Retail expert Charlie Chase recently participated in a private unplugged discussion about disruption challenges with retail executives hosted by the Retail Council of Canada. All quotes have been edited for maximum clarity, and participants have been de-identified.
The last two years have created a major disruption for retailers and consumer goods companies, which has intensified competition, caused consumers to shift their buying patterns, buckled supply chains and significantly increased omnichannel purchases.
During times of uncertainty, organizations must use their data to protect and increase revenue, reduce costs and improve the accuracy of forecasting and demand planning. As a result, companies are embracing analytics and new data streams as an opportunity to understand consumers and demand as never before.
However, mastering your analytics, which is an enterprise effort, requires a culture shift, enhanced staff skills, processes, data, analytics and technology. During this discussion, Charlie Chase delved deeper into these topics to determine how retailers and consumer products companies are adapting to the shifting consumer demand patterns, because of the new digital economy and unforeseen disruptions.
The discussion surfaced a series of questions regarding how the power of advanced analytics can solve retailers and consumer goods challenges regarding supply-chain resilience. Below you'll find excerpts from Charlie's discussion.
An AVP of an international retailer asks:
Question: Given the last two years, do you feel that you’re seeing a change in an organization's willingness to adopt a change in culture to become a more analytics-driven organization?
Charlie: Absolutely! We’re seeing many retailers as well as their consumer goods suppliers starting to put together digital transformation strategies looking for new technologies, processes and analytics skills. Nike for example hired over close to fifty data scientists over the past five years and then spread them across the organization. Hanesbrands has a chief analytics officer (CAO) who reports to the CEO with several data scientists, demand forecasting and planning, and inventory planning reporting to the CAO.
A director of logistics asks:
Question: Nike and Kellogg’s are massive companies. Those are multi-billion-dollar enterprises, but how about the $50 million businesses? What is that first step when it comes to demand planning? We don't have 50 data scientists like Nike. So, what would be those steps for somebody like a lot of retailers out there?
Charlie: I'm currently working with a family-owned nutrition company in NYC. They sell nutritional raw ingredients, as well as products they make with an annual turnover of less than $100 million UDS. They hired one data scientist and have a supply chain person and one marketing person. They are forecasting product demand by selling direct to consumers through their website, selling to brick mortar stores, selling their products on Amazon and ingredients to consumer goods companies. One of the main ingredients in a lot of their products comes directly from Japan with a six-month lead time. They started out by collecting data internally, as well as externally.
Question No. 2: We have SAP as our ERP platform. It’s where our data is linked, we have a full data warehouse and omnichannel platforms which all feed into the data warehouse. Next, we have demand planning and data analysts. Humans can only process so much. So, what are those automation or automated bots on technology platforms that can really deliver good insights and ROI?”
Charlie: Many software vendors have access engines to access the data warehouse. One of the challenges is that ERP systems were designed before the digital economy, and as a result are not able to operate effectively in the new digital economy or handle unforeseen disruptions. But are you collecting all the other data? As I mentioned, point of sale data, depletions from warehouses and inventory data. If you have any kind of in-store information around promotions, all that data can be captured in your data warehouse, or your central data repository wherever it resides – data lake or cloud. Now there are SAS technologies that can fill the gaps and complement SAP with more advanced analytics.
Knowing what the customer wants is only part of the answer. Businesses need to know what state their supply chain is in so they can actually meet customer demand.
A CEO of a national retail chain asks:
Question: You spoke a lot about demand-based forecasting and planning. I wanted to understand more about the supply side of forecasting because that is equally unpredictable. Even if you know exactly what the customer will do, you don't know what the supplier of the supply chain will do. How do you quantify that? How do you quantify the uncertainty around supply routes and the availability of products?
Charlie: There are technology companies that can deploy in combination with demand forecasting and planning -inventory optimization and replenishment allocation. A lot of companies had to go to an allocation format because of supply chain challenges due to choking points in the supply chain. By the way, we don't have any problems or issues, right? We only have challenges and opportunities. The opportunity is how can we optimize available inventory in a way that is fair and equitable to all my customers until I can get more inventory? That's one way many companies have chosen to do it by default.
Another opportunity that we did with a direct-to-consumer grocery retailer was to provide segmentation and substitution analysis. We looked at what products can be supplemented in place of other products they had in inventory. For example, if a certain pasta sauce was out of stock because of supply chain disruptions they could recommend a similar product (flavor, style, brand) to their customers. On their website, customers were directed to an alternative product – pasta, sauce, as well as complementary alternatives. We did a lot of that type of analysis for this retailer enabling them to counteract a lot of the disruptions by moving customers to complementary products that were available.
They also implemented a cloud-native forecasting and planning solution using predictive analytics and machine learning and collected data reflective of the disruption – Google trends, epidemiological data and local economic data – thus improving their weekly SKU forecasts by 12.6% (over 90% accuracy on average across their product portfolio).
If you collect the right data, deploy the right technology and conduct the right analysis, businesses can begin to build resilience in the supply chain and can quickly adapt to shifting consumer demand.
Here’s how you can unleash the power of SAS advanced analytics at your company.