Unified Commerce Part Two

Unified Commerce, What Have You Done? Part Two

Brian Monaco
Vice President of Retail Services

In the first part of this blog series, our discussion centered around unified commerce’s impact, Industry 4.0’s onset, traditional retailer adaptations and long-term solutions. Regarding long-term solutions, we referenced four potential opportunities (i.e., data mining, forecasting, scheduling and mobile solutions) for brick-and-mortar institutions to optimize their workforce without upending customer service expectations. Let us focus on two of those four opportunities in today’s blog: forecasting and scheduling. Forecasting accuracy is paramount when it comes to the effectiveness of scheduling and mobile solutions. Thus, getting it right must be a priority for brick-and-mortar retailers who desire relevancy in an online age.

Forecasting is so important for retailers that those who fall behind in predictive technology or data processing “are already legacy companies,” according to management author Ram Charan1. In recent business examination, companies who forecast well found better schedules, cost reductions, employee satisfaction, product availability, reduced waste and automated processing2, 3.

Let us begin with the basics of forecasting. First, what is it? Forecasting is, “predicting the future as accurately as possible, given all information available, including historical data and knowledge of future events that might impact the prediction”4. Many retailers have a concept of this; where they trail competitors, however, is in how they forecast.

Retailers who use fixed, rule-based algorithms with variables like sales, inventory, pricing and items alone to predict future purchasing are making decisions based on insufficient data3, 5. For forecasting technology to benefit retailers, it must give leaders insight into each step of the desired management process, such as workforce optimization or inventory control. Organizations utilizing state-of-the-art forecasting technology include variables like social media, weather patterns, shipping requirements, competitor pricing and even celebrity trends3. Let us move on to a second important point. When forecasts are accurate, retail leaders understand operational needs. On the labor side, this directly leads to optimized schedules.

Scheduling is a tricky concept in the retail industry. Antiquated methods included store managers reviewing employee availability and manually creating weekly schedules. Human error prevailed whenever data was overlooked. Today, we have the benefit of using machine learning, a form of artificial intelligence revolutionizing how we do business. In a nutshell, machine learning is a way for forecasting and scheduling engines to become smarter with every piece of data input into their system over time. Machine learning recently showcased incredible savings for retailers through out-of-stock reductions, shrink decreases and gross-margin increases5. When paired with task-based staffing and scheduling, these processes become innovatively effective.

Task-based staffing may be a new term for some. Retailers utilizing legacy methods schedule at the job level; in other words, this is appropriating labor based on an employee’s role (e.g., cashier) versus a store’s needs. Task-based scheduling is a more granular tactic, allowing stores to schedule cross-trained associates at shorter intervals to complete task clusters. Let us look at an example. John Doe is a cross-trained cashier at Store One. Traditionally, a job-based scheduling system might staff John for four hours as a cashier and four as a produce clerk. Alternatively, in task-based scheduling, John is staffed for two hours to open the store (a cluster of task activities), two to restock sale items in grocery, two for cashiering in the front end over lunch, and two to cull products and record shrink in produce. One can easily see the latter method’s adaptability to accommodate customer and store demand. Task-based scheduling is also far more directive for John so that his time is better utilized as needed throughout the departments he is qualified to work in.

So far in this blog, we reviewed several important topics: forecasting accuracy, machine learning, scheduling optimization and task-based scheduling. But…how do these concepts tie into unified commerce? Great question.

As far as we can tell, retailers have four major options moving forward to remain competitive in the unfolding of Industry 4.0:

  • Invest upfront in new technological strategies using the Internet of Things (IoT), artificial intelligence or mergers/acquisitions to adapt to e-commerce.
  • Invest in continuous improvement and expense control to generate capital to later invest in unified commerce initiatives. Forecasting, scheduling, mobile initiatives and process improvements may help significantly in this area.
  • Some combination of the prior two options.
  • Be realistic about what online options will take away from business in the future and invest in meaningful, game-changing differentiators to more than offset the business risk.

As we mentioned in Part One of this blog, unified commerce is sticking around. Retailers must adapt in order to remain relevant. Whether Amazon is a direct competitor or not, brick-and-mortar companies are following suit in their own way to capitalize on new online markets and digital technology. Welcome to the ever-changing world of retailing. We invite you to join a new wave of retailers on that journey.

References

  1. Charan, R. (2015). The attacker’s advantage: Turning uncertainty into breakthrough opportunities. New York, NY: Public Affairs.
  2. Laubl, D., Schlogl, G., & Silen, P. (2015). Smarter schedules, better budgets: How to improve store operations. Zurich, Switzerland: McKinsey & Company.
  3. Sillitoe, B. (2017, June 15). Retailers urged to change approach to demand forecasting. com. Retrieved from https://www.computerweekly.com/feature/Retailers-urged-to-change-approach-to-demand-forecasting
  4. Hyndman, R., & Athanasopolous, G. (2018). Forecasting: Principles and practices (2nd ed.). Melbourne, Australia: OTexts.
  5. Glatzel, C., Hopkins, M., Lange, T., and Weiss, U. (2016, November). The secret to smarter fresh-food replenishment? Machine learning. McKinsey & Company: Retail. Retrieved from https://www.mckinsey.com/industries/retail/our-insights/the-secret-to-smarter-fresh-food-replenishment-machine-learning