Retail tire distributors roll out data-driven operations with data warehousing

Who is the client?

Our client is a leading tire distributor from the United States. Their customers are tire dealers and retail shop owners across the country. Working with leading automotive brands and retailers, they strive to ensure seamless inventory management and supply chain operations, while ensuring customer satisfaction. 

Challenges

Being in the industry for decades, the client handles a staggering amount of data through systems to manage their day-to-day tasks—sales, purchases, returns, inventory, customer searches, rebate programs, and forecasting. 

Relying solely on transactional systems, they faced the following challenges.

Too much data lying around: Multiple systems mean multiple data points like invoices, purchase notes, sales data from across the shops, customer rebates data, and lots like this. Besides, they ran customer rebate programs frequently, which required connecting data from multiple applications and heavy processing, where they had to spend too much time and effort.

No central version of the truth: Their compartmentalized view of data didn’t allow them to have a centralized location to access key metrics about business performance. For example, comparing the previous year's sales with the current or comparing the sales of different tire brands. 

Have to wait for reporting data: Our client’s company had multiple functional units, each with its own reporting needs, like what’s mentioned in the above section. However, they had no instant access to such custom reports and often had to wait for their data teams to run queries and obtain information.   

Unforeseeable inventory: Their current data architecture didn’t support advanced and accurate forecasting operations of demand across different locations. Yet, knowing this demand ahead was crucial for a company that sold products from a myriad of tire brands to different sellers across different locations.

What did we do?

We connected data from transactional sources with the help of data pipelines from Azure Data Factory. The automated ETL process facilitated the fast availability of downstream data and insights. Post cleansing and transformation, this data is moved to the data warehouse we built using Snowflake in full and incremental loads. 

Our data analysts also constructed aggregate tables to present granular and  Power BI is utilized for reporting and visualization of KPI metrics they measured related to accounting, sales, and inventory. 

The solution

Moving data from traditional dashboards to data warehouses periodically created the following impact and improvements.

Resolved identity issues: Centralized data handling fixed one of their major issues - multiple unresolved identities, leading to a skewed data representation. They are able to identify their customers and benefit from more accurate retail analytics. 

Dashboards that summon instant action: They no longer follow chaotic reporting methods to get hold of business insights. The dashboards present every metric they measure visually and vividly which they can click, view, and act on.

Blending customer preferences and retail analytics: They track customer behavior and online/offline purchase habits. Combining this with retail analytics, they were able to track customers, how they research and make decisions, and what leads to purchases. All of this helped them understand their customers better and enhance their marketing strategies. 

Demand forecasting: Presenting data in a new way helps them identify patterns within and connect dots like never before. They could understand the details behind the demand for different tire products, and their specifications, which leads to drawing near-accurate predictions about future demand thereby streamlining inventory storage and supply chain. 

Conclusion

Dashboards built on different levels made their decision-making process faster and simpler with a new ray of certainty. Their data is no longer trapped within transactional systems but serves them well through modern retail analytics. The company leadership has admitted that they could be more forward-looking now and receive insights faster without requesting. 

With data warehousing and visualization, they have laid a strong foundation which has led them to move towards innovative data science and AI solutions.