Big Data

Information to better Understand your Business: Who’s your Big Data? Big Data Metrics, what it is, how it works and who benefits.

Overview:

Big Data is still a relatively new term for most within the Supply Chain industry. Big Data can be used in today’s logistics environment to solve many of the challenges in producing and distributing product on a domestic and international scale. The definition of Big Data is still an emerging one. For the purpose of this report, we view Big Data as readable reporting used to enhance operations from both a time and monetary stand point. One major challenge with Big Data usage is getting all the data needed in order to make it valuable in a standardize and easily understandable format. The benefit of Big Data help both the executive and laborer better understand the costs in doing business on a local, domestic and international level.

Make no mistake, Big Data and using it in Supply Chain decisions is making progress but there is still a long way to go on both a micro and macro level. The fundamental source in collecting Big Data comes primarily from sales orders and shipment transactions. We also now have to gather information from a relatively new source with the emergence of Big Data in its raw form via social feeds, such as Facebook and mobile technologies, such as apps. Bringing all these areas into a stream of meaningful information is the key for everyone wanting to improve their business. For any business leader, this data in a report form, contributes to solving the daily challenges of satisfying customers on a consistent basis while inspiring employees to perform at a high level from both a time and cost perspective.

Analytics “We know, we don’t know”

Current Analytics answer questions people know to ask. Asking the questions that help propel a company onto the next level is the hard part for most. One big question companies would like to know sooner rather than later is “how are we perceived in our industry and market, furthermore, is our competition delivering a solution we should be but aren’t?” Using the information we have available allows you to ask the right questions or validate to your customers your benefit to them as a business partner. Understanding your customer on an intimate level from a service vs. costs on a transactional level is critical in today’s environment.

Real Life Story:

I currently have a client importing product from Mexico to the US via truckload. Their average truck load weighs between 30 to 32,000 pounds. After reviewing this over a 90 day period though our freight payment module, we presented a plan to reduce their annual truckload usage by a third simply by increasing the weight of each shipment to the trucks maximum cargo weight. By doing so, this saved them over $200,000 a year in annual truckload expense. If they relied solely on their ERP system for this process improvement, it probably would not have happened since it is very challenging for them to garner this shipping information on a transaction level basis in a clear black and white decision process.

The ability for systems such as Enterprise Resource Planning (ERP), Advanced Planning System (APS), and Supply Chain Execution (SCE) to react quickly to scenarios just outlined above are hard due to the manipulated lines of integration they work off of. The bar is continuing to rise when it comes to understanding how all these software applications improve the order to cash process. However, one fact remains consistent; every company trying to integrate Big Data is looking to incorporate the information in a refined format to solve new problems with measured and stable techniques. Traditional Supply Chains often respond late to emerging challenges, thus making it difficult to ask the right questions at the right time to avoid costly mistakes thus making the Supply Chain a continual work in progress and analytics reviewed.

“Who leads and uses Big Data”

Most Big Data leaders are at the CIO or IT level within most companies, very few Big Data teams are assembled with cross-functional members of the company. The desire or understanding by individuals at this level is interpreting future demand while focusing on current supply. In this type of process it is hard for many who don’t pull from multiple areas of the company to create a Big Data metric to measure current and anticipated future activity.

Making Big Data work within a company takes everyone wanting to learn and thus impacting their specific area within the operation with that knowledge. Most Supply Chain leaders need and want to be involved on all levels of data interpretation, even customer comments through social sites, such as Facebook, to determine where to steer operations in meeting the all-important supply and demand pressures. Using platforms as such will help companies understand more effectively the impact of new product launches, market effectiveness and customer comments concerning product changes. Bringing all this information into an easy to read and actionable metric is key for everyone within the company’s four walls using such information.

“What are the takeaways?”

1. Invest in reoccurring scalable analysis everyone in the company understands and modify to bring value into their part of the product life cycle.
2. No one person or company can deliver a Big Data solution specific to your needs or organization. Working with multiple providers specific to their level of expertise is how you benefit from Big Data initiatives.
3. Big Data metrics need to deliver new value-based outcomes. Using data to understand the current situation and to modify or enhance the future is critical to long term success.
4. Everyone needs to be involved in Big Data initiatives. This should start with key leadership within a company and a mobile quoting coalition supporting the process throughout the complete operation.
5. Start simple, the old analogy “Go Big or Go Home” does not apply in Big Data initiatives. Pick a path that will invite and motivate all who partakes in the journey to share ideas and continually refine the process into a successful conclusion. Remember this should be run like a marathon not a 100 yard sprint.