I have been told for four years now that Industrial
Engineering is a “broad” degree. To me, it seems like there is one obvious
option for a career choice—manufacturing, or there are other areas that we
don’t focus as much on in our curriculum. I wish that I knew more about the
supply chain, logistics, and transportation side of Industrial Engineering
because I find that much more exciting than the manufacturing route. Needless
to say, while looking for jobs, I have looked into transportation type jobs. We
have talked about how big data analytics can apply to so many fields that I
wanted to look into how it applies to the transportation field.
I found an article in Data
Informed by Lora Cecere, the founder of Supply
Chain Insights, titled “Five Supply Chain Opportunities in Big Data and
Predictive Analytics” that discusses areas in which the supply chain world
needs to step up their game. As of now, supply chains are inflexible and are
not good at responding to changes because they run based on past data. There
are five suggestions for how to use today’s technology to improve the supply
chain industry.
1. Mobility:
People today are used to being able to receive data at their fingertips no matter where they are in real time. Now that it is cheaper to perform computation, those in the supply chain industry need to take advantage of being able to compute and analyze data on the go. With mobile devices, it is possible to work anytime at any place, rather than waiting on computation overnight. Overnight computation means that the data analysis is delayed—which needs to be avoided.
People today are used to being able to receive data at their fingertips no matter where they are in real time. Now that it is cheaper to perform computation, those in the supply chain industry need to take advantage of being able to compute and analyze data on the go. With mobile devices, it is possible to work anytime at any place, rather than waiting on computation overnight. Overnight computation means that the data analysis is delayed—which needs to be avoided.
2. Internet of things:
Mobility and easy access to information leads to the evolution of internet of things. From what I understand, the phrase “Internet of things” refers to the idea that defects and problems will be detected as they happen. There will be no need for checkups (whether we’re talking about machine maintenance or those with the doctor) because sensing technologies will be able to detect changes in performance in real-time. Supply chains will be designed in a way that allows them to respond based on their specific needs, rather than what is expected.
Mobility and easy access to information leads to the evolution of internet of things. From what I understand, the phrase “Internet of things” refers to the idea that defects and problems will be detected as they happen. There will be no need for checkups (whether we’re talking about machine maintenance or those with the doctor) because sensing technologies will be able to detect changes in performance in real-time. Supply chains will be designed in a way that allows them to respond based on their specific needs, rather than what is expected.
3. Big data:
We all understand the basics of big data analytics at this point. Data comes in such large quantities, often messy and unformatted. While supply chains are designed to run on structured data, the data that is most useful is often not structured. This valuable data comes in the forms of customer reviews and Twitter data, which we all know is not standardized. The supply chain industry needs to take advantage of the big data analytics methods that we have discussed so often. By decreasing the time that it takes to analyze these large amounts of data that reflect the customers’ satisfaction, the supply chain industry will be able to better satisfy those that they are trying to reach.
We all understand the basics of big data analytics at this point. Data comes in such large quantities, often messy and unformatted. While supply chains are designed to run on structured data, the data that is most useful is often not structured. This valuable data comes in the forms of customer reviews and Twitter data, which we all know is not standardized. The supply chain industry needs to take advantage of the big data analytics methods that we have discussed so often. By decreasing the time that it takes to analyze these large amounts of data that reflect the customers’ satisfaction, the supply chain industry will be able to better satisfy those that they are trying to reach.
4. New forms of predictive analytics:
Relating to big data, predictive analysis needs to be applied to the large amounts of data that are received in the supply chain field. Predictive analysis can be based on text mining techniques as well as finding trends within the data over time. Not only does the data need to be organized and cleaned up, but also needs to be used to make predictions for what is to come.
Relating to big data, predictive analysis needs to be applied to the large amounts of data that are received in the supply chain field. Predictive analysis can be based on text mining techniques as well as finding trends within the data over time. Not only does the data need to be organized and cleaned up, but also needs to be used to make predictions for what is to come.
5. The cloud:
With the use of cloud computing, the supply chain industry has hope. The cloud allows for continuous computation. Benchmarking will no longer be something that is a reflection of past data; rather, it will be something that is a real-time indication of how the company is doing. We have discussed how cloud computing makes things so much easier because it can handle so much more data at one time than a traditional machine. There is no exception when considering cloud-based computing in the supply chain field—the faster the computation, the better.
With the use of cloud computing, the supply chain industry has hope. The cloud allows for continuous computation. Benchmarking will no longer be something that is a reflection of past data; rather, it will be something that is a real-time indication of how the company is doing. We have discussed how cloud computing makes things so much easier because it can handle so much more data at one time than a traditional machine. There is no exception when considering cloud-based computing in the supply chain field—the faster the computation, the better.
*This article is about a year old so some of these ideas discussed may already be in place more-so than they were at the time it was written.
Sources:
http://data-informed.com/five-supply-chain-opportunities-in-big-data-and-predictive-analytics/
http://data-informed.com/five-supply-chain-opportunities-in-big-data-and-predictive-analytics/
Five Challenges of Managing Big data in Supply Chains:
ReplyDeleteLogistics and supply chains are the richest data domains around. With data doubling every 18 months, how can companies manage the scale, quality and security of the data in the supply chain?.The rise of complex and global business networks means that a majority of the data will be generated outside a company’s firewall. Companies face the following five challenges as they manage big global data in the supply chain and strive to improve collaboration, automate processes and increase the visibility and efficiency of supply chains.
1. Eighty percent of the supply chain data set is outside the enterprise
Only 20 percent of a supply-chain data set is internal and 80 percent is contributed by external partners. without access to 80 percent of the supply-chain execution data, companies are starved for a detailed transaction record of the end-to-end life cycle of a transaction as it executes across multiple partners and suppliers.
2. Most companies are supply-chain data blind
Because 80 percent of the data is outside their own ERP systems of record, most companies are supply-chain data blind. While 95 percent of the orders get to their destinations more or less on time, the remaining uncertainty means companies have to carry more inventories. Companies that are global which is where pretty much every industry has gone have especially long lead times. These are complex and risky supply chains. As a result, companies lack a unified view of how the product is moving through the supply chain.
3. Supply chains in different industries use radically different models
The supply chain of a major electronics manufacturer is very different from the supply chain of an apparel/footwear company. They have very different drivers, business models and business rules. the benefits of fixing a data feed for one customer in one industry does not flow to another industry even though the data feed is common across both industries.
4. Garbage in, garbage out
Common data standards don’t exist. While most data is structured, much of it is proprietary and comes in a variety of formats including XML, flat files and spreadsheets.
The data source origins are not reliable across all customers. If they have a quality data set that covers the transactional details of order fulfillment or the purchase-to-pay process, they can begin to glean actionable insights such as how their supply chain is operating, what they need to do to change it, whether they are sourcing in the right areas, whether they are working with the right providers, etc.
5. Lack of common platform for collaboration and building communities
With their current disparate systems and lack of a unified informational model, companies cannot enable massively scalable information-sharing or build collaborative communities across their network of partners.
Once communities are put together, they can participate in sharing best practices to improve data quality. They can start a blog, post a comment, and send a signal. This allows participants along the supply chain to collaborate around business topics.
Sources: http://sandhill.com/article/five-challenges-of-managing-big-data-in-supply-chains/
Dear Brianna,
ReplyDeletePlease come to my office to discuss some of the existing opportunities of IE in the analytics of transportation. I also encourage you to have a look at the following references:
http://www.physicalinternetinitiative.org/ (make sure to check the links)
http://www.analytics-magazine.org/special-articles/424-supply-chain-analytics-becoming-more-and-more-important
Safety Analytics (let us discuss)
http://onlinepubs.trb.org/onlinepubs/millennium/00044.pdf (TRB is a great reference for the latest in transportation research)