How you analyze supply chain performance and efficiency is unlimited when you have access to real-time data and accurate information. There are five common sorts of supply chain analytics that you’ll encounter in the ecommerce sector to help you get started.
All of these supply-chain analytics provide far more information than simply identifying problems and trends.
1. Supply Chain Descriptive analytics
Descriptive analytics are statistics measuring past events or data that you already have. This form of analytical measurement uses historical data to spot patterns and assess return on investment.
You might use data from prior orders to determine which items are most popular or sluggish selling. With this information, you may decide whether to end a product or increase the reorder quantity for your most highly demanded goods.
2. Supply Chain Predictive analytics
Predictive analytics use past data, statistical algorithms, and artificial intelligence to anticipate future events.
Predictive analytics can assist firms find any potential changes or disruptions, allowing them to proactively prepare and adapt their supply chains.
Predictive supply chain analytics is a form of predictive analytics that uses historical inventory data to calculate a given reorder deadline while maintaining inventory levels and keeping costs reduced.
Many inventory applications and other tools not only give inventory forecasting insights, but they also allow you to create automatic reorder points to save time and be alerted at the right moment.
3. Prescriptive analytics
Perspective analytics is a method of statistical analysis that uses massive quantities of data processed by computer software to assist decision-makers.
These supply chain analytics may be used to plan the optimal shift for your company. Many times, companies use prescriptive analytics to see when it’s time to outsource fulfillment.
It’s possible that you’ll want to establish an online brand and handle purchases in-house at first, but as your company expands, spending time on orders becomes a waste of time. You may discover that staff time is more effectively spent on revenue-generating activities than on order fulfillment.
When companies reach a high order volume, they frequently delegate the fulfillment to a technology-enabled 3PL in order to save money and time in the long run.
4. Cognitive analytics
Cognitive analytics employ machine learning and artificial intelligence to produce human-like reasoning and decisions from massive amounts of data. This makes the method more effective, allowing businesses to speed up their decision-making process.
AI allows more visibility and integration across networks in ecommerce supply chains, allowing companies to grow their supply chain while maintaining efficiency.
Despite the fact that a lot of AI for ecommerce progress is still in progress, 61 percent of executives said reduced expenditures and 53% said increased earnings.
5. Diagnostic analytics
To Spot a Problem, You Must Look at Overall Performance. In the quest for knowledge as to why mistakes, faults, and delays happen, diagnostic analytics is the term used.
This type of supply chain analysis can help e-commerce firms determine why things like delivery delays, procurement disruptions, and reduced carrier capacity occur.
Supply chain analytics can be used for a variety of purposes, but they all have the same goal: to improve your supply chain efficiency.
For example, Launch Fulfilment started collecting shipping carrier data trends to help online retailers track carrier performance every week at the outset of the epidemic. (The USPS had an average of 182 days in transit during January 2020 through June 2021 shown below.)
Brands may take action to inform their customers about possible last-mile delivery delays and why.