Otto's Consulting Report

Topics: Statistics, Regression analysis, Prediction Pages: 7 (2151 words) Published: March 1, 2013
Otto’s Auto Parts Statistical Report

Hansen Consulting proudly presents the following statistical information for Otto’s Auto parts. Statistics is a branch of mathematics that makes it possible for you to gain an edge over your competitors by providing a method for collection of data, and a way to summarize and quantify it to represent real world observations from which predictions can be made. Statistics also includes stochastic modeling, which is a powerful tool that incorporates random variables to predict future outcomes. For decades insurance companies have successfully profited from using stochastic modeling for making predictions of unknown entities. Stochastic models can be run hundreds or even thousands of times to show the likelihood of which outcomes are likely to occur, based on different managerial decisions, and what their expected ranges are in the long run. We will discuss stochastic modeling in further detail later.

We have prepared an in depth report for your consideration. Our report includes a tool crib staffing analysis, an inventory simulation for the manufacture of your catalytic converters, an employee profile concerning age and gender, profit-ability analysis for handheld computers, and an accuracy test of your current personal screening tests. Some of the challenges you are currently faced with in the “Tool Crib” at your Pierre manufacturing facility are similar to the problems Chrysler dealt with when Lee Iacocca asked Hansen Consulting to help put them on the path to profitability in 1979. For your consideration, we have analyzed your tool crib system using the patented Wedel Queuing Analysis Software, to assist you in determining the optimal number of employees to run your Pierre tool crib.


The Patented Wedel Queuing software is derived from queuing theory, where predictions can be made for fabrication & assembly worker time spent waiting in line, and their arrival times, in relation to a varying number of tool crib servers (workers). In essence, queuing theory provides the tools necessary to analyze and understand the relationship between congestion and service delays caused from waiting in line. Our queuing analysis for the tool crib demonstrates the balance between server idle time (tool crib workers), versus customer costs (total time for fabrication & assembly workers to check out tools or to return tools). As server idle time increases, customer costs decreases, and the total cost also decreases until the optimal number of servers is reached. After the optimal number of servers is reached, the total cost begins to increase. Based on our analysis, the expected optimal cost saving balance is achieved when there are 8 workers manning the tool crib. We have included the costs for having 6 – 10 servers, in the table located below, to demonstrate cost comparisons for your consideration.


SERVERS| SERVERIDLE TIME| SERVER COST (cost/hour)| CUSTOMER COST (cost/hour)| TOTAL COST (cost/hour)| 6| 11.1%| $168| $361| $529|
7| 23.8%| $196| $217| $413|
8| 33.3%| $224| $189| $412|
9| 40.7%| $252| $179| $431|
10| 46.7%| $280| $176| $456|


Simulation is a method of creating a model system to imitate the operation of a real system over time. To create a model system, random numbers are chosen to mimic future unknowns, such as delivery times for orders, and project completion times, or product demand. Simulation models have been used to predict expected numbers of passengers for commuter airlines flights, or construction completion dates for track home developers, and for estimating optimal inventory levels at pharmacies, to name a few examples. Simulations become powerful tools for predicting expected outcomes when they are run hundreds or even thousands of times to reveal a “long run average”. For example: if you simulate the flipping of a coin 300 times,...
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