Master of Agribusiness / Department of Agricultural Economics / Vincent Amanor-Boadu / Order picking errors have an adverse effect on performance because they contribute
to lost time, resources and customer loyalty. Therefore, it is imperative that organizations
reduce errors as much as possible. However, organizations cannot effectively reduce errors
until they understand the factors that determine and influence them and can isolate the
sources of those errors. Product distribution at Frito Lay is very critical in the supply chain
activities of the company and understanding and managing the level of errors that occur at
the distribution phase of operations is critical for the firm’s long term sustained
competitiveness.
This study examines Frito Lay’s order filling processes and how order volumes
affect the level of errors. The company uses two types of order picking technologies: prepick
and bulk order, conventionally also known as pick-to-light and voice-pick technology
respectively. The main objectives of the study are: (a) to examine the impact of size of
volume processed at the distribution center on errors recorded for each order pick
technology and (b) the impact of regional and seasonal differences across Frito Lay’s
distribution network.
The data pertaining to pre-pick volume, pre-pick error, bulk volume and bulk error
were collected for ten consecutive quarters time period ranging from first quarter of 2009 to
the second quarter of 2011 and across 16 divisional distribution centers in four regions of
the U.S. The data were organized into a panel for analyses using Stata® 12.1. With no a
priori foundation for choosing any particular structural equation form, a number of
structural equations were estimated and compared to consistency with economic theory and
internal consistency. Two different sets of models were estimated: one for each
technology. The regression results from the analysis from the pre-pick order picking
technology models showed the quadratic model was the “best” model, whereas the linear
model turned out to be the “best” structural form for bulk order picking system.
This research provides valuable information to management in attempt to address
errors in the order fulfillment system. Because errors may be human, and these human
errors may emanate from lack of knowledge or poor skills, they can be addressed with
training and education. The human errors may also be a result of processes in the plant.
These could be addressed by the reconfiguration of processes and educating people about
those processes. Finally, the errors may be motivational, leading to poor focus in executing
responsibilities. To address these types of errors, management may choose to implement
both positive and negative incentives. Positive incentives will provide rewards to
employees who meet error reduction targets that are established at the beginning of certain
periods. Negative incentives may include penalties for exceeding pre-specified error
thresholds.
The Frito Lay system would benefit more from this research if the data had
included human resource demographic data as well as economic information. It would
have allowed the research to estimate the effect of errors on the economic performance of
the different distribution centers and help determine the economically optimal level of
errors at the different centers.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/19777 |
| Date | January 1900 |
| Creators | Ali, Jamaa A. |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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