Artificial Intelligence is taking over business processes and tools including supply chain and demand planning. Companies are turning to AI to compute more complex algorithms and include more variables in their forecasts such as market dynamics, store expansions, or economic trends. Companies are looking to AI to improve forecast accuracy which can lead to improved inventory management, less back orders, less obsolete inventory, and higher customer satisfaction. The increase in computing power behind AI tools makes it easier to compute large amounts of data and include more variables in demand forecasting, but the tools come at a cost and companies need to weigh the advantages and disadvantages before implementing a new tool.
Advantages:
Improved Accuracy: AI algorithms lead to more precise demand forecasts. AI tools process larger volumes of data including additional variables beyond traditional POS or demand history capturing patterns and anomalies that traditional models miss.
- Agility and Speed: AI tools provide near real-time insights, enabling companies to respond quickly to changes in market demand or supply chain disruptions. They allow for scenario planning of multiple forecasts at once to determine the impact of events like a strike, natural disaster, or economic changes.
- Enhanced Decision-Making: By automating data analysis, AI frees up demand planners to focus on strategic decisions instead of manual forecasting tasks. Demand planners won’t spend as much time managing the models and forecasts and can focus on collaborating with cross functional partners.
- Cost Savings: Improving forecast accuracy leads to cost savings for companies. These cost savings include less stock outs, less expedited shipments, less overtime for the manufacturing plant, and less holding costs.
Disadvantages:
- High Implementation Costs: The upfront investment in AI tools, infrastructure, and training can be significant. Companies need to weigh the cost of the AI tool with the forecast accuracy improvement they expect and what cost savings they expect from the increased forecast accuracy.
- Data Dependency: AI requires large, clean, and reliable datasets. Poor data quality can lead to inaccurate forecasts. If AI receives bad input data, it will not improve the forecast’s accuracy. Companies will need to put more time into ensuring the data is accurate before entering AI tools.
- Data Complexity: AI forecasting systems require large amounts of data and have complex models that can be challenging to implement and maintain. They require strong expertise from the data managers to maintain. In addition, increased data storage and computing power are required.
- Change Management: Shifting from traditional planning to AI-driven processes can face resistance from teams accustomed to legacy methods. Demand planners and their cross functional partners in sales, finance etc. should be trained on the AI tool to build a better understanding of the tool and to build trust in the forecasts it creates.
AI tools are trending and are the future in many different industries including demand planning and forecasting. Companies are looking to implement new tools, but before they implement, they should consider the positive and negative impacts. They need to determine if the incremental costs of the AI tool drive enough forecast improvement and will result in enough cost savings in their supply chain to justify the investment. AI is a long-term investment and will be part of the future of demand planning and forecasting, but companies should be strategic in their implementations to ensure they are getting measurable returns.
Interested in learning more? Contact Tanya & Price Group at info@tanyapricegroup.com.