Title: Sr. Director, BSP Services & Technology , Walgreens Company
Walgreens Company, an American company, operates as the second-largest pharmacy store chain in the U.S., Walgreens specializes in filling prescriptions, health and wellness products, health information, and photo services. The company operates 9,277 stores. Founded in Chicago, in 1901, the Walgreens HQ is in the Chicago suburb of Deerfield, Illinois. In 2014, the company agreed to purchase the remaining 55% of Switzerland-based Alliance Boots that it did not already own to form a global business. Under the terms of the purchase, the two companies merged to form a new holding company, Walgreens Boots Alliance Inc., on December 31, 2014. Walgreens became a subsidiary of the new company, which retains its Deerfield headquarters and trades on the Nasdaq under the symbol WBA.
Walgreens experienced a high volume of Not on File (NOF) errors at its retail stores, causing more work for the store employees. Walgreens stores have a huge inventory of drugs and other products. Currently, some products may not have either a price or product codes in the system, which results in the NOF error. This creates several financial and customer experience challenges.
Financial Impact: If store employees don't know the price of the product, they manually key it in and complete the transaction with the customers. A product that has an error would be either sold for less or more than the actual price, which impacts revenue.
Bad Customer Experience: NOF errors often lead to higher check out time and manual override results in huge price variations.
Impact on Store Operations and productivity: Store employees and IT support staff spend time in raising and resolving a “not on file” error. It is observed that a store employee spends at least 5 mins for raising an incident on NOF issue.
Challenges with the existing approach
Raising a ticket at the store level is cumbersome and personnel will often miss a few NOF issues while raising an incident or they will consolidate the NOF scenarios and raise the incident once or twice a day. Due to this, valuable time is lost and store personnel who are involved in raising the incident will have to let go of their actual work or have to put up work extra hours to compensate for it.
Walgreens implemented Digitate’s ignio™ solution to compare the incoming transaction data by detecting the anomaly and send the anomaly data to the Walgreens SAP central application for applying a fix, which is done by updating and pushing the article/price from the SAP central application to Point of Sales (POS) application at stores. After 12 hours, ignio performs the validation check at the stores’ POS application on whether the article/ price push performed at SAP central has reached the store or not. Then ignio notifies the stakeholders about the successful and unsuccessful article/ price push to the stakeholders.
Before the ignio™ solution, the approach was reactive and required lots of manual coordination among various teams involved (IT RunOps, SAP RunOps, POS RunOps, business management teams, etc.). It was prone to errors and valuable time is lost in the process. Also, there was a chance that duplicate entries were populated in the system.
Determining whether the SAP Push action was successful or not is crucial for the business. Delay in determination means there are chances that the push was not successful and the NOF issue still exists in the system and its detrimental to the business.
With ignio, we decided to address this issue proactively before incidents are raised at the store level. We connected with various teams to understand what happens when a store personnel manually keys in the information for NOF articles in the POS register. We found that there are different applications where ignio can tap the NOF information, like SAG (Customer Specific Authentication Layer) and SAP CAR (Customer Activity Repository).
By designing a solution that gathered data automatically from multiple systems, ignio helped to bring down the SLA for NOF resolution from 3 days to just 12 hours.
- Brought down the SLA for NOF Resolution from 3 days to 12 hours
- Revenues saved/ Avoiding Revenue loss with reduced SLA
Business Transaction Benefits
- Average time lag (No. of days) between manual override (Identified the first time) can be NOF corrected. (eg: Allerlife dietary supplement pill has been sold with an overridden price to 20 customers across 5 days at a NY store)
- Average number of sales done based on an overridden price for an item (eg: Allerlife dietary supplement pill has been sold with an overridden price to 20 customers across 5 days at a NY store)
- No of NOF Incidents created every day – 500 incidents per week for 9000 live stores
- Average time taken to create an incident by store user – 5 mins
- Average volume of overridden transactions with missing price across stores per day – ~700 transactions/ day
- Every Store end up creating multiple duplicate incidents for same article - For few instances UPCs were overridden 10 k times for an article
- Any overridden sale could potentially be a breach of privilege – Attract legal compliance lawsuits.
Walgreens used Digitate ignioTM AIOps for parsing the incoming data from SAP CAR system, which was stored in the Postgres db for logical manipulation and decision making and report generation. AIOps has also been interacting with the Walgreens central POS system through a handheld service. Then we have AI.ERPOps which mines data from SAP CAR and interacts and triggers action SAP S4/ HANAsystems. Both ignioTM AIOPs and ERPOps are e-bonded with a custom wrapper function.
- The NOF Data flows through different systems and applications like POS local, PCMS, SAG, SAP PO, SAP CAR, SAP BW. Identifying and tapping the right data source at right time was the key to determining the proactiveness of this approach.
- Handling multiple data formats was a challenge. For example, converting the incoming tlog CDS data file into string and then to .csv format for consumption of AIOps. Similarly, when AIOps passes the CDS data to POS HH, the received output from POS HH will be in xml format. ignio™ AIOps had to parse the xml data.
- Determining the actual wait time when the NOF anomaly detection data being transferred from ignio proxy server to SAP S4/ HANA server path and to fetch the acknowledgment data from SAP S4/ HANA Server path post SAP action and transferring back to ignio proxy server.
- Since multiple systems were involved, the availability of test data at lower environment was a challenge. There were instances that testing was successful in one of the lower environments but when we started testing in one of the higher environments it failed due to inconsistencies in the test data formats.
This is one of the most complex use cases with respect to business and automation that Digitate had previously handled. There were moments the collective team were in doubt of whether we could meet our project targets. But thanks to Digitate’s agile-based sprint approach, we were able to overcome all our hurdles and challenges on time and able to meet our delivery targets.
Check 3:17 - https://digitate.com/wp-content/uploads/2022/06/Digitate-Steve-Walgreens-v3.0.mp4
About Walgreens Company
WBA is an integrated healthcare, pharmacy and retail leader serving millions of customers and patients every day, we play a critical role in the healthcare ecosystem.
A trusted, global innovator in retail pharmacy with approximately 13,000 locations across the U.S., Europe and Latin America, WBA plays a critical role in the healthcare ecosystem.