The Objective of my thesis project is to determine, quantify, and ultimately address main drivers of order picking accuracy issues at Frito Lay warehousing distribution center utilizing ?Pre-pick? modular system. Sales orders are filled/picked using two categories and processed utilizing different technology (pre-pick modular system and Bulk orders):. Pre-pick modular systems volume order category or small format utilizes customized orders of assortment of eaches (one bag), these type of sales orders are mostly placed by small grocery stores, mini retail, gas stations and independent mom and pops stores, etc. which can only sell an assortment of products. Filling orders for pre-pick or small format adds complexity to the picking process and can create challenges for order accuracy and as such is processed utilizing pre-pick module system which is designed to deliver customized orders.
At the distribution center, Bulk format orders are characterized by full cases products orders as placed by major retailers such as Wal-Mart, Kroger?s, Target, etc. that can support volume sales of such large quantity product, these orders are picked at the warehouse using voice technology consisting of a headset with a microphone and wearable handheld through which the order is picked for the route sales, typically this format (sales) order and are less prone to picking errors. My task or hypothesis is on a given total volume orders for adjustment/errors, pe-pick will have more errors compared to bulk and quantify using statistics to approve/disapprove this hypothesis.
The Objective of the thesis project is to: determine the effect of error in a pre pick and error in bulk on a give volume order received in a distribution center(pre pick and bulk are two means of order picking/fulfilling to service sales), from theory or the operation knowledge on given volume order, orders picked using pre pick system
has more errors/impact on a given volume compared to orders filled using bulk and with two years data(2009, 2010) I want to quantify the impact and
validity of my assumption. The data that I am working with is based on volume orders received
(pre pick orders, bulk orders) and their respective errors recorded in all of Frito Lay distribution centers(133 distribution centers is
included in the observation), they are divided into four division based on regions(North, South, West, East). The methodology i used to analyze the data, since the data has observations on the same variables(volume order of pre pick and bulk, errors for each) from
many distribution centers from two different periods(2009 and 2010), it therefore combines cross-sectional and time series to be pooled
into a panel data- use a concept called “(first)differencing model ” to get rid of fixed effect for a linear panel model or alternatively build longititdunal SPSS using stata, in which variables and error term are expressed as the differences, for the two
year time period my setup looks like:
Change in volume (Year2-Year1): β0 +β1Δ prepick error+β2Δ Bulk+ε
I will also include dummy variables to look at different divisions and analyze the significance of the regression model and approve/disapprove the hypothesis.
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