Move Over Predictive Supply Chain Strategies, Prescriptive Analytics are Taking Over
Supply chain optimization relies upon being able to interpret collected data effectively and having solutions that address the various needs of the company. Traditionally, analytical reporting has been used to gain logistical information, which can then be interpreted by supply chain management.
However, as more sophisticated technology and processes emerge, many businesses are realizing the benefits of pairing predictive analytics with prescriptive analytics.
What is predictive analytics?
There are two key building blocks for data analytics: descriptive and predictive analytics. With descriptive analytics, data is collected, and the business gets an overview of its status. Predictive analytics then takes the information and, using machine learning (MI) and models specific to the need being addressed, shows potential scenarios based upon this data.
While there are several models which can increase the efficiency of your supply chain, the primary models are:
- Linear Regression – A dataset is used to create a linear line through the center point of the data, enabling predictions based on the relationships between the existing data and the regression line. Linear Regression models use only numeric information.
- Text Mining – Text words are used to search a database for data and specific information. This is the model used by search engines and the find function in many programs.
- Optimal Estimation – Predictions using this model are based upon observed factors.
- Clustering Models – Different groups with similar qualities are focused upon to give predictions. Cluster Models can gather demographics, regional revenue, annual weather, and traffic reports.
- Forecasting Models – Predictions from this model are based on the goals of the industry. Usually, the methods used for forecast models are native, qualitative, quantitative, casual, judgmental, or a time series.
Under each of these primary models, are additional models which can be used. It is critical that businesses have a firm understanding of their descriptive analytics as well as choose the right model to get the proper results.
Growing numbers give better results
Estimations show that nearly 2.5 quadrillion bytes of information are provided daily. IBM has stated that the global market for predictive modeling will cap 10.95 billion by the end of next year. The growth in data, when compared to the information available for data analysis a decade ago, has narrowed the variable margins of predictive analytic models.
Because of exponential data growth, how to process the information has developed. Many supply chain tools have transitioned to the web and data analytics is one of them. Companies can gain better predictive and prescriptive analytics using real-time information.
What is prescriptive analytics?
Prescriptive analytics takes the information provided from the predictive model and goes a step further by providing a solution to the problem being addressed. Prescriptive analytics use mathematics, data mining, statistical algorithms, machine learning, and AI for its decision-making.
Various models of prescriptive analytics are available, offering various levels of artificial intelligence involvement. Supply management will still need to decide based upon the various outputs from the prescriptive analytics program. Because prescriptive analytics models are decision-makers, some models can be integrated with other software to automatically perform necessary tasks.
Are prescriptive analytics needed in your supply chain?
Many of the issues which arise within a supply chain are from a human misinterpretation of data provided. While a supply chain may use predictive analytics, they may handle the plausible scenarios provided by the models with more wishful thinking rather than a practical strategic approach.
Why is this? The answer is simple, we all want the best for our business and so we view things in the best light. But this is not always the best strategy for your supply chain.
Prescriptive analytics are necessary as they provide the best solutions to scenarios that might occur. This eliminates the guesswork of what supply chains should do. Because prescriptive analytics is a decision-making tool, dependable and effective problem-solving is obtainable. Prescriptive analytics remove the habit of doing things how they have always been done and urge the supply chain to implement methods that are the best ways to get things done.
Putting it all together
When reviewing predictive and prescriptive analytics, remember:
- Prescriptive analytics is based on the data from descriptive and predictive analytics.
- Supply Chains must have clean data to gain useful information from prescriptive analytics.
- Decisions are made based upon machine learning and AI to give the best potential outcome.
- Prescriptive analytics must have the proper model to gain factual projections.
In conclusion, prescriptive analytics is a requirement of any supply chain seeking fact-based solutions and data-driven results. By having solutions that are data-driven, supply chains can save time, money, and avoid critical issues which commonly arise in companies which only use predictive analytics.