SPAR is a global food store chain with over 12000 stores in 38 countries across 4 continents that serves over 10 million consumers every day. SPAR Hypermarkets and supermarkets in India is the result of a license agreement between the Dubai-based Landmark Group's Max Hypermarkets India and SPAR International. Max Hypermarket India, with 16 stores in 9 locations, is a leading footprint hypermarket chain in the country.
Most of the IT endeavors of the company so far were in the space of process automation. However, given the growth of the retail landscape in the country, they had to embrace certain strategic initiatives in a bid to make the business more competitive, efficient and profit-oriented. The need of the hour for them was to have not just more stores, but to sell more, retail efficiently, increase margin yields, to stock optimally, and to buy smart.
They needed an effective decision support system (DSS) to assist business unit owners to make informed decisions. The silos of transactional data compiled by Max on a daily basis had the potential to help make data-driven decisions.
The project implemented
‘Right from the offset, we wanted to have a business solution and not a technology platform. This is why we embraced BIRetail, a solution which is built specifically for the retail vertical,’ says Sunil Nair, Head Technology & Business Solutions - SPAR. Data integration was well architected through a deep understanding of the data elements in a retail business scenario. The data warehouse and OLAP architecture was pre- productized and fine-tuned to ensure better value and higher response time performance.
The solution has proprietary KPIs that provided pivotal insights to the health and functioning of various facets of the business. Further, it provides a library of retail-specific reports, dashboards and specific use cases that bring business benefits to the customer. The ERP solution of Max, which was in a proprietary format, is integrated with the DSS.
'The user interface of the solution is highly intuitive and non-technical,' says Sunil. 'This encouraged business users to embed the usage of this solution in their daily business operations, without incurring massive technical training sessions,' he adds. Scheduled reports and exception alerts were configured from the solution to proactively touch the business users, even without them logging into the solution.
The business benefits
The company started gaining tangible user benefits from this deployment within 20 weeks of the start of implementation.
Increased Sales Volume: With better insights into store operations, the Market-Basket Analysis led to identification of product affinities, encouraging store staff to enforce better cross-sell opportunities and increase the Average Basket Value.
Reduced Stock loss: Ensured demand-driven re-ordering and daily dynamic inter-store transfers to balance the demand-supply gap between stores.
Increased Margins: Availability of current sales volumes allowed more dynamic pricing options, increasing yields effectively. Effective tracking of vendor metrics like Fill Rates and Average Fulfillment Time laid benchmarks of procurement cycles, making the merchandising process efficient.
Minimized Dead Stock: Near Real-time visibility of "Forward Stock Cover" based on current ‘Avg Daily Safes".
Enhanced Customer Loyalty: Using Regency-Frequency-Monetary Value algorithms ensured better predictability of customer buying patterns. This ensured customers always complete their entire shopping list in every visit. Early detection of potential "non-returning" customers is followed by pro-active outreach campaigns to win back their loyalty.
For the stakeholder company, the solution has brought enhanced business efficiencies across operational departments of the organization through increased revenue and profitability, higher customer loyalty, and better supply chain and inventory optimization. Customers have demonstrated higher loyalty to the brand, because of better availability of products and higher fulfilment of their shopping lists in every visit. This is achieved through advanced customer analytics using techniques like Customer Lifecycle Analysis and application of Frequency-Frequency-Monetary Value based use cases.
For society, within the employee fraternity at Max, there is higher visibility of peer performances, leading to a healthy competitive spirit. This has served to be inspirational to the staff ta uplift individual contributions.
The challenges of the implementation
‘The key challenge was to understand business issues in order to decide where technology can assist and ensure technology was used to solve business problems rather than forced upon the business,’ says Sunil. ‘Also, it was important to avoid traditional silos with a project manager sitting to the side because the aim is to create a common vision across the silos in order to break them down and use people across different functions. ‘So we created cross-functional team with different skills,’ he adds.
Fallowing processes were adopted to make the change management process simple and successful.
- Setting up of Change Governance board
- Collaborative approach with the business stakeholders
- Communications and Engagement stating
- Change process roadmap with milestones and Time/Ines
- Current problems and why the need for a Bl solution
- User Training ( with multiple rounds of refresher trainings)
- Feedback and Fallow-ups
The future plans
‘Implementation of BIRetail is rightfully the start of a journey towards business optimization,’ says Nair. Engagement of the business users to directly use the solution is the true measure of the success of the solution, he adds. However, the organization needs to move from Analysis to Analytics to Predictive, and increase the business throughput achieved from such a solution. Each business unit is being encouraged to envisage how BIRetail can assist in improving decision making in everyday operational operations as well as in strategic functions.
‘Specific use cases are being built around every scenario identified and solutions are created to address such use cases,’ Sunil adds. The next initiative is to engage in statistical modeling to build predictive models for strategic planning in the operations of stores, and the organization on the whole, he concludes.
