of these models reliably predict software development effort There are two reasons why traditional software effort estimation models do not predict software effort accurately The rst reason has to do with the selection of a model that appropriately captures the underlying software development production process The second reason may be the fact that the variables used in software effort prediction models do not capture all of the actual software development effort drivers Traditional software cost estimation models predict either software development effort or software development cost Among the popular models for predicting software effort is the common cost model (COCOMO) Using a smart data approach, we have shown that software size and team size have a Cobb Douglas relationship with software development effort A Cobb Douglas production function is a convex production function, which means that for a xed software size and team size a unique minimum software.

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Algorithm 12 Create Node List 1: Assign a priority to each n V 2: Put source nodes n V : pred(n) = into priority queue Q 3: while Q = do Q only contains free nodes 4: Let n be the node of Q with highest priority; remove n from Q 5: Append n to list L 6: Put {ni succ(n) : pred(ni ) L} into Q 7: end while which costs O( log V) and the determination of the free nodes amortizes o O(E) Removing the node with the highest priority from the heap is O(1) An example for a metric that is not compatible with the precedence order is tl + bl a metric that gives preference to the nodes of the critical path Table 51 displays several node orders of the sample task graph obtained with node level based priority metrics The values of the node levels are given in Table 41 As analyzed in Section 44, calculating these levels has a complexity of O(V + E) For all these metrics the rst part of the simple list scheduling is thus O(V log V + E) (calculating levels: O(V + E) + sorting: O(V log V) or O(V log V + E)) Already the few examples in Table 51 illustrate the large variety of possible node orders In experiments, the relevance of the node order for the schedule length was demonstrated (eg, Adam et al [2], Sinnen and Sousa [177]) Unfortunately, most comparisons of scheduling algorithms (eg, Ahmad et al [6], Gerasoulis and Yang [76], Khan et al [103], Kwok and Ahmad [111], McCreary et al [136]) analyze entire algorithms: that is, the algorithms differ not only in one but many aspects This generally impedes one from drawing conclusions on discrete aspects of the algorithms (eg, on the node order) Still, most algorithms apply orders based on node levels and the bottom level order exhibits good average performance (eg, Adam et al [2], Sinnen and Sousa [177]) Breaking Ties Table 51 also shows examples where the node order is not fully de ned, indicated in the table by parentheses around the respective nodes Incidentally,.

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