Ever wonder how Amazon can recommend products that you forgot you needed (or didn’t know you wanted)—and deliver them to you that same day? Thank predictive analytics. With data at the core of their operations, Amazon knows how to effectively leverage their wealth of information through each link of the supply chain.
Since the emergence of the Amazon Effect, companies are recognizing the need to be more creative and more granular to optimize their business. Forecasting from historical data can only take you so far.
For many, these drivers put predictive analytics at the forefront of planning and strategy. Before taking these efforts in house, evaluate your current position. Do you have enough sources for a big-data perspective? Do you have internal expertise to assemble the insights you require? It may be prudent to consider outsourcing the data science — enabling you to stay focused on what your business does best.
Keys to success
To successfully implement data science and predictive analytics, consider these four key factors:
- Understand your objectives. Are you looking to predict costs? Highlight compliance issues? Without a targeted focus, it’s difficult to find the most pertinent combination of data that will produce a model with the highest level of confidence in predictability.
- Data availability. Whether that be from within your organization, from external sources, or some combination.
- Accessible expertise. Knowledgeable practitioners are needed to develop and test the models to extract meaning from the data.
- Set an action plan. You need to understand the actions you will take from the insights you gain, or it is all just more information. Without an action plan to take advantage of what you learn, you are only increasing the volume of data and adding to its complexity.
Avoid pitfalls
Businesses using forecast modeling often limit their view of data to conventional transaction history, and therefore may miss the larger picture. For a broader, more accurate perspective, look at other economic factors and consider unconventional data sources as contributing factors in predictability.
Forecasting and predictive analytics are two separate practices. With forecasting, the goal is to project a forward trend based on historical data. Predictive analytics is more granular. You are predicting behavior, expressed as a percentage of confidence or probability, based on data processed through a mathematical model. This points to the importance of having clean data and as well as the right data for the solution. What other factors could be impacting future behavior? Weather data, employment and interest rates, etc., can all be potential drivers of your model.
Keep data in your view
The industry is currently focused on transparency, more specifically, the transparency of data between a company and its suppliers. As sharing data becomes more prevalent (and necessary), so does the risk of data breaches and cyber-attacks. Make data security a core consideration when transmitting and entrusting your data to a third party.
The information and data you analyze or use for predictive analytics is critical to keeping your supply chain competitive—making it equally important for third parties to apply the same level of scrutiny in handling data they have access to and are transmitting that you do. Anyone transmitting or analyzing your data needs to take appropriate precautions to ensure the data isn’t compromised. If your vendor has a security breach, think of all of the data and information regarding your supply chain that are put at risk.
To hear more about predictive analytics and what it can mean for your business, check out this webinar from Supply & Demand Chain Executive. And for a new source of freight data to inform your predictive model, download the latest U.S. Bank Freight Payment Index.
Bobby Holland is the Director of Consulting Services at U.S. Bank Freight Payment
