Big data is in every step of your supply chain. When materials are purchased, when pallets are transferred, when alarms go off, when people respond—these interactions and millions more create a network of data rich with invisible rhythms and patterns. Upon careful inspection, this data reveals pain points, redundancies, and successes, allowing you to refine processes, better allocate resources, and increase profits.
Every forward-thinking company will want to harness data, interpret it properly, and use the resulting data visualizations to enact change, but with so much information out there (and tools changing every day), where do you start?
Bit by bit: enabling data in your supply chain
Actionable insights come from powerful visual analytics, which rely on good data. We know data is everywhere, but how can we access, gather, and sift through it? Obvious pieces of data include transaction records, itineraries, meetings notes, etc. However, with the Internet of Things, another layer of data can be accessed. Everyday objects like sensors, vehicles, or name badges can have processors attached, connecting them to the internet and enabling them to send and receive data in real time. The data can then be viewed by a human using a digital interface (like a cell phone app) or analyzed directly by artificial intelligence.
With everyday objects transmitting information, humans no longer need to monitor machine statuses. Instead, objects can monitor themselves—and alert humans only when necessary. For example, a sensor can send a warning when its temperature reaches a certain threshold or when, based on a machine learning prediction, the sensor anticipates maintenance being required and preemptively initiates a repair process. This self-monitoring is called Predictive Asset Management (PAM) and is one of the most dramatic benefits of the Internet of Things.
Clarity in action: how data visualization can simplify your decisions
Although PAM already allows for redistribution of human energy across a variety of situations, data visualization has a myriad of possible benefits to supply chain management processes. Whether it’s simply a graph that helps you gain clarity about what time of day your deliveries are most likely to delay or whether it’s a flowchart demonstrating all previously-used variants in a given process, data presented properly can put you a step ahead when trying to make the best decision.
With machine learning algorithms able to quickly analyze a mountain of RFP response data, vendor selection is much easier than ever before. You could, for example, structure a table with all possible vendor requirements, preferences, or essentials laid out, then instruct the machine learning algorithm to extract data from written content supplied by applicants, then populate your table accordingly. Instead of spending hours evaluating on your own, you can simply read the finished table that displays clearly which applicant presents the most advantageous combination of your requirements.
When delivery vehicles are connected to real-time traffic updates, it’s possible to process both sources of a data using machine learning to optimize delivery routes, reduce fuel consumption, and avoid costly delays.
Data visualizations can be graphs, charts, spreadsheets, or dashboards combining more than one type, all of which can use many sources of data. Dashboards are useful for getting a holistic view of a project or situation, easily highlighting pain points or successes.
Making it happen: digital transformation and the art of change management
With how fast technology and tools change, it’s easy to feel out of the loop, even compared to last week! The idea of modernizing business technologies may seem overwhelming, particularly for long-term employees accustomed to well-established practices and familiar technologies. However, with proper change management, modernization is a smooth process, well-supported by careful planning and step-by-step execution.
Contact us to talk about how we can help you use visual data analytics in your business today.