What Can Help Retailers Avoid Out-of-Stocks? High-Quality Data
The pandemic was a shock for retailers. Panic-buying of disinfectants, hand sanitizers and countless other goods resulted in historic demand and out-of-stocks for many products, leading to lower customer satisfaction and brand loyalty, not to mention loss of sales. As upstream suppliers dealt with their own supply chain disruptions, retailers were left with empty shelves and angry customers. No matter what industry, the surprise arrival of COVID-19 was an expensive and unpredictable business challenge. However, where there's a challenge, there's always an opportunity and, if nothing else, the pandemic laid bare the need for retailers to better understand and react to customer demand to rise above the supply chain challenges.
The simplest way to prevent out-of-stocks is to know exactly when and where consumers will want your product. According to experts, this hasn’t been the focus for retailers and CPG companies. Dr. Kurt Jetta, who has more than 30 years in the CPG and grocery analytics industry, made the observation that a major failure of companies during the pandemic has been a lack of good demand planning. For retailers, getting started with demand planning can be difficult, but regardless of industry, impactful demand planning begins with high-quality data. The “secret sauce” of demand data varies by industry. Ideally, it should be data that measures the need for a product or service — think birth rates as an indicator of formula sales or illness levels in a region measuring the need for cough medicine. Regardless of what the data is, it should still hit the same three notes of timely, consistent and complete.
For demand planning, timely data means being up-to-date enough to make accurate predictions about what will occur in the near and midterm. Real-time data — or as close as you can get to real time — is the gold standard in many cases, but not always. Sometimes, real-time data sacrifices completeness for rapidity resulting in data getting “back filled” days or even weeks after reporting. If using timely data for their demand planning, retailers can shift stock quickly and efficiently in reaction to changing conditions.
Consistent data can be difficult to find, but is incredibly important for long-term strategy. While demand forecasting is an effective way to overcome the current supply shortages, it's one of the first steps toward creating a resilient supply network, which can help companies overcome supply chain woes in the future. Consistent data allows retailers to compare demand across different geographies and times, meaning they know for sure whether demand in one region is higher than another. Knowing the data is consistent reduces errors and allows retailers to move confidently.
Finally, completeness is another attribute of good data for use in demand planning. Complete data show the whole picture, or at least a representative portion of the whole picture. When complete data is used in demand planning, predictions will account for variables that may have been overlooked in the past, opening up sales opportunities with new demographics or in new locations.
With all this data, what comes next? Insights. Illness insights allow companies with illness-based demand to prevent out-of-stocks by predicting demand. For example, insights solutions business Kinsa uses data to create illness insights, which show when and where illness will rise and fall. These insights begin with quality data collected in real time from millions of households across the country through the use of app-enabled smart thermometers. Kinsa collects temperature, symptom and demographic data completely anonymously, creating illness insights from the aggregated data. These insights can then be implemented in many areas of business and planning, helping optimize a company’s supply network (e.g., shifting inventory based on customer needs before demand increases).
Data and insights can also be used to further build resilient supply networks by integrating them cross-functionally in sales, marketing and logistics to ensure retailers are providing what their customers need, exactly when and where they need it.
John Zicker is vice president of data science at Kinsa, a public health company on a mission to stop the spread of infectious illness.
John Zicker is VP of Data Science at Kinsa. John’s work on advanced data science spans four decades and has seen him applying his expertise to fields as diverse as cybersecurity, life sciences drug discovery, particle beam experiments, and business intelligence. John is a 5-time entrepreneur and 3-time CEO. John holds a BS in Electrical Engineering from the University of California at Davis as well as a Master’s of Biomedical Engineering from the University of Wisconsin-Madison. Some highlights of John’s previous work experience includes positions held as CEO at Sana Security, CrossLoop and Ampilion, CTO at Sagent Technology, engineer at Nasa, Lawrence Livermore National Laboratory and Stanford Linear Accelerator Center.
Kinsa is a public health company on a mission to stop the spread of infectious illness. By turning the most common medical device in the home - the thermometer - into an app-enabled communication system, Kinsa guides the ill to care and services to get better faster, and aggregates real-time population health insights at the first sign or symptom of illness, long before an individual enters into the healthcare system. Kinsa's unique and proprietary illness insights are used by public health departments to detect and contain new outbreaks, by retail, manufacturing and healthcare enterprises to forecast demand and optimize messaging, and by schools, families and individuals to predict, prepare for and prevent outbreaks.
