There was a time not long ago that SAS (‘Statistical Analysis System’) was the domain of only those companies that could afford to hire the specialists that knew how to get the most from the platform’s capabilities. But for the last decade at least, SAS has been making its advanced data analytics available to businesspeople who don’t have a PhD in data science or even know anybody who does. Without a great deal of fanfare, SAS has been working to democratize advanced analytics in ways that make it possible for business decision makers to optimize operational processes with insights derived from advanced analytics.

At last week’s SAS Global Forum 2021, the company highlighted just how far it’s gone to make what company CTO Bryan Harris framed as the real “new normal” – data driven decision making – available to more companies. At the e-conference, Harris spoke about the explosion of data in today’s environment, with its complexity, diversity, and sheer size. SAS has been unwavering in its belief that those companies that embrace the strategic value of data and the insights that can be derived will be the winners over the long haul. To help companies handle the explosion of data available to them, Harris underlined the company’s objective to handle “any data, anywhere, at any scale” and to “be able deploy models faster”.

The company’s belief that (in Harris’ words) “analytics is at the heart of human progress” permeated the sessions offered at the conference. Bringing that lofty ambition down to something that a business decision maker can relate to, Harris pointed out that “analytics is foundational to optimization”. For retailers and their partners, the challenge is to use demand sensing models that help companies to optimize the distribution of products to where consumers want them. In essence, SAS wants to help retailers create a real-time link between forecasted customer demand and what is actually happening in the real marketplace, so that supply can be realigned in the most optimal way to meet real demand.

The Waitrose Case Study

That’s the theory, and it has been put into practice at one of the world’s most storied grocery brands, UK’s Waitrose. Trading Grocery DevOps Manager John Mayes shared how the London-based retailer makes analytics-driven supply chain decisions. Mayes pointed out that Waitrose supports a large number of SKU’s, with the retailer’s 350 shops featuring a blend of local products – often trading with suppliers that are within 30 miles of the stores that they serve. Because of this strategy, there are many single points of failure with no substitution strategies. At the same time, COVID has triggered changes in demand and how customers shop. Waitrose expects that some (but not all) of those changes will be permanent and is considering how to roll those changes into their long-term strategies. Like retailers everywhere, Waitrose has had to employ new analytics to understand changing consumer demand and to respond in a way that not only captures sales, but will also improve profits.

Ironically, in October 2020 Waitrose ended its involvement in Ocado, a commercial D2C service, right as the spread of COVID was accelerating direct-to-consumer (D2C) order volumes. Waitrose had been one of the first grocers to get into direct delivery over 20 years ago via its involvement in the development of Ocado, but by the end of 2020, the UK grocer had its own branded delivery service.

To respond to heavy demand, Waitrose has deployed a D2C fulfillment process that employs “dark store” fulfillment in the London metro area, and pick-from-store D2C in other parts of its UK market. Mayes pointed out that because of the pandemic lockdown, Waitrose “exceeded volume estimates at an unimaginable rate” and has had to expand capacity rapidly in order to meet massive demand for the service.

From this experience, the company developed several new requirements, including:

• To better understand the relationships between operational KPI’s (for example, “if the goal is higher on-shelf availability, what is the impact on wastage?”)
• To use data and insights from analytics to develop “trusted models” that identify behavioral patterns and inform operational processes based on those patterns, and to closely monitor outcomes
• To analyze “unexpected data correlations,” and,
• To optimize stock control by better balancing demand and fulfillment.

According to Mayes, the objective of using SAS analytics is to “stay one step ahead” even as the velocity of change is increasing. The grocer wants to institutionalize “a forward-leaning way of looking forward”.

Lessons For Retailers & Their Trading Partners

In another session, a panel of experts from SAS and consulting firm McKinsey discussed how “the new digital normal” creates opportunities to improve demand management in retail and consumer products companies. The panelists highlighted all the challenges facing trading partners that can benefit from advanced analytics, including:

• Pressure to improve short-term profitability
• To develop more predictable forecasts, and,
• To deliver “one version of the truth” to all the line-of-business organizations within the company (e.g., marketing, merchandising, supply chain)

According to the panel, the volume of change that challenges the supply chain has “demand planners stuck in the middle”. To get past these challenges, the group recommended using new analytics to develop more predictable forecasting capabilities that allow for volatility on the sales side by understanding the causal factors that shape demand, and to better predict the effect of different promotions and price policies and use artificial (AI) models to consider new variables.

Finally, the panel pointed out that retailers and their partners need to determine how many of the COVID-inspired changes will remain, and which will revert to pre-pandemic days. One thing that won’t change, according to the panel, is that demand forecasting is “no longer a project – now it’s part of the process.”

The Microsoft Partnership

SAS announced its strategic partnership with Microsoft in June 2020, and it was highlighted at the Global Forum. At the heart of that partnership is a strategy to make SAS analytics available to a far greater audience, and to dramatically improve time-to-value for corporate customers. SAS has fully embraced the notion of “democratized” AI and analytics, first of all with attractive and easy-to-use user interfaces, and secondly to enable companies to interchangeably use open-source AI models and SAS-developed capabilities in a Microsoft cloud-based environment.

SAS capabilities have been integrated into Microsoft’s popular Dynamics 360 platform, including new supply chain use cases for analytics. SAS and Microsoft hope that with SAS’s analytics expertise and Microsoft’s cloud capabilities, a whole new audience will be able to take advantage of advanced analytics to optimize their businesses.

The White Coats Aren’t Going Away

None of SAS’s efforts to bring advanced analytics to “everyman” signals the demise of the mythical “white-coated” data scientist. In fact, a casual walk through job postings on LinkedIn seems to indicate that “data scientists” and “SAS programmers” might be more in demand than ever before. So, all the money spent by young professionals on getting their college degrees isn’t wasted. As SAS CTO Bryan Harris said at the conference, the explosion of new data continues unabated, and with that comes new analytics, new insights, and new applications for those insights. There are plenty of new data relationships to be explored and new models to be developed. Eventually, those too will be “democratized” and made available to companies that need them.

SAS has been consistent and unwavering in its objective to make organizational decision making smarter and faster. Retailers and their trading partners are bound to be the immediate beneficiaries. Ultimately, however, we consumers are the real winners.