Things are looking either very good or rather grim for the holiday shopping season.
On one hand the National Retail Federation anticipates “that retail sales will now grow between 10.5 percent and 13.5 percent to more than $4.44 trillion this year as the economy accelerates its pace of recovery.”
On the other Salesforce predicts that “U.S. retailers will face an extra $223 billion in costs of goods sold this holiday season, which include year-over-year jumps in the costs of freight, manufacturing, and labor.”
This leaves retailers and manufacturers with some tough decisions. We are already in the peak back to school and holiday shipping season – meaning costs to obtain finished goods and raw materials are going up.
Even if you addressed forecasting visibility problems faced last year, is your organization prepared to make time sensitive procurement decisions that will determine the fate of your company’s financial performance?
Pay or Wait
In early 2020, panic and unpredictable consumer behavior exposed weaknesses in supply chains unable to foresee the changes and course correct. This year demand clarity is more certain, but many supply chains are struggling to navigate ill-prepared shipping and logistics networks:
“At key points in the supply chain there are raw material shortages, labor shortages, shipping container shortages, freight space shortages and facility closures. Across the board retailers are wrestling with higher freight costs and constrained shipping capacity.”
The situation has buyers of finished goods, components and raw materials choosing between the lesser of two evils just in the hopes of meeting forecasts:
- Either pay expedited fees for freight to avoid missing the peak shopping season, or
- Wait, revise forecasts down, and pay less later.
Neither choice is desirable, and it’s likely that unforeseen shortages and disruptions impact either scenario.
In April of this year the author of Deloitte’s 2021 Global Procurement Officer Survey reported that CPOs “need to be very much focused on building agility within their organizations. All of the trends we’ve been talking about require some level of agility, adaptability and resilience in order to effectively respond and be prepared because there’s just so much change and disruption coming up at you. You need to be able to pivot pretty quickly.”
Given the challenges CPOs cited over the past 12 months (below), it’s obvious many were caught off guard at the most inopportune times, costing their companies expedited shipping fees, lost revenue, and brand damage.
Since then, some CPOs have mapped supplier networks to identify the sources, locations, materials, and components representing the most and least risk, in the hopes of avoiding a repeat. Such visibility could offer first mover advantage to get first in line for limited inventories or available shipping and freight capacity should it be necessary.
Yet even with this information at hand, without the ability to activate it across the product portfolio at scale, it’s of little use. This is how Hyperautomation can help.
Robots and Analytics Better Together
Blending concepts from Robotic Process Automation and Artificial Intelligence, Hyperautomation is defined by Gartner as “the approach that organizations use to rapidly identify, vet, and automate business processes that originally required some form of human judgement or action. Hyperautomation involves a combination of technologies that include robotic process automation (RPA), machine learning (ML), artificial intelligence (AI) and many others.”
Consider this RPA example from UiPath:
“A retailer that wants to source a specific product can use RPA to interrogate a database of suppliers, identifying companies that sell the product within a defined list of variables, cost being the most obvious. The RPA robots can then generate a request for quotation and review multiple quotations when they come back, based on costs, fulfilment times etc.”
This creates value by speeding profitable buying decisions that avoid out of stock conditions versus manual methods. Yet if you were to layer in advanced analytics (like AI) at key points in the process, McKinsey found in 2017 that costs could be reduced by up to 8 percent by negotiating more favorable terms. It isn’t quite that simple in practice, however.
In that same research, McKinsey noted:
“Procurement functions generate more data than any one employee can track and manage. At one midsize manufacturing company with approximately $2 billion in annual revenue, for example, procurement had data on more than 20,000 transactions for a single category, each with four to five statistically significant drivers of price.”
Hyperautomation is tailor made for handling complex scenarios like this, where the capabilities of RPA and AI complement one another to deliver outcomes neither can without the other.
Alteryx paired with partner UiPath, for example, to offer “end-to-end process automation spanning robot-driven, analytic and data-driven processes. RPA extends the value of APA (Analytic Process Automation) by automating high-volume tasks, bringing in new and complex data sources for analytic processing within Alteryx and using robots to automate manual output tasks to downstream operational systems.”
Keep in mind that McKinsey’s Pre-Pandemic 2017 example did not reflect a real time, high stress scenario requiring rapid responses to market conditions across potentially thousands of categories, each of which can make the difference between receiving product on time and paying expedited fees or facing further delays.
This is a point raised by Alteryx RPA partner Blue Prism, whose integration “enables organizations to create greater insight from their customer data, thus, enabling manufacturers to more quickly adapt to evolving customer preferences, as well as providing the more personalized experience that customers now demand.”
Improving both response time to seize narrow windows of opportunity and the value of decisions made in those moments requires looking beyond internal and supplier data sources.
External data such as economic, political, financial, and environmental are often factors in supplier costs, raw material and finished goods availability, and shipping capacity. All must be considered in analytical models to offer as much an early warning system as possible to procurement teams and their leadership.
Blue Prism emphasizes this as well, creating “an end-to-end, analytics, data science, and business process automation solution that enables organizations to create and derive value from all data – whether structured or unstructured, no matter where it resides.”
The Snowflake Data marketplace offers a variety of external data providers, and this Starter Kit from Alteryx makes it easy to explore the possibilities with a no/low code environment that integrates with leading RPA solutions like UiPath and Blue Prism. This allows analysts, data scientists and process automation developers and engineers to collaborate on use cases like those described in this post.
With CEOs prioritizing growth and investments in AI to achieve their goals, there is no better time to leverage Hyperautomation to reduce supply chain risk while improving the odds of meeting, if not beating, the forecast.
Alteryx RPA Partner Integrations