Optimizing the supply chain with BIG DATA

We stated previously on our blog that “big data”might be the future in terms of how we do transportation and where the logistic industry is moving. But what is big data? Big data is huge. Big data gathering takes place everywhere: Google, Facebook, Twitter, Instagram, Youtube and many, many more. Almost every action one can take in the internet is being recorded and stored for someone to analyse. And how big is “big data”? IDC says that in 2010 (6 years ago!!!), the size of digital universe became bigger that a zettabyte (bigger than 1,000,000,000,000,000,000,000bytes).

After considering how big “big data” is, we should first draft a strategy on how to implement “big data” into ones business before we become specialized on the logistics parts.
First, the business should set clear goals for what big data should be used. It is crucial that Data scientists, analysts and developers work in close collaboration to identify how big data can be used to achieve what. Secondly, it is imperative that data becomes operational from the start, meaning data should be transformed into a usable action plan while being as accurate as possible. Data inaccuracy was and still is today, especially when considering the vast amount of data, a huge problem.
Thirdly, having error-free data becoming action,it is time to iterate the process of data management. Iterate the data enriching and refining by adding new data and eliminate unnecessary and inaccurate data from your “data management pipeline”. While looking for trends and patterns within these final data sets, continue this iteration until you can use a vast amount of data and transfer it into an action that add value to your business.

How can this data management of big data be put to use for logistics? How will it affect supply chain providers, its solutions and its technologies? Once this process is established, big data can support data warehouse optimization, 360-degree customer analytics, real-time operational production and delivery intelligence and many, many more.

Sources that create data in the whole supply chain are various. An overview of data sources can be seen in the picture (Figure1) below. The figure further shows how important the data is from its source and how good it can be utilized:
Figure1. Importance and Current Performance of Big Data Projects in Companies with Big Data Initiatives

big data. jpg



Click to access Big_Data_Report_16_JULY_2012.pdf


However, while many firms have noted the tremendous potential of Big Data for supply chain management, many of them have not yet integrated it into their operations because they lack the financial, technological or human resources to do so. While these are clearly challenges, it is estimated that the digital universe will be over 40 trillion gigabytes by 2020 – a significant portion of that being data that can be leveraged to generate business insights. As time passes, those firms who have integrated Big Data into their supply chains, and both scale and refine that infrastructure will likely have a decisive competitive advantage over those that do not.

Figure2. Supply Chain Management and Big Data




Click to access Big_Data_Report_16_JULY_2012.pdf

The aspects that could be improved by the Big Data are the possible benefits for the supply chain management, customized production and service, automatic sourcing, optimized pricing, etc. Although there are countless opportunities to leverage big data, we will focus in this post explaining the real-time delivery tracking, where Big data management systems can be used to strengthen fulfilment, by hardware devices as well as through software for warehousing and processing, the data inputs from bar codes, radio frequency identification (RFID), GPS.

These systems are able to capture traffic sensor data, road network data and vehicle data in real time.It will allow logistics managers to optimize the capacity schedule delivery.One of the advantages will be the possibility of address the unforeseen, for example, track packages and vehicles in real time (no matter where they are), automated notices sent to customers in the event of a delay, and provide customers with real-time delivery status updates in case of traffic jams and many more

Besides, vehicle sensor information can be used for predictive maintenance, which will increase the life of business equipment, by scheduling preventive maintenance based on current and historical data. Others examples con be found in the article of How to Optimize Supply Chain Management with Big Data, such as “a firm can configure its transportation business intelligence system to route notification of delivery delays to customer service centres automatically; customer service representatives can then anticipate, and respond to, customer complaints appropriately.”

Nevertheless many are still the knowledge require into a company to get the maximum profits into the supply chain management throughout the Big Data.

Some of them:

  1. A data warehouse system for Hadoop providing a SQL interface but also allowing the plug-in of other custom MapReduce programs.
  2. Parallel Processing. Distributing data and business processing across multiple servers simultaneously to reduce data processing times.
  3. Pattern Recognition. Techniques to sense patterns in data that can be used in decision making
  4. Survival Mining. A use of predictive analytics to identify when something is likely to occur in a defined time span








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