{"id":259522,"date":"2021-06-14T13:19:44","date_gmt":"2021-06-14T10:19:44","guid":{"rendered":"https:\/\/hmak.org\/?p=259522"},"modified":"2021-12-08T10:29:10","modified_gmt":"2021-12-08T07:29:10","slug":"reliability-in-complex-inventory-accounting","status":"publish","type":"post","link":"https:\/\/hmak.org\/main\/reliability-in-complex-inventory-accounting\/","title":{"rendered":"Reliability In Complex Inventory Accounting Systems"},"content":{"rendered":"<div style=\"margin-top: 0px; margin-bottom: 0px;\" class=\"sharethis-inline-share-buttons\" ><\/div><div id=\"toc\" style=\"background: #f9f9f9;border: 1px solid #aaa;display: table;margin-bottom: 1em;padding: 1em;width: 350px;\">\n<p class=\"toctitle\" style=\"font-weight: 700;text-align: center;\">Content<\/p>\n<ul class=\"toc_list\">\n<li><a href=\"#toc-0\">Sensitivity Analysis: An Example<\/a><\/li>\n<li><a href=\"#toc-1\">Sensitivity Analysis Table<\/a><\/li>\n<li><a href=\"#toc-2\">Video Explanation Of Sensitivity Analysis<\/a><\/li>\n<li><a href=\"#toc-3\">Characteristics Of Scenario Analysis<\/a><\/li>\n<li><a href=\"#toc-4\">Sensitivity Analysis Of Population Viability Analysis Models<\/a><\/li>\n<\/ul>\n<\/div>\n<p><img decoding=\"async\" class='wp-post-image' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"257px%\" alt=\"sensitivity analysis accounting\"\/><\/p>\n<p>Variance-based methods allow full exploration of the input space, accounting for interactions, and nonlinear responses. For these reasons they are widely used when it is feasible to calculate them. Typically this calculation involves the use of Monte Carlo methods, but since this can involve many thousands of model runs, other methods can be used to reduce computational expense when necessary.<\/p>\n<p>For example, if one element of your supply chain is negatively affected by changes in base interest rates, a sensitivity analysis can help you predict when to act in order to lessen or avoid those impacts. One of the benefits of conducting a sensitivity analysis is that it enables companies to carry out in-depth studies on each variable in a given scenario.<\/p>\n<p>By changing an input variable, and measuring how the outcomes are affected by that change, the analyst can gauge how sensitive the model is to the individual input variable. The proper CVP analysis requires that the new fixed cost be divided by the new unit contribution margin to determine the new break-even level. Such analysis is important to evaluate whether an increase in fixed costs is justified. In capital budgeting calculations, sensitivity analysis changes one assumption or estimate at a time to see how the results change. For example, a business may expect to earn $500, $1,000 and $1,000 in the first three years of a project. If the business makes an initial investment of $2,500, it will recoup its expenses in three years.<\/p>\n<ul>\n<li>The cost of capital is 8 %, assuming the variables remain constant and determine the project\u2019s Net Present Value .<\/li>\n<li>One of the simplest and most common approaches is that of changing one factor at a time.<\/li>\n<li>They also sell a variety of baked goods and T-shirts with their logo on them.<\/li>\n<li>Despite its simplicity however, this approach does not fully explore the input space, since it does not take into account the simultaneous variation of input variables.<\/li>\n<li>Based on the above-mentioned technique, all the combinations of the two independent variables will be calculated to assess the sensitivity of the output.<\/li>\n<\/ul>\n<p>In a design of experiments, one studies the effect of some process or intervention (the &#8216;treatment&#8217;) on some objects (the &#8216;experimental units&#8217;). In sensitivity analysis one looks at the effect of varying the inputs of a mathematical model on the output of the model itself. <a href=\"https:\/\/en.wikipedia.org\/wiki\/Retained_earnings\">retained earnings<\/a> In both disciplines one strives to obtain information from the system with a minimum of physical or numerical experiments. Scenario Analysis is not about changing one variable (e.g., if interest rate increases 1% or the cost of raw materials increase by 10%).<\/p>\n<p>Note that full variance decompositions are only meaningful when the input factors are independent from one another. This is when one performs sensitivity analysis on one sub-model at a time. This approach is non conservative as it might overlook interactions among factors in different sub-models . There is not enough information to build probability distributions for the inputs.<\/p>\n<h2 id=\"toc-0\">Sensitivity Analysis: An Example<\/h2>\n<p>It is very important to rightly interpret the sensitivity analysis study. After sensitivity analysis definition, let\u2019s take an example to further clarify the concept. Based on these NPVs, the business would choose project 2 because it has got the highest NPV among the three projects being considered. Now let us assume that the sales revenue for these projects is subject to change due to uncertainties in forecasting. The net present value is a measure of the present value of an investment.<\/p>\n<p>The impact on the cash movements for each month could be calculated from just examining the changes proposed. This approach requires the application of some logic to the problem but is a quicker technique than redrafting the whole budget. Here we mean changes in the sales units, or the production or purchase units. It could also apply to some extent to overheads or fixed assets (e.g. hiring or buying <a href=\"https:\/\/www.youtube.com\/results?search_query=what+are+retained+earnings\">what are retained earnings<\/a> additional equipment not included in the original budget). Sensitivity analysis involves examining what happens to a budget when changes are made in the assumption on which it is based. It is also known as \u2018what-if\u2019 analysis, and can be carried out using a spreadsheet or with manual calculations. Manual calculations are easier if they focus only on the parts of the budget that are subject to change.<\/p>\n<p>Scenario Analysis allows businesses or independent investors to assess investment prospects in order to avoid bad investment decisions. Scenario Analysis takes the best and worst probabilities into account, so investors or potential investors can make better informed decisions.<\/p>\n<h2 id=\"toc-1\">Sensitivity Analysis Table<\/h2>\n<p>The advantage of this approach is that it can also deal with &#8220;given data&#8221;, i.e., a set of arbitrarily-placed data points, and gives a direct visual indication of sensitivity. Quantitative measures can also be drawn, for example by measuring the correlation between Y and Xi, or even by estimating variance-based measures by nonlinear regression. One of the most commonly used screening method is the elementary effect method. Basically, the higher the variability the more <a href=\"https:\/\/twitter.com\/QuickBooks\">QuickBooks<\/a> heterogeneous is the response surface along a particular direction\/parameter, at a specific perturbation scale. As a result, the VARS framework accounts for the fact that sensitivity is a scale-dependent concept, and thus overcomes the scale issue of traditional sensitivity analysis methods. More importantly, VARS is able to provide relatively stable and statistically robust estimates of parameter sensitivity with much lower computational cost than other strategies .<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"257px%\" alt=\"sensitivity analysis accounting\"\/><\/p>\n<p>Isn\u2019t that why you build a model in the first place \u2014 to get some clarity or answer as to the future performance of the business? The purpose of the financial model is to provide some insight into future performance but there is no one correct answer. Clients and managing directors like to see a range of possible outcomes and this is where the sensitivity analysis, or \u201cwhat-if\u201d analysis comes into play. As per the requirement of the decision-making area, the variables and their types would differ.<\/p>\n<h2 id=\"toc-2\">Video Explanation Of Sensitivity Analysis<\/h2>\n<p>Sensitivity Analysis changes variables\/assumptions one at a time, which makes it possible to see which variables\/assumptions will have the greatest impact on a business. For example, the borrowing rate can be changed by one basis point at time, or the gross margin percentage can change by specific increments. The results of one could be drastically different than the other and lead to different causes of action.<\/p>\n<p><img decoding=\"async\" class='aligncenter' style='display: block;margin-left:auto;margin-right:auto;' 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\" width=\"259px%\" alt=\"sensitivity analysis accounting\"\/><\/p>\n<p>What-if simulations can explore how both direct and indirect changes to a variable affects the business. For example, a scenario analysis might look at all the elements of your business in the event of a financial crash and run the three different scenarios. You can estimate what happens to your profits if all inputs are as rosy as can be under the circumstances, if they get as bad as possible, or if they remain average for historical crashes. It involves considering what outcomes are possible under a variety of possible future scenarios.<\/p>\n<p>Such \u201ccost plus\u201d agreements must be carefully constructed, else the seller has little incentive to do anything but let costs creep up. Sometimes that company may complain about cost increases negatively affecting its margins. Before assuming the worst, take a closer look to see how the bottom line is being impacted.<\/p>\n<h2 id=\"toc-3\">Characteristics Of Scenario Analysis<\/h2>\n<p>Let us take the example of a simple output formula, which is stated as the summation of the square of two independent variables X and Y. Performing such analysis helps us predict better the outcome of a decision, based on a range of variables.<\/p>\n<h2 id=\"toc-4\">Sensitivity Analysis Of Population Viability Analysis Models<\/h2>\n<p>It helps the decision makers of business to learn about the different parameters that drive a business. Along with that, the business knows how each parameter affects its functioning and profitability. This analysis evaluates the best business model after considering the different bottlenecks and variables. The aim of sensitivity analysis is to arrive at such business model that results in higher EPS. To sum up, every business must conduct sensitivity analysis to stay ahead of its competitors and for higher growth as well as sustainability. So what can you do if the financial model\u2019s results are not the final results?<\/p>\n<h2 id=\"toc-5\">Sensitivity Analysis In Financial Modeling<\/h2>\n<p>Sensitivity analyses can help decision-makers come up with better-informed choices to steer their businesses. But like any financial modeling tool, they come with advantages and disadvantages. For example, you might want to look at how a decrease in shareholder dividends may affect the price of shares in a publicly traded company. Those simulations could inform your decision making process about whether <a href=\"https:\/\/online-accounting.net\/using-cost-volume-profit-models-for-sensitivity\/\">sensitivity analysis accounting<\/a> to invest in that company\u2019s stock. Let\u2019s say you run a coffee shop in a mall, and you know it is busiest in December. If you\u2019d like to know how the increased foot traffic might impact your revenue, you can conduct a sensitivity analysis. Although it is informative to view the change in the model from altering a single variable, in many cases more than one variable is likely to change concurrently.<\/p>\n<p>The regression is required to be linear with respect to the data (i.e. a hyperplane, hence with no quadratic terms, etc., as regressors) because otherwise it is difficult to interpret the standardised coefficients. This method is therefore most suitable when the model response is in fact linear; linearity can be confirmed, for instance, if the coefficient of determination is large.<\/p>\n<div style=\"margin-top: 0px; margin-bottom: 0px;\" class=\"sharethis-inline-share-buttons\" ><\/div>","protected":false},"excerpt":{"rendered":"<p>Content Sensitivity Analysis: An Example Sensitivity Analysis Table Video Explanation Of Sensitivity Analysis Characteristics Of Scenario Analysis Sensitivity Analysis Of Population Viability Analysis Models Variance-based methods allow full exploration of the input space, accounting for interactions, and nonlinear responses. For these reasons they are widely used when it is feasible to calculate them. Typically this &hellip;<\/p>\n<div style=\"margin-top: 0px; margin-bottom: 0px;\" class=\"sharethis-inline-share-buttons\" data-url=https:\/\/hmak.org\/main\/reliability-in-complex-inventory-accounting\/><\/div>\n","protected":false},"author":20,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-259522","post","type-post","status-publish","format-standard","hentry","category-9"],"_links":{"self":[{"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/posts\/259522","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/users\/20"}],"replies":[{"embeddable":true,"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/comments?post=259522"}],"version-history":[{"count":0,"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/posts\/259522\/revisions"}],"wp:attachment":[{"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/media?parent=259522"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/categories?post=259522"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hmak.org\/main\/wp-json\/wp\/v2\/tags?post=259522"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}