Challenges in Demand Planning (and How AI Can Be the Solution)

The most persistent and costly problems that companies face are due to poor demand forecasting.

However, the demand planning process in companies is being underestimated to an unprecedented extent. Despite its significant impact on business operations and the anticipated challenges for the year 2024 and beyond, many companies still rely on the outdated practice of performing their planning calculations in their ERPs, or worse, in spreadsheets. This combination of factors creates a perfect storm that only those companies willing to innovate and revolutionize their supply chain processes through AI implementation will be able to overcome. 

Demand planning in theory is easy, but in practice, it can become very complex.

Impact of Demand Planning on Business    

Organizations that refine their demand planning processes experience substantial benefits, such as preventing loss of sales, reducing inventory costs, and improving customer satisfaction.

Forecasting serves as the cornerstone of business planning, whether as input for strategic decisions like Strategic Planning or Sales and Operations Planning (S&OP), or for short and medium-term decisions like determining a firm production or purchasing plan. Additionally, its impact extends to other critical areas such as finance, purchasing, and even human resources, among others. 

• Efficiency in Manufacturing: In the manufacturing environment, delivery deadlines are crucial at every stage of the process; however, the process can become confusing and error-prone if the involved parties do not have a clear understanding of the correct number of units to produce. This can result in wasted time, materials, and resources. Moreover, if deadlines are not met, it disrupts the production plan, causing significant stress throughout the company, which must adapt and incur unplanned costs to avoid customer service loss. 
   
• Efficient Warehousing: If the stored inventory exceeds the actual demand from customers, it indicates poor demand planning. Excessive inventory storage is costly, and the more there is, the higher the associated costs. 
   
• Precise Logistics: Transportation costs represent another significant expense in company budgets. Incorrect demand calculations complicate shipping and distribution planning. Sending too few products can lead to the loss of customers to the competition, while sending too many products results in unnecessary expenses and waste generation.
   
• Excellence in Customer Service: Customers are the fundamental pillar of the business, and the company must maintain a solid reputation. Lack of product availability or delivery delays can drive customers to explore other options.

Looking Ahead to 2024 and Beyond 

The demand planning process is not particularly straightforward, often presenting a combination of internal and external challenges.

• Rise in Personalization and Customization: The growing demand for customized products, whether in B2B or B2C environments, poses a significant challenge. Motivated by various factors such as changes in product mix, the opening of new channels, competitive movements, strategic adjustments, and promotions, this increase in personalization creates instability in demand. Adapting to these dynamic changes becomes essential for effective planning. 
   
• Empowerment of E-commerce: The unstoppable rise of giants like Amazon and Mercado Libre has raised customer service expectations. The proliferation of social commerce, where demand originates through social networks, has transformed purchasing behaviors, imposing additional challenges in the inventory realm. Companies are forced to manage a broader range of products, larger inventory volumes, and even more agile production or acquisition times to stay competitive. 
   
• Multitude of Variables, Both Internal and External: The complexity is magnified when considering the multitude of factors impacting supply and demand. A successful forecast must address each of these factors, assessing their relevance and understanding their impact on demand. Identifying all variables influencing demand requires experience and knowledge, and unfortunately, this challenge will only intensify over time. Established processes and a talented team may not be enough. Factors like seasonality, the strategic location of stores for retailers, competitor activity, new product launches, globalization, geopolitical factors (such as nearshoring in the Mexican case), events like the COVID pandemic, and international conflicts (such as the wars between Ukraine and Russia, and the Israel-Palestine conflict) are just a few examples of the variables that must be considered 

Why Aren't Demand Planners Adopting AI?

A respected demand planning team is constantly seeking to improve its accuracy by at least a percentage point; however, when presented with the opportunity to implement AI or machine learning, people in this field are very cautious.

• Lack of Executive Leadership Support: Executive leadership support plays a crucial role in the adoption of AI. Only a few leaders are willing to take risks and allow experimentation, providing the necessary resources for technological projects. Alignment from the top down must revolve around data use and belief in the power of AI, engaging all key stakeholders to share resources and information. 
   
• The Nature of AI: Most applications that use artificial intelligence are not easily understandable for classical business intelligence systems. The increase in predictive accuracy of machine learning algorithms leads to increased model complexity, making it extraordinarily difficult for humans to understand. This phenomenon is commonly known as the "black box" of AI. In the realm of supply chain management, where decisions and planning involve active human participation, these individuals are responsible for such decisions. It is challenging for them to trust recommendations emerging from the black box as these may be difficult to defend or explain clearly. 
   
• Love of Spreadsheets: Related to the previous point, many planning teams live in the world of spreadsheets. The technical and mathematical nature applied in the Supply Chain leads teams to deal with countless versions of Excel files and attempt to add more variables to formulas, despite the complexity. A fondness for spreadsheets can introduce human bias and resistance to more advanced calculations. 
   
• Organizational Barriers: Organizational barriers include the size and scale of the company, the workforce's attitude toward change, understanding of AI, competition between intra and inter-company verticals, and misaligned goals. Initial high costs can also serve as a barrier, as some leaders may not enthusiastically see AI investment due to inherent priorities in their area and internal competition for budget. 

Like an investment, the best time to implement AI was a year ago; the second-best time is now. 

If you don't have AI in your processes or are already in an implementation project, it's highly likely you won't catch up to your competitors who are already doing it. 

While AI has been a trendy topic, driven in part by the excitement generated by ChatGPT and generative AIs, the reality is that many global companies have been actively adopting artificial intelligence for the past decade; however, there still exists a significant majority that has not, choosing the strategy of being ”fast followers,” or waiting for others to lead the implementation before joining. This strategy may not be the most appropriate to fully leverage the benefits of AI. 
 
And much like in the financial world and compound interest, every day that artificial intelligence better understands your business processes, demand planning, and inventory movements, it becomes even more accurate and provides more tangible benefits. 

• Integrating AI into Business Processes Takes Time: It's essential to recognize that integrating AI into business processes takes time. In the case of supply chain planning, it's not a process that can be implemented overnight. Although the technology offers tremendous potential to reduce costs, like any innovation, incorporating AI into business processes involves training and educating employees, overcoming resistance to change, and adapting to significant cultural shifts. However, the long-term benefits far outweigh the initial challenges. 
   
• The Human Factor Is Crucial When Using AI: Competitors who have already implemented artificial intelligence in their demand planning processes understand that leveraging it is advantageous, but they acknowledge that the human factor remains crucial for success. Their human capital is already equipped with the necessary training, having overcome cultural shifts, and possesses the technical and qualitative capacity to leverage this technology quickly and effectively. The combination of artificial intelligence and employees leads to more creative and precise decision-making. 
   
• Empowered Economic Capabilities: According to a Gartner study, even a small error in your forecasts can trigger significant financial problems: a 1% slip results in a 2.7% overcost in finished product inventory, a 3.2% overcost in transportation, and a 3.9% overcost in expired inventory. For a large-scale company, each month without using AI to improve that 1% accuracy in forecasts means substantial losses in critical indicators for business growth, such as cash flow, profit, and profitability. 

The Future Waits for No One 
 
The business landscape is in constant evolution, and technology shows no mercy. Companies that proactively embrace AI in their supply chain not only reap immediate benefits but also position themselves for the future. Continuous innovation is essential, and AI provides the foundation for ongoing evolution and adaptability to market changes. 
 
Artificial intelligence is not just the future of the supply chain; it is the present that defines competitiveness. While the best time to implement AI was a year ago, delaying no further and starting now is a strategic decision that will make a difference in the efficiency, accuracy, and overall success of your supply chain. The revolution is already underway; join now or risk falling behind. The future of the supply chain is driven by artificial intelligence, and the second-best time to embrace it is today.