Igor Rikalo is President and COO at o9 Solutions.
Over the past year, technologies that utilize artificial intelligence (AI)—especially generative AI—have become a dominant topic of conversation across the business world. Many employees may wonder how this impacts their current roles as more business leaders consider how to digitally transform their business operations by incorporating AI technology and platforms into their processes.
Broadly speaking, AI technology is unlikely to replace most professions outright. Instead, it will likely shift affected roles to focus on strategic and value-driven responsibilities while AI handles rote daily tasks.
In the next five to ten years, I expect that there will be a dramatic transformation in how businesses across many industries handle planning and decision-making. Here are some ways that current AI technology and platforms are shaping this business function.
Short-Term Tactical Planning
If we look at today’s planning processes, the daily decision-making cycles would be considered short-term planning. Already, we’re seeing more automation driving the tactical daily planning aspects of the enterprise. AI platforms are equipped to collect volumes of data and “connect the dots” to understand and oversee the myriad of demand drivers that could fluctuate in the near term and automate the demand forecasting process. However, planners will also have more significant insights into the supply side as well as bigger-picture business objectives, so they’ll have a greater ability to strategically balance demand and supply and prescribe automated actions based on broader guiding principles.
I’ve observed how automated technologies are assisting demand planning capabilities across the food and beverage, apparel and retail sectors. Through automation, teams can operate in a “touchless” planning mode where most of the tactical planning is handled by algorithms, allowing planners to focus on the exceptions. By tracking metrics over time, teams can determine the number of overrides performed during each weekly or monthly planning cycle and take steps to further optimize processes and reduce exception scenarios.
Algorithms can also help make daily forecast planning processes even more precise at a larger scale. For example, with AI technology and advanced algorithms, I’ve found that demand planning teams of 25 to 40 employees have been able to monitor forecasts for up to 200,000 products across dozens of locations each week. This level of productivity would be unheard of without the assistance of advanced technology.
Beyond automation, AI capabilities can also help teams make better-informed decisions when short-term supply chain disruptions crop up. For example, if severe weather conditions temporarily shut down a distribution center, and planning teams have input weather forecasts and logistics data into a planning platform’s algorithm, the team could have access to real-time information that gives them the capability to respond to a short-term disruption, such as rerouting product deliveries to other regional distribution centers for the next few days or weeks until the affected distribution center is operating at full capacity.
AI technologies might not be able to prevent short-term disruptions, but AI-powered algorithms and data could give teams enough advanced notice so that they can come up with an alternative game plan. This allows them to keep their supply chains running while also focusing on resolving larger supply chain issues at hand.
Long-Term Strategic Planning
When short-term tactical planning becomes more automated, planning and management teams can focus more on mid- to long-term scenarios that can help drive the business forward. Planning processes become more collaborative, as AI platforms enable teams across multiple departments (e.g., operations, planning, finance and sales) to glean insights from the same data sources, which helps reduce silos that may hinder a company’s planning capabilities. Scenario planning is becoming far more sophisticated regarding financial tradeoffs, risk and opportunity analyses, and leading indicators in business decision-making processes. Based on my industry experience, I expect that those areas are where business intelligence will continue to evolve with AI and that using knowledge models will become even more critical for businesses.
AI technologies also allow companies to rapidly perform “what if” simulations to determine how different factors (like regional weather patterns and logistics delays) will impact their supply chain and delivery timelines. When done at scale, incorporating “what if” scenarios into strategic integrated business planning processes is a valuable capability that can add millions to a company’s bottom line.
For example, a large retailer could realize millions of dollars in avoided freight expedited costs by performing “what if” scenarios on their logistics costs as part of the integrated planning process to better balance supply and demand. From a CPG industry perspective, if a large beverage company wants to determine ways they can better meet customer demand, advanced algorithms could allow them to perform hundreds of scenarios that simulate the cost and outcomes of activating available production lines or extending shifts, which could help increase the company’s revenues.
Adding Value To Planning Practices Through AI
We’re on the cusp of a revolution regarding how AI can positively impact business planning processes and provide opportunities for a company’s workforce to collaborate more efficiently on strategic decisions that can elevate the business. For a long time, enterprise resource planning (ERP) systems have taken up the majority of spending allocated to an enterprise technology stack. However, I see a substantial amount of value leakage in integrated business planning, and AI technologies can provide an opportunity to address this.
By incorporating AI, as well as existing big data and algorithmic technologies, into what can be considered the “brain” of the enterprise, an optimized planning system can generate value and become an enabler in the enterprise. It can accomplish this by allowing teams to work together more cohesively using the same dataset as a standardized source of information to plan more effectively, handle tactical needs more efficiently and place greater emphasis on strategic initiatives across the business.
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