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technically feasible, and in fact we have identi ed more than one possible mechanism to accomplish the project goal The complexity of the SCRI tasking demanded a superior analytical approach, rst to investigate the feasibility of a predictive model, and later, to explore avenues that would lead to the development of that model Throughout the duration of the project, we followed a rigorous analytical approach in a number of ways First, we utilized a relevant literature review and used structured interviews and brainstorming with DLA experts to hypothesize the major causes of backorder aging Data was gathered, scrubbed, and analyzed using appropriate statistical techniques generally accepted in the industry, such as multiple regression analysis, to determine probabilities Using sensitivity analysis and Ishikawa diagrams, we investigated, conceptualized, and brainstormed in order to determine the component elements of ALT and PLT These components include controlled inventory, Acquisition Advice stock codes (AAC), Acquisition Method Suf x Code (AMSC) Procurable Codes, Precious Metal Indicator Codes (PMIC), Tech Documentation Codes, Item Standardization Code (ISC) Categories, Water Commodity Categories, Controlled Inventory Item Code (CIIC) Categories, and Criticality Categories By training with that data, and investigating some baseline descriptive statistics, we discovered retrospective patterns in the data that support the hypotheses that long ALT and long PLT lead to increased birth to ship dates Once the feasibility of integrating data to develop a predictive mechanism was established, more sophisticated methods were required to provide actual prospective forecasting of backorder aging, employing B3I data In order to accomplish this task, it was necessary for the subject matter experts to develop discrete numeric variables from the qualitative string characters present in the data set First, logistic regression analysis was employed to nd these prospective relationships Second, in a later iteration of the SDP, Bayesian networks utilized the discrete variables to determine the probabilities of these factors impacting birth to last ship date, customer wait times, and logistic response times Another iteration of the SDP may allow for the triangulation of the Bayesian probabilities and qualitative data in order to develop an expert system for B3I and backorder aging prediction The SDP of planning, analyzing, designing, and implementing a solution can be seen in our mechanism development methodology We followed an iterative, incremental method of literature review, conceptualization, hypothesis development, descriptive statistical analysis, retrospective regression analysis, utilization of discrete variables to run logit, and determination of prospective Bayesian network probabilities The next step in the SDP is to utilize this information in order to develop an expert system These methods were chosen for the reasons described above Even with improved demand forecasting as a result of CDP and the concurrent ability to reduce backorder age caused by inaccurate forecasting, the process could be enhanced even further with the development of a process/mechanism for identifying which items have a probability of causing signi cant backorder age Several successes can be drawn from the analysis of the PHDM and RHDM databases Descriptive statistics provide several interesting results For example, the combined RHDM and PHDM datasets contain 56,052 birth date to last shipment (BLS)
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entries There are 1833 BLS entries that are over 360 days and 48,136 BLS entries that are less than 90 days It can be seen that there is a statistically signi cant difference between the means for less than 90 days and greater than 360 days for quantity ordered, administrative lead time, production lead time, and unit price of the NIIN These conclusions suggest that the pro les of the less than 90 day sample and the greater than 360 day backorders are distinctly different A majority of the AAC groups fall into the D, H, J, and Z categories for both <90 and >360 days For both categories, groups 1 and 3 account for a majority of the AMC groups, whereas C, G, H, and P are the major AMSC groups Discriminant analysis also provides some interesting insights For example, ALT, PLT, unit price, and quantity ordered correctly classify ACC groups D and H approximately 77% of the time The overall model is signi cant at p 0000 Therefore if the ALT, PLT, unit price, and quantity ordered are known, the buyer can use this model to predict if the item is stocked or nonstocked and order accordingly ANOVA for the model is signi cant at p 0000 Regression analysis supports our hypotheses that there is a possible relationship between ALT, PLT, and ACC code with birth to last shipment date ( p 000) There is also support for the hypothesis that suggests there is a possible correlation between unit price and birth to last shipment date (p 0005) However, no relationship was found between quantity ordered and birth to last shipment date (p 0957) Since the slope of the curve is positive for PLT, ALT, and unit price, it can be concluded that an increase in any of these three variables will lead to increased birth to last shipment time, and vice versa AAC codes D and H (stocked) have an inverse relationship with birth to last ship date Therefore any D or H item that is in stock takes less time to ship than those that are not stocked SOE Paradigm Is Main Contention One of the main contentions of our book is that the SOE paradigm was advanced as a best-practice approach to achieving results from enterprise integration, placing enterprise context at the forefront and endpoint of all enterprise integration initiatives SOE is presented as a replacement for traditional systems integration approaches, such as the one employed currently by the DLA, that are too often narrowly focused, and from which results are functionally siloed, rarely producing the magnitude of improvement planned, and costing far more than expected The SOE initiative addresses the causes and the remedies and identi es what actions and elements can improve optimizing enterprise performance with computer automation In our example, the war room mentality of the decision makers led to a bullwhip effect that hid the real issues affecting the supply chain SOE was developed in concert with a subset of US Department of Defense (DoD) customers who manage the most complex enterprise in the world Because optimizing performance in the defense enterprise is dependent on supply chains and complex relationships between government customers, contractors, subcontractors, and suppliers, this may be the source of best practices in addressing associated problems and needs One limitation of this study was the sample size
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