Data is one of the most valuable commodities for all businesses. Gathering data about a company’s products, sales history, suppliers, etc., and then applying supply chain analytics helps reduce costs, make better decisions, and create new products and services to meet customer needs.
Advanced analytics in supply chains
Data analytics is used in all aspects of supply chain management (SCM). It enables organizations to respond quicker, increase efficiency, and gain greater integration across the supply chain. You can use any of the four main types of data analyses–descriptive, diagnostic, predictive, and prescriptive–to gather the insights necessary for your business:
Descriptive analytics tells you what has happened in your supply chain, by consolidating and classifying data through similarity and differentiation. It involves no uncertainty; it finds the reasons behind past successes and failures. However, it relies heavily on the human review of data and does not involve robust techniques to predict the future.
Diagnostic analytics infers the causes of outcomes and compares the effect of different variables on them. Diagnostic analytics has a certain level of uncertainty due to inconsistencies, data scarcity, unknown factors, data sampling, and preparation techniques. Although it may not be able to predict future events, finding the cause of why something occurred helps make improvements and prepare for similar occurrences in the supply chain.
Unlike descriptive and diagnostic analytics, which look at the past, predictive analytics looks forward. It helps forecast future events, quantities, or times at which events might happen based on diverse variable data. It offers you great insights by relating contextual data with other customer behavior and web server data.
Predictive analytics helps you anticipate events and be proactive by evaluating multiple scenarios to optimize your supply chain using the following advanced techniques:
- Data mining: Which data are connected?
- Pattern identification: What pattern, or lack thereof, deserves an action/correction/adjustment?
- Monte Carlo simulation: What could happen?
- Forecasting: What if the trends persist?
- Root cause analysis: Why did this happen?
- Predictive modeling: What might happen next with various variables?
Prescriptive analytics understands what has happened and why, and presents you with many “what could happen” scenarios to help make decisions. It is used when there are too many options, variables, constraints, and data points in play.
Prescriptive analytics can be complex and difficult to manage. You must use advanced models, scenarios, and simulations with known and random variables to understand the range of possible outcomes. If used well, it can help you optimize inventory, production, and customer experience.
Forecasting and data analytics
The process of analyzing data using sophisticated techniques and tools to gain deeper insights is known as advanced analytics. Of these techniques and tools, simulation is best when human limitations or mathematical complexity cause others to fail.
Monte Carlo simulation is an algorithmic approach that considers thousands of scenarios, calculates their outcomes and the likelihood of their occurrences, and consolidates the results based on random samples. Since it is a predictive technique that accounts for uncertainty in independent variables, it is great for handling large volumes of diverse data.
Risk analysis based on Monte Carlo simulation helps you make great contingency plans, fulfill customer demand, and increase profitability. It can also be easily adjusted to the changing circumstances in markets.
In the absence of analytics, data becomes unwieldy. If you apply intelligent analytical tools and sophisticated algorithms to it, you can draw deep insights and then plan ways to boost business growth. Ensure you invest in a robust software solution that leverages great data analytic methods and provides you with the insights you need.