June 1, 2023

Applying analytics to improve people planning in a retail chain

The company lacked the analytical edge to take people planning to the next level.

Applying analytics to improve people planning in a retail chain

At a glance

Challenge

Retailers, especially those reliant on store staff, need to adapt to emerging trends that highlight the dual role of sales and operational fulfillment. Our client, a large fast-food chain, faced challenges in advancing their people planning due to limited analytical insights. Key challenges included identifying productivity drivers, optimizing operating modes, anticipating workload over time, and generating efficient plans to address expected workloads.

Solution

Performance analytics are essential for people and capacity planning. Statistical analysis of transactional data and client surveys revealed that experience impacts productivity, while age influences customer satisfaction, highlighting the need for effective retention policies. Forecasting techniques were used to identify seasonality and other demand patterns, translating future demand into workload projections. An optimization-based approach was then developed to create efficient work plans that align with legal requirements and employee needs.

Results

The proposed approach generated a 10% reduction in total working hours, primarily benefiting larger stores by capturing economies of scale through uniform standards. These savings were achieved without compromising service standards or employee satisfaction. The model was implemented in an intuitive decision-support tool, enabling iterative plan refinement. Additionally, close support was provided to store managers during the initial phase to ensure full adoption of the tool.

Challenge

Retailers, especially those reliant on store staff, need to adapt to emerging trends that highlight the dual role of sales and operational fulfillment. Our client, a large fast-food chain, faced challenges in advancing their people planning due to limited analytical insights. Key challenges included identifying productivity drivers, optimizing operating modes, anticipating workload over time, and generating efficient plans to address expected workloads.

Approach

Solution

Performance analytics are essential for people and capacity planning. Statistical analysis of transactional data and client surveys revealed that experience impacts productivity, while age influences customer satisfaction, highlighting the need for effective retention policies. Forecasting techniques were used to identify seasonality and other demand patterns, translating future demand into workload projections. An optimization-based approach was then developed to create efficient work plans that align with legal requirements and employee needs.

Results

The proposed approach generated a 10% reduction in total working hours, primarily benefiting larger stores by capturing economies of scale through uniform standards. These savings were achieved without compromising service standards or employee satisfaction. The model was implemented in an intuitive decision-support tool, enabling iterative plan refinement. Additionally, close support was provided to store managers during the initial phase to ensure full adoption of the tool.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

Retailers should be particularly aware of this emerging trend, notably regarding store staff, as it plays a crucial double role: sales and operational fulfillment.

Our client, a large fast-food chain, had long devoted considerable effort to managing its store-based staff. Still, the company lacked the analytical edge to take people planning to the next level.

Four key challenges needed to be addressed:

  • What are the main productivity drivers and how to act on them?
  • What is the optimal operating mode?
  • How to anticipate workload along time?
  • How to generate efficient plans that tackle the expected workload?

Performance analytics are at the core of people and capacity planning. A statistical analysis of both transactional data and client surveys highlighted two main trends about employees: experience drove productivity, while age drove customer satisfaction. Either way, effective retention policies emerged as a strategic priority.

Forecasting techniques were then applied to understand seasonality trends and other patterns in order to grasp future demand. Insights about productivity helped to translate such demand into workload projections.

Our AI-generated summary

Our AI-generated summary

The work plans obtained according to the proposed approach yielded a reduction of around 10% in total working hours. Efficiency gains arose mostly in larger stores, as economies of scale were captured by promoting uniform standards between stores.

These significant savings were reached without breaching service standards (e.g., minimum staff requirements) or harming employee satisfaction.

The model was materialized in an intuitive decision-support tool, allowing for iterative plan refinement. And as the planning process is as important as the intelligence behind it, close usage support was provided to store managers in an initial phase, ensuring the full adoption of the tool.

Subsequently, an optimization-based approach was developed to generate efficient work plans that met both legal restrictions and employee needs.

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