It’s an age-old problem in business. A mishap occurs, like a flawed batch of products that are produced due to an as-yet-unknown manufacturing glitch.
Downtime to analyze and fix the malfunction could take hours or days, depending on the severity of the problem and whether defective manufacturing parts have to be ordered, shipped, and installed.
Collecting the Data & Describing the Problem
Depending on the nature of the business and the items being produced, even a temporary halt in production could cost a manufacturer thousands, perhaps even millions, of dollars. Where do decision makers turn? Data visualization tools are great first steps.
Data visualization capabilities can strengthen decision making by enabling operational and business leaders to quickly identify the nature of a sudden problem, as well as the factors that are contributing to it.
In the case of the aforementioned manufacturing glitch, data visualization tools can be used to gather all the pertinent information.
That data includes equipment vibration levels, the age of the manufacturing equipment being used, temperature readings and other environmental conditions, maintenance history, and any recent changes in the quality or types of materials being used in production.
Data visualization and predictive analytics tools can be used to assemble all of the applicable information and data inputs available, and illustrate the most likely factors that are contributing to the production errors. One example is excess friction during one phase of the manufacturing process.
Solving the Tough Problems
Visualization techniques can help executives see why the production problem occurred, enabling them to get to the root cause of the issue and take action swiftly. Of course, data visualization tools and techniques can be applied to any number of business issues as they arise.
For instance, a multistate outbreak of E. coli leads public health and agriculture officials to identify the source of the outburst and a means of responding to it.
After gathering medical records – and the ages, locations, and recent dietary patterns of patients who have been treated – visualization tools can help bring to light that the majority of persons suffering from E. coli illnesses had eaten romaine lettuce that was sold primarily through the same grocery chain across different states.
From there, public health and agriculture officials are able to identify the farm, or farms, where the lettuce was produced to ensure that farm workers, distributors, and grocery staff are educated on the proper produce handling and hygiene recommendations.
Let’s consider an altogether different scenario: Executives for a national clothing retailer discover that profits for a popular line of blouses have suddenly plummeted in the Midwest.
Data discovery and data visualization techniques enable business leaders to quickly ascertain that a four-day-only discount on the blouses wasn’t properly reset for nearly 250 stores in a six-state region. This forced store managers to accept the marked (discounted) price on the garments.
By quickly identifying the nature of the problem and making the necessary price corrections in the company’s point-of-sale system and across its affected inventory, the retailer is able to avert any additional losses.