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On this page
  • Schedule generation of commercial reports and send them to BI software or directly to stakeholders
  • Streaming SLA information for order deliveries
  • Monitor overpaid orders
  • Warehouse stock monitoring
  • Filtering data sets by various values on the fly

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  1. Use cases, patterns, templates, examples
  2. Industry-specific use cases

E-commerce & Logistics

PreviousFinance and PaymentsNextCustomer Success

Last updated 8 months ago

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Same as the E-commers deal with customers, suppliers, and orders between them. Therefore it is an industry where an organization needs to know about any good or positive event related to customers' accounts as fast as possible. Therefore receiving such information in a real-time mode is vital for many departments from BI to risk, operations, or compliance. And that's what Ylem is good at.

Schedule generation of commercial reports and send them to BI software or directly to stakeholders

Streaming SLA information for order deliveries

With Ylem it is very easy to monitor KPIs but also SLAs for various cases. One of the examples for e-commerce specifically is to detect orders that are not delivered within SLA time.

Monitor overpaid orders

In case an organization accepts payments from customers with direct bank transfers, it opens a potential for mistakes made in the references or amount.

The following pipeline can help to control and find overpaid orders.

Warehouse stock monitoring

With Ylem you can control the size of your stock and send alerts to the operational teams if it is necessary to restock some items:

Filtering data sets by various values on the fly

Let's imagine we have the following dataset and we need to filter it in various ways:

[
   {
      "amount":250,
      "created_at":"2022-10-01 16:01:11",
      "currency":"USD",
      "id":1,
      "organization_id":1,
      "status":"dispatched",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":250,
      "created_at":"2022-10-01 16:01:11",
      "currency":"USD",
      "id":2,
      "organization_id":1,
      "status":"paid",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":3677.2,
      "created_at":"2022-10-01 16:01:11",
      "currency":"USD",
      "id":3,
      "organization_id":2,
      "status":"completed",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":21.87,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":4,
      "organization_id":1,
      "status":"assembled",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":21.87,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":5,
      "organization_id":4,
      "status":"assembled",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":341.76,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":6,
      "organization_id":1,
      "status":"complete",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":2110.76,
      "created_at":"2022-10-01 16:01:11",
      "currency":"USD",
      "id":7,
      "organization_id":3,
      "status":"new",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":127.89,
      "created_at":"2022-10-01 16:01:11",
      "currency":"USD",
      "id":8,
      "organization_id":3,
      "status":"new",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":127.9,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":9,
      "organization_id":4,
      "status":"new",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":344.44,
      "created_at":"2022-10-01 16:01:11",
      "currency":"USD",
      "id":10,
      "organization_id":5,
      "status":"paid",
      "updated_at":"2022-10-01 16:01:11"
   }
]

Let's look at the following 4 examples:

Filter: 
#(status == "assembled")#

Result:
[
   {
      "amount":21.87,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":4,
      "organization_id":1,
      "status":"assembled",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":21.87,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":5,
      "organization_id":4,
      "status":"assembled",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":67.01,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":12,
      "organization_id":2,
      "status":"assembled",
      "updated_at":"2022-10-01 16:01:11"
   }
]

Filter:
#(status == "assembled")#.amount

Result:
[
   21.87,
   21.87,
   67.01
]

Filter:
#(status == "assembled").amount

Result: 21.87

Filter: 
#(amount < 30)#

Result: 
[
   {
      "amount":21.87,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":4,
      "organization_id":1,
      "status":"assembled",
      "updated_at":"2022-10-01 16:01:11"
   },
   {
      "amount":21.87,
      "created_at":"2022-10-01 16:01:11",
      "currency":"EUR",
      "id":5,
      "organization_id":4,
      "status":"assembled",
      "updated_at":"2022-10-01 16:01:11"
   }
]

Filtering data from large data sets is one of the most common and important tasks for e-commerce operations. One of the powerful instruments Ylem offers for that is with GJSON query language.

💼
financial industry
Transformer
Possible pipeline
An example how to retrieve data as SQL
Generared CSV report
Potential SLA monitoring pipeline
Potential SQL query to retrieve data
Potential pipeline
Potential SQL query
Example of the pipeline
Example of the message to the ops team