FastGood’s Proposed Geography Analytical Network Solution.
FastGood’s current supply chain network design’s failure to support its stakeholders
has pushed it seeking new network design that can offer a new approach that would
ultimately solve this problem which has rapidly affected the company’s finances. That is
where Geography Analytical solutions, a product of Hewlett Packard, comes in. the product
has been offered to the FastGood via the company’s head of supply chain innovations, Mrs.
Indra Banerjee.
Geography analytics enables the display of data and information about the network to
control the optimization of the chain of supply (Acksteiner & Trautmann, 2013. This
information is displayed on a map. This is done by mapping out locations that will be
engaged throughout the process. These locations include distributions centers and
warehouses. This is followed by imputing relevant information that uniquely distinguishes
these locations (Acksteiner & Trautmann, 2013). The final step involves the categorization of
these locations to allow for easy filtering and maneuvering through them for a distinguished
view of every location through-out the process. This is possible through the application of a
smart directory structure (Acksteiner & Trautmann, 2013).
Based on the shortcomings experienced by the company using the current network
design, the Geography analytical solution seems a capable alternative for the company given
its ability to offer solutions to these areas of concern. First, the company, according to the
database provided by the company’s analytics department, has struggled to monitor the
movement of its product (Craighead, Blackhurst, Rungtusanatham, & Handfield, 2007). This
is demonstrated by the approach adopted by the current network design interface that displays
multiple figures which might be difficult especially after a period that is as long as 52 weeks.
The Geographical Analytics solution gives every point room for data that can be added,
edited, hidden, and viewed at the click of a button (Acksteiner & Trautmann, 2013). This
, would reduce the chances of operators to be overwhelmed when weekly data has piled up by
the end of the last week of the year.
Secondly, given the outlay of information in the database which relatively lacks in
discipline as far as order goes, there is always the likelihood of errors being made during data
input, which is a normal occurrence (Acksteiner & Trautmann, 2013). However, an error
made in the current network design would be difficult or impossible to point out when
realized in very long after it is made. This would prove to be a costly occurrence for the
company in its bid to balance the numbers at the end of a financial year (Craighead,
Blackhurst, Rungtusanatham, & Handfield, 2007). With the Geographical Analytics solution,
however, the error would be swiftly spotted soon after in made. Whether the error is related
to conditions that have been skipped during data input like taxes, the solution’s Artificial
Intelligence can display prompt boxes as soon as the figures become questionable.
The proposed Geographical Analytical solution was primarily designed to support and
enhance network optimization in the supply chain (Acksteiner & Trautmann, 2013). This
means that its most basic role is to reduce the cost and even time spend throughout the supply
chain. The current network design adopted by FastGood is likely to spend more time and
ultimately fail to reduce the costs in comparison to the proposed solution. This is because of
the more complex readings in the exact directions of different locations which include
latitudes and longitudes in comparison to Geographical Analytical solution’s visualized
readings on locations that can be seen on the map.
The proposed solution will be able to offer more as far as demand analysis is
concerned (Acksteiner & Trautmann, 2013). The network design has been built to offer
predictive insight on demand in different areas within the map. This is unlike the current
network design that bases its demand highlights based on recent company activities,
especially sales. The proposed solution will highlight demand in different areas and also
FastGood’s current supply chain network design’s failure to support its stakeholders
has pushed it seeking new network design that can offer a new approach that would
ultimately solve this problem which has rapidly affected the company’s finances. That is
where Geography Analytical solutions, a product of Hewlett Packard, comes in. the product
has been offered to the FastGood via the company’s head of supply chain innovations, Mrs.
Indra Banerjee.
Geography analytics enables the display of data and information about the network to
control the optimization of the chain of supply (Acksteiner & Trautmann, 2013. This
information is displayed on a map. This is done by mapping out locations that will be
engaged throughout the process. These locations include distributions centers and
warehouses. This is followed by imputing relevant information that uniquely distinguishes
these locations (Acksteiner & Trautmann, 2013). The final step involves the categorization of
these locations to allow for easy filtering and maneuvering through them for a distinguished
view of every location through-out the process. This is possible through the application of a
smart directory structure (Acksteiner & Trautmann, 2013).
Based on the shortcomings experienced by the company using the current network
design, the Geography analytical solution seems a capable alternative for the company given
its ability to offer solutions to these areas of concern. First, the company, according to the
database provided by the company’s analytics department, has struggled to monitor the
movement of its product (Craighead, Blackhurst, Rungtusanatham, & Handfield, 2007). This
is demonstrated by the approach adopted by the current network design interface that displays
multiple figures which might be difficult especially after a period that is as long as 52 weeks.
The Geographical Analytics solution gives every point room for data that can be added,
edited, hidden, and viewed at the click of a button (Acksteiner & Trautmann, 2013). This
, would reduce the chances of operators to be overwhelmed when weekly data has piled up by
the end of the last week of the year.
Secondly, given the outlay of information in the database which relatively lacks in
discipline as far as order goes, there is always the likelihood of errors being made during data
input, which is a normal occurrence (Acksteiner & Trautmann, 2013). However, an error
made in the current network design would be difficult or impossible to point out when
realized in very long after it is made. This would prove to be a costly occurrence for the
company in its bid to balance the numbers at the end of a financial year (Craighead,
Blackhurst, Rungtusanatham, & Handfield, 2007). With the Geographical Analytics solution,
however, the error would be swiftly spotted soon after in made. Whether the error is related
to conditions that have been skipped during data input like taxes, the solution’s Artificial
Intelligence can display prompt boxes as soon as the figures become questionable.
The proposed Geographical Analytical solution was primarily designed to support and
enhance network optimization in the supply chain (Acksteiner & Trautmann, 2013). This
means that its most basic role is to reduce the cost and even time spend throughout the supply
chain. The current network design adopted by FastGood is likely to spend more time and
ultimately fail to reduce the costs in comparison to the proposed solution. This is because of
the more complex readings in the exact directions of different locations which include
latitudes and longitudes in comparison to Geographical Analytical solution’s visualized
readings on locations that can be seen on the map.
The proposed solution will be able to offer more as far as demand analysis is
concerned (Acksteiner & Trautmann, 2013). The network design has been built to offer
predictive insight on demand in different areas within the map. This is unlike the current
network design that bases its demand highlights based on recent company activities,
especially sales. The proposed solution will highlight demand in different areas and also
