Manufacturing & Logistics IT spoke with leading industry spokespeople from the analyst and vendor community about how modern Demand Forecasting & Planning/Sales & Operations Planning (S&OP) solutions and other planning-related technology can help to provide a more efficient supply chain, manufacturing, & distribution regime.

Karen Sage, chief marketing officer, Syncron, believes AI and machine learning (ML) should be crucial tenants to a company’s Demand Forecasting/Planning/S&OP software. Together, AI & ML are intelligent workhorses capable of going beyond what any excel spreadsheet, homegrown solution or even direct human involvement could perform on its own,” she says. “Renowned AI researcher and former Ivy League professor, Dr. Darrell West gracefully describes AI as a wide-ranging tool that enables people to rethink how we integrate information, analyze data and use the resulting insights to improve decision making.

Indeed, AI is intentional and adaptive – foundational attributes of any effective decision-making entity, and particularly for tasks like inventory management which involves a lot of complex data, both historical and real-time, and need to leverage that data whilst capturing and optimizing multidimensional objectives such as balancing costs, service levels and risk. The benefits of AI layered with the strength of ML, which analyzes data for underlying trends and anomalies, give manufacturers a reliable foundation to make the best decisions for their business and their customers depending on their situation. The bottom line is, AI and ML brings intelligent, automated decision making to reality and scale.”

Additional promise of reward

In terms of drivers for change, Sage considers that AI and ML have begun to play major roles in everything we do to simplify and automate our lives when it comes to being able to operate and successfully manage a supply chain. “Because these technologies have only begun to be deployed on a large scale recently but have already shown immediate and substantive advantages even in their most rudimentary of forms, AI/ML models to be refined, advanced and matured,” she says. “Without a doubt, AI and ML will remain essential tools and will grow in use and sophistication, more efficient and better at solving inventory optimization problems at scale.”

However, adds Sage, one thing the pandemic has taught us is that AI models work best when faced with normal operating events and common directives such as to drive cost efficiencies under constraints of goals defined by metrics used to ensure customer expectations are met consistently and predictably. “Under uncertainty and different, varying objectives, simple AI/ML models can fail to meet expectations and can become unpredictable in their recommended consequences,” she says. “For example, in the event there is a tremendous storm forecasted, there is likely a run on basic staple goods in the local grocery store. The AI/ML algorithms not having a line of sight into things like weather or a broader view into surrounding areas and inability to transport goods over the road, might recommend reactive responses that might not only result in even worse inventory stockouts but extra fulfillment costs.

During the storm mentioned above, a run on basic staple goods might deplete inventory to a point where new inventory ordered is triggered. Even worse, because of the state of the inventory and estimated delivery transit times, the models might recommend expediated shipments, the raising of allowable spend for goods and unintentionally creating a worse situation and sub optimization of spend. Fulfillment forecasting is particularly challenging in predicting demand – even more than supply. That said, anomalies come from both supply and demand data, since there will be unexpected fluctuations in each.”

Sage also makes the point that the recent fluctuations of the supply chain have put a spotlight on how we are all impacted by our shared global economy. “Container shortages, extended lead times and cash-strapped suppliers have pushed manufacturers to re-examine their legacy systems and make critical determinations on if they have the right tools to meet the demands of the future,” she says. “Today, it is integral to busines success to move beyond relying only on historical data from past sales or stock outs to instead introduce complexity to demand forecasting using technology developed specifically for this purpose.

To succeed in the aftermarket especially, you must master the delicate balance of managing sporadic, no-traditional supply chain demand, while meeting the expectation of your customers.” Sage adds that implementing a software solution built specifically for optimizing and streamlining your inventory ecosystem offers residual benefits to the manufacturer as a byproduct is more reliable and comprehensive data, which can then be analyzed to further enhance business operations. “Syncron has the unique distinction of focusing only on the aftermarket space, allowing our customers to enjoy the extra benefit of leveraging insights from peer companies across varying industries,” she says.


Note: This article was originally published in Manufacturing & Logistics IT Magazine, here