Enabling Better Decision Making: Streamlining Distribution Centre Data Systems for a Leading Retailer
During these uncertain times, it is imperative for businesses to control the effects of shock events by implementing cost optimization plans. With the significant business-wide decline in budget and resource allocation, streamlining processes is the needed strategic response.
Mu Sigma helped one of the largest retailers in Australia streamline and centralize financial reports across multiple distribution centers to enable effective decision making and cross-learning.
The Mu Sigma Approach
The Supply Chain team wanted to automate report generation across multiple distribution centers. They had multiple distribution centers (DCs) that follow distinct process of reporting KPIs. Emerging business complexity and volatility amongst these distribution centers eventually led to these processes becoming obsolete.
Through our Art of Problem Solving (AoPS) approach, we pinned down the underlying problem of the consumption of standardized processes and reporting structure.
1. KPI identification and rationalization
Due to the lack of standard reporting, each DC assumed their process of reporting KPIs to be correct. This lack of unified process limits visibility in operations and productivity across DCs.
As the retailer’s transformation partner, we aimed to bridge the gap between the current state and the desired state enabling budget optimization, cross-learning, and a streamlined process, all while providing 360-degree support.
• Limited visibility into DC productivity and remuneration
• Difficulty in measuring the performance of each DC
• Trade Architecture: Should the promotion be supported by features like ads and on which channel?
• Comparing DC’s with each other wasn’t possible
• Holistic visibility across Distribution Centre operations
• Enable effective decision-making through streamlined coordination
• Focus on key productivity and remuneration metrics supported by a central function
2. Identifying data sources
Data sources required to build a standardized reporting framework were identified through alignment from stakeholders.
Automated data extraction was done from 11 data systems by web scraping. Once the data sources were identified – the next step was to clean the data, identify the calculation for key metrics and finalization on data sources for various KPIs.
3. Report design creation and iteration
Insight-led decision making, metric calculation and backend dataset creation was done. We compared various data sources for individual KPIs, highlighting the similarity and differences.
While implementing the updated process top-down, organizations must zoom out to ensure they stay true to their outcomes in the long-term while creating an impact in the short-term. The reports were initially tested within a focused group and then scaled up to the entire DCs.
4. Report automation
We automated KPI calculation and report generation using Python and VBA. The end-to-end automation of reports enabled the adoption of daily forecast process and the weekly upload of numbers helped daily comparison of KPIs against the target.
The new reporting process also maintains a central data repository for historical records to ensure data integrity. This makes the process scalable and adaptable in case a new DC is created.
5. Driving consumption of the new streamlined process
Once the processes were streamlined, consumption of standardized processes and reporting structure were enabled through alignment with stakeholders, feedback incorporation and system fixes.
All these steps helped us in providing a smooth transition to stakeholders by sending daily and weekly comparison reports and by exhaustive process documentation.
Along with automating report generation for all DCs with ~100 standardized metrics to improve data integrity through the elimination of manual interventions and streamlining data systems, we also:
• Streamlined on-floor Distribution Centre processes
• Implemented Zero-touch automation of financial reports to ensure no human intervention is required
• Set up a daily forecast process which allows for proactive action through daily comparison of KPIs for the week against the set target
• Improved data integrity by standardization of data systems
Upon deriving insights from the reports, clients can:
• Have holistic visibility across operations – tracking and optimizing resource utilization
• Benchmark Delivery Centres – drive cross-learning amongst Delivery Centres
• Calculate remuneration of employees, clearly define different types of working hours based on operations performed
Through our out-collaboration initiatives with the client, we were able to show:
• Reduction of effort: Saving ~90 hrs/week
• Reduction in cost per year: ~$400K
Click on the link to know about how we helped the client cut supply chain losses with the proposed solution.