Seeing the Forest the Trees
By JACK LEVIS, Director of Process Management, UPS
Smart companies know that business analytics help them to make better decisions. According to a study by the MIT Center for Digital Business, the organizations driven most by analytics had four percent higher productivity rates and six percent higher profits. Manufacturers are aware of the importance of analytics as well. In the recent IDC Manufacturing Insights’ 2012 U.S. Supply Chain Survey, big data and analytics were ranked as the most important supply chain pillar by U.S.-based manufacturers. Even as manufacturers acknowledge the importance of analytics, many are still unsure about the best ways to incorporate this business intelligence tool into their supply chain operations.
First, it’s important to understand that business analytics is a scientific approach to making decisions and is traditionally broken down into three forms: descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics provide an accurate picture of what has happened. Predictive analytics take this information one step further to enable an assumption about what will happen in the future or explain what has happened in the past. Finally, prescriptive analytics shows the best course of action to take to optimize the business operations.
Today, decisions are more complex and need to be made faster than ever before, creating an environment where business analytics, specifically predictive and prescriptive analytics, can be a major competitive advantage.
Inside UPS: Making the Most of Predictive Analytics
UPS has a long-standing culture of leveraging analytics for significant business impact and was one of the first companies to do so. From this experience, UPS analysts know that a crucial element of predictive analytics is identifying key cause-effect relationships and underlying factors that may not be apparent to a casual observer.
For example, UPS used predictive analytics to reduce maintenance expense and increase reliability of its fleet of more than 70,000 vans by installing sensors to monitor key automotive characteristics. For UPS, vehicle breakdowns can be very expensive, so the company used to follow a rigorous preventive maintenance schedule. Unfortunately, breakdowns do not follow a time table and the preventive maintenance schedule did not increase reliability commensurate with its cost. As a result, UPS developed a predictive system to collect data from its fleet and analyze it. Based on the analysis, UPS developed a model to predict vehicle failures. Now, thanks to its telematics system, UPS can predict when a failure is imminent and take individually tailored corrective actions. By employing a basic understanding of predictive analytics and applying it to a unique company problem, manufacturers can find ways to utilize data to generate similar cost and time-saving results.
UPS’s most significant and recent success in improving its business process management has stemmed from the development of predictive analytics tools as part of an internal suite of systems known as Package Flow Technology (PFT). Based on statistical analysis of historical data on previous package deliveries, PFT allows UPS to forecast resources (drivers, loaders, etc.) needed before the packages show up in its delivery centers and plans for their efficient delivery. It continuously fine tunes its plans as the time for the delivery approaches and more information becomes available.
Prescriptive Analytics in Action
The realm of prescriptive analytics is more challenging when compared to descriptive and predictive analytics because prescriptive analytics require implementation, which affects many people across multiple levels. Another example of analytics in action at UPS is the use of PFT to create efficient sorting and loading plans and optimize drivers’ delivery routes. For UPS to really benefit from this technology, the routes developed by PFT should be implementable. In other words, UPS drivers should be able to serve their customers exactly as prescribed by PFT.
This proved challenging, even for UPS. The model that UPS needed in order to develop optimized route planning models for drivers was extremely complex and the existing solution methodologies were not suitable to meet the size and scale needed for UPS operations. As a result, the UPS research and development team spent a considerable amount of time developing and field testing new solution methodologies. Today UPS has a proprietary, state-of-the-art methodology that has been rigorously tested over the past few years.
Once the routes are optimized, UPS provides information to drivers on their handheld computers, known as DIADs (Delivery Information Acquisition Device), to enable them to make better decisions based on real-time data. Central monitoring stations receive real-time updates of the on-road operation which enables UPS operations to run faster and more efficiently.
Analytics for Any Operation
Understandably, the level of resources required for UPS’s analytics program may not be realistic for every organization. Fortunately, advancements in cloud computing technologies allow more companies to hold and analyze data of these proportions. Additionally, common software applications such as Microsoft Excel have a tremendous amount of analytics capabilities, as long as organizations recruit talent with the proper knowledge-base to use them.
For manufacturers looking to develop their own analytics program, keep in mind a few best practices:
Never underestimate your data needs. Always allow enough time and resources to make sure your data is accurate — including allowing time for prototyping. In many ways, advanced analytics is similar to drilling for oil in that you can’t be sure of what you are going to find until you start drilling down.
Focus on implementation. A great solution that is not implemented is worthless to business. Rather than solving one large problem, solve a series of smaller problems. In addition, it’s okay to leave a user in the loop by allowing the selection from multiple options vs. providing the one final answer.
Know where you stand. Analytics metrics are leading indicators, not lagging. If you implement a change based on findings resulting from prescriptive analytics, follow up to make sure those changes have the desired measurable effect.
Don’t underestimate the people side. Familiarize yourself with professional associations such as INFORMS, as well as academic organizations whose members are devoted to the growth and development of the field of analytics. These are the best places to bounce around ideas, gain advice, learn about new developments in the field, and even recruit staff or interns for additional help.
No matter the size of the company or the budget, some degree of analytics is always within reach. Determine the goals, determine the value, and then find a way to get there.
The Value of Seeing the Forest & the Trees
UPS utilizes analytics because it allows decision makers to see the forest from the trees, separating relevant facts from the noise, thus providing valuable insight and revealing patterns that lead to predictive and prescriptive information. Getting the big picture from the data is key to fact-based decision making which, when combined with knowledge and expertise, can result in a highly efficient and competitive operation.