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4 Ways to Get the Most Out of Predictive Analytics For Your Supply Chain

Predictive analysis is a practice that involves extracting information from already existing data sets. It can determine patterns and possibly predict future outcomes as well as trends. 

It does not exactly tell what will happen but what might happen. So, looking at predictive analysis, you might question why you should use predictive analytics (big data), Machine learning (ML) and Artificial intelligence (AI) in supply chain management?

Predictive analytics are particularly used in supply chain management because they make the whole process more accurate. They also have high credibility, so you can rely on them without having to worry about problems with accuracy. For supply chain managers, it is important to comprehensively use them to your advantage. It helps to understand and improve the customer experience and thus improves the brand’s standing, which in turn improves  the business value and profits.

Here are a few of the areas in SCM Analytics where ML and AI play an important role: –

  1. Forecasting –

Predictive analytics provide forecasts that are more accurate when compared with other methods of analysis. It works best with other dynamic forecasting models. With the help of ML and AI, predictive analytics won’t just help for long term forecasting, but they also help make with short term demand sensing more accurate as well.

  1. Customer Experience Improvement –

With ML, it is possible to score each customer individually, thus improving customer-centric activities like sales, marketing, customer service, etc. For example, taking real time data from POS system helps to make an accurate decision on customer sentimental or behavior change. AI helps to indicate if a  customer will continue to buy products, and if so, which products,  in the coming days or months. This helps the sales and marketing team to make better decisions.

  1. Competitive Analysis –

With the help of complex ML models, we can do competitive analysis with the help of various data such as promotions, price, social media, ads, etc. Doing such analytics will not only require sales or promotions price data, but also Text analytics on social media like Twitter, Facebook, Google, etc.

  1. Geo-Analytics –

Logistics ares the most crucial part of the supply chain. ML and AI help to optimize routes between the various nodes of the supply chain. The introduction of IoT (Internet of Things) helps to collect various data from the different nodes and it is used as input for optimization. Not only does this help to coordinate the loading and unloading of trucks, it also helps to make decisions at the manufacturing plant if for instance, a truck is running late.  It also helps to take better orders for the products which are on the truck.

We at thinkSCM are building a platform for Machine Learning and Artificial Intelligence which will be integrated to SAP IBP System via HCI through an integration hub. The integration hub is not only to communicate with thinkSCM and IBP, but it will also provide a WebUI frontend to update key figures and communicate with the non SAP Cloud systems like Salesforce, CRM, etc.

In beta release, we will have ML which will analyze historical data such as actual sales, marketing budget, temperature, social data (population density, twitter, etc) and provide results/answers to questions such as: –

  1. Customer forecast
  2. How to improve margins if demand falls
  3. Impact on sales from running promotions
  4. Where to spend promotional dollars

 

Stay tuned and visit thinkSCM to learn more!

(By Ankur Patel)