Industry
- Retail
Omnichannel Experience & Intelligent – The Future of Retail
Solution
- Consulting
TCS Consulting Services to Achieve Success in the Business 4.0 Era
Highlights
- Workforce planning and labor costs constitute the biggest line of expenses in selling, general, and administrative expenses (SG&A).
- Inaccurate sales forecasts, escalating labor costs, poor employee fit, and high attrition hamper labor budgeting and planning efforts.
- Retailers who adopt a combinatorial approach—top-down and bottom-up—for labor planning and budgeting will realize significant benefits.
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Getting wage planning right is critical
Accurate workforce planning and budgeting is extremely critical for a company’s financials.
In times of cost pressures such as inflation, selling, general and administrative (SG&A) expenses have the dubious reputation for being the first to come under the radar. With workforce planning and labor costs constituting the biggest profit and loss line of expenses in SG&A, there is plenty of room for optimization for even the best of retailers.
At the fundamental level, calculating headcount for carrying out core retail operations such as till handling, replenishing shelves, loading, and unloading trucks seems simple—maintain a minimum headcount and add a buffer based on workload drivers such as store traffic, weekend sales, and promotions. However, realities such as inaccurate sales forecasts, escalating labor costs, poor employee fit, and high attrition severely hamper labor budgeting and planning efforts. Let’s say that as a part of its annual operating plan (AOP), a big box retailer allocates $200 million towards workforce planning and budgeting. Even a 1% variance implies a loss of $2 million, making accurate workforce planning and budgeting extremely critical for the company’s financials.
Why current practices don’t work
The traditional approach that negates nuances such as store profile, intraday sales, and store attributes, drastically impacts labor planning and costs.
Typically, at most stores, the labor budget is largely based on sales and store manager’s judgment. This approach that does not factor in unique store profiles and associated labor hours leads to huge gaps not only in employee experience but impacts customer experience as well. For instance, customers expect apples to be crisp, yoghurt to be firm, and cilantro to be fresh. Stores with higher assortment of fresh produce may require more stringent operations such as handling, packaging, monitoring as against a store that stocks only general merchandise.
A few other common practices include following an approach that worked well in the previous year (for example, downsizing headcount or maintaining an average headcount), rolling out an annual budget based on weekly or monthly forecasts, and going by leadership mandates. For instance, each store’s labor budget may have a target of % of expenses/sales; the percentage varies according to the store type (wholesaler or retailer) or according to geography-specific regulations that affect labor allocation and costs. However, a hierarchical approach without engaging key functions such as finance, HR, and operations negates the nuances of workforce planning such as intraday sales and store attributes, drastically impacts labor planning and costs.
Data-driven workforce planning and budgeting
An optimal workforce and budget is profitable, aligned to local regulations, and creates an optimal shopping experience.
To build a workforce that supports this mission, retailers must be agile in their daily operations to make sure the right person with the right skills is in the right job at the right time. The impact of suboptimal workforce planning and budgeting run deep—operations are hit hard by the effects of headcount variances or reduction. The human resources function also directly feels its effect across the employee life cycle—from hiring, training, to retention. It is, in fact, one of the most impacted due to suboptimal workforce planning. Sometimes, HR hires more people than needed and invests a lot in training them. When these candidates are not properly utilized, they leave. Similarly, with under hiring, less people do more work and they, too, leave eventually.
While crafting their workforce management strategies, planners would do well to evaluate various scenarios that may have a direct bearing on sales or headcount. Let’s explore two common scenarios.
Scenario #1: Increase in hourly wages
Against the backdrop of a tight labor market, retailers may decide to increase hourly wages to meet industry average to both hire and retain labor. Assuming a business declares a 10% increase in hourly wages, the onus to balance wage increases may be passed on to category managers and stores teams. While category managers need to figure out what markdown or promotion decisions they need to take, stores teams need to come up with a sales plan on how many more items they need to sell, assuming the margin will remain the same.
Scenario #2: Projected sales increase
During the AOP, store forecasts a sales increase of +4% in US dollars, but the inflation rate is +6%. This implies that the volume of sales (items) will reduce 2%. Assuming there are no other changes in assortment and processes, what will be the impact on overall store headcount and worked hours? For a replenisher, the effort to restock a bottle is the same irrespective of the price, whether the sales price is $2 or $2.50; similarly, the effort of a cashier in scanning items remains the same.
These complexities can be addressed only with a data-driven approach. Retailers can take either a top-down or a bottom-up approach for optimizing wage planning and budgeting.
Your opinion matters
What approach do you follow while planning your workforce and budget?
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Top-down approach
The top-down approach works best when there are no big changes in the store’s processes.
Based on key drivers that affect store workload demand or client flow, such as forecasted volume of sales (items) and transactions, store sales area, assortment size, number of floors, and availability of pharmacy that drives traffic to the store, retailers can arrive at the optimal workforce.
This approach can be used to simulate the impact of the forecasted drivers on the head count.The regression model can also be used to arrive at the optimal head count based on the proposed budget (see Figure 1). While the top-down approach is typically finance-driven and can help identify potential opportunities for headcount optimization, worked hours variance and dispersion reduction, it works best only in scenarios where store productivity levels remain the same along the pre-determined period, with no changes in process and operational standards.
Figure 1: Multiple regression graph for determining the optimal workforce
Bottom up
The bottom-up approach is more activity-based and inclusive but requires higher levels of organizational readiness.
Typically, workload demand equals the product of time standard and workload drivers. If it takes eight minutes to retrieve a pallet at the backroom, and this happens 10 times every day, that means 80 minutes are spent daily in just moving pallets around the store.
By taking an activity-based approach (classifying activities as value-accretive and non-value-accretive and finding the right job profile), the bottom-up plan is more inclusive. It also considers the store profile and how technology at stores affects workload drivers and time standards.
Store profile: This is a challenging factor in the AOP considering that retail chain networks are composed of stores with varying profiles. The bottom-up approach helps extract major insights on headcount and worked hours based on the unique store characteristics such as:
- Assortment type, depth, and breadth: A higher assortment of fresh produce involves more operational complexity such as handling, packaging, and storing than managing general merchandise. Similarly, the complexity in managing planograms varies between a store having 5,000 items versus one with 40,000 items.
- Price change frequency: Stores with low pricing change (everyday low price) and stores with strong price variations (high-low) have varying impacts on store traffic and sales and require different headcount to manage both.
- Promotional activity: Stores with higher promotional activity may require more staffing to manage special booths and drive customer interactions.
- Curbside pickup: Stores offering conveniences such as curbside pickup and stores that serve as dark stores have varying workforce demand.
- Assortment type, depth, and breadth: A higher assortment of fresh produce involves more operational complexity such as handling, packaging, and storing than managing general merchandise. Similarly, the complexity in managing planograms varies between a store having 5,000 items versus one with 40,000 items.
In-store technologies: New-age technologies improve operational efficiencies and customer experience while reducing headcount.
- Self-checkout: As shoppers increasingly expect convenience and more control over their shopping journey, self-checkout reduces the demand for cash operators.
- Automated product returns: The elimination of manual forms significantly lowers the need for customer support.
- Route optimization: AI-powered route optimization at the backroom reduces the average time spent on item pickup.
- Assortment optimization: With an assortment that is leaner and yet offers choice to customers, the effort needed to manage the number of items on the shelves in a store is lower—routine store operations that associates carry out, such as checking for items’ expiry and updating price tags for SKUs with no electronic shelf labels, can be done faster.
- Self-checkout: As shoppers increasingly expect convenience and more control over their shopping journey, self-checkout reduces the demand for cash operators.
The bottom-up approach is more mature and operational-driven than the traditional top-down approach but requires higher levels of organizational readiness and a mindset shift to adopt new ways of working (people, processes, and technology). It is anchored by:
A clear operating model and process engineering (backed by a lean, agile way of working and customer experience mindset)
- Robust store operations supported by technologies such as IoT and robotics
- Well-documented processes, routines, standard operating procedures (SOPs), and training resources, with clearly identified workload drivers and time standards at the task level.
Good governance that supports all business stakeholders, especially across operations, HR, and finance.
What works best
A combinatorial approach that integrates the best of both top-down and bottom-up practices is recommended to realize significant benefits.
Store associates are the champions responsible for smooth operations and a crucial determinant of the customer's shopping experience. In times of inflation, where every dollar counts, even a small improvement in wage planning and budgeting makes an enormous difference.
The top-down and bottom-up approach have their inherent benefits; however, the bottom-up approach helps find and eliminate key bottlenecks to optimize store personnel costs and store workload demand. It infuses efficiencies in processes related to assortment, replenishment, pricing changes, store layout, shrink management, inventory management, workforce scheduling, and till optimization.
In fact, a combinatorial approach that integrates the best of both is recommended to realize significant benefits—meeting the top-down (target) plan while fostering greater collaboration among various stakeholders and pushing retailers to the next level in data management maturity, to arrive at a mutually agreed upon final plan.
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