Retail Transformation From Data to Systems Thinking

Retail Transformation: Shifting From Data Analysis to Systems Thinking

Julie Bushee, Retail Labor Manager

In the ideal retail situation, labor analysts can focus their attention on data interpretation, allowing their information technology to do heavy data mining. Alas, this is rare. Many labor teams require analysts to gather and shape data prior to analysis. In the worst-case scenarios, retailers have understaffed or non-existent labor teams to complete these tasks. I empathize with retailers in this struggle. When analysts are required to gather and shape data during workforce management (WFM), they are less effective than their competitors for several reasons.

Data analysis detriments

First, this combination leaves room for human error in a complex array of workflows, from work measurement to scheduling effectiveness. Second, the necessary data massaging that occurs prior to interpretation takes considerable time, providing analysts with less energy and capacity to focus on fine-tuning the details that matter in getting schedules to stores. Third, the extra time to send important information to store managers and employees leaves associates at a disadvantage as they rush to implement labor plans, ultimately dissatisfying customers and benefiting competitors.

This scenario certainly varies from our ideal. However, there can be a bright and hopeful future on the horizon if retailers consider a shift from data to systems thinking. Systems thinking involves the utilization of tools offering wall-to-wall labor management and implementation capabilities. For analysts who use systems thinking and analysis, data is at their fingertips to be arranged, rearranged and configured for actionable review. Analysts can quickly see issues and call them to leadership’s attention.

Systems thinking overview

The shift to systems analysis requires a technology investment, but it includes a significant return. Research shows that putting the right people in the right place doing the right things at the right time causes a ripple effect of organizational productivity and profits. However, few WFM solutions do this well. Retail customers expect an excellent experience that includes stores with a lovely atmosphere, stocked shelves and on-cue service. Organizations that can accomplish this will lead the industry. Investing in technology that offers retail leaders time to focus on the important things is imperative.

This can be done by ensuring WFM tools are calibrated in a way to catch outliers so that analysts can understand them and make decisions based on that information. The best WFM systems pull the data together, while the labor team summarizes information to help managers lead the business. Many best-in-class WFM tools are intuitive and self-learning, leading to more accurate plans, forecasts and schedules every week. This allows small labor teams to transition their focus from wrestling with data to more value-added pursuits, like fine-tuning the system, training store users or instilling best practices into standard operating procedures.

The optimal labor management journey

Let’s take a walk through the labor management journey to see how a great WFM system will aid labor analysts along each step of the way.

Work standards and forecasting

A wall-to-wall WFM system starts by providing actionable checks and information updates to labor analysts. Beginning with work standards, operating procedures and labor goals, the system should include effective-dating and data flow checks. Afterwards, the system should forecast at every business level and allow the retailer to differentiate between versions. A great forecast is built from the lowest tier and checked upward for accuracy. Here, the system can also monitor data set points and proactively notify analysts around potential issues.

I have heard retail gurus talk about forecasting as the cornerstone to effective, wall-to-wall store planning; thus, systems thinking in this space is monumental for labor teams. Some WFM systems let users make edits to system-generated forecasts, but few use automated technology to feed those edits back into the system, allowing it to learn from its mistakes and get smarter. It is important that labor teams have visibility into the accuracy of each forecast. The more instinctive the system, the fewer edits the labor team makes over time.

To summarize, a world-class WFM system will include the following functionality for work standards and forecasting:

  1. Standards and labor model transparency
  2. Forecast version control
  3. Quantifiable forecast accuracy
  4. Anomaly identification
  5. Automated system learning

Staffing

With work requirements and a great forecast, the system workflow moves onto creating staffing demand. A very complex but necessary step is to ensure labor requirements and service standards match. This is instrumental in labor management; does your WFM system create staffing demand? The system must compare staffing requirements against work requirements to ensure no gaps exist. If a gap is present, a great WFM system will provide actionable details around necessary changes. This ensures things like minimum coverage and safety valves (as a part of queue management) are in place to meet service expectations.

Scheduling

As a final step, WFM systems use staffing demand to create optimized schedules by building shifts covering work requirements and employee constraints. The best schedules utilize task-based workload planning. A labor team wastes time writing schedules that a WFM system can provide automatically. The system covers all work requirements, staffing necessities and scheduling rules and directives. If a retailer’s WFM system is self-learning, schedules become more and more accurate over time. For a small labor team, this sounds dreamy!

Wrapping up

In conclusion, where gathering and shaping data was necessary in days past, machine learning WFM systems now provide the mining work for labor analysts. I have seen this work first-hand and believe in its effectiveness. Retail is a small-margin business, making any competitive advantage important. Thus, moving from a data to systems analytical perspective with the right tools could lead a retail business into greater profits.