#supplychainplanning #forecasting #demandforecasting #integratedbusinessplanning #sapibp #treehouse

I was trying to explain to a friend of mine, who is in the medical field, how I help companies improve their forecast accuracy and optimize their supply chain. He had a hard time understanding the difficulties and challenges involved. So, I asked him, “Do you know what you’re going to buy at the grocery store next month?” He looked at me blankly and said, ‘No, I don’t.’

My response was, ‘So, you don’t know what you’re going to buy next month, or the following month, or 3 months from now. Just imagine how we not only plan and produce it but also make it available at the correct location.

Before we delve into a detailed discussion on improving forecast accuracy using AI, I would like to simplify the concept of forecast accuracy for those who are not familiar with supply

In supply chain planning, forecast accuracy refers to the degree to which a predicted demand forecast matches the actual demand experienced by a company. The accuracy of demand forecasts is critical for effective supply chain planning as it helps companies determine the

•  right levels of inventory,
• Procure the right quantities at the right time
• plan production schedules
• Plan distortions
• and allocate resources efficiently.

 Forecast accuracy in supply chain planning is typically measured using statistical metrics such as Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), or Mean Squared Error (MSE). These metrics provide a quantitative measure of how closely the forecasted demand matches the actual demand.

 High forecast accuracy is important for supply chain planning because inaccurate demand forecasts can lead to

•  stockouts
• excess inventory
• increased costs
• decreased customer satisfaction.
• Inaccurate forecasts can also lead to production delays, disruptions in the supply chain, and increased transportation costs.

To improve forecast accuracy in supply chain planning, companies can use a variety of techniques such as statistical modeling, machine learning, demand sensing, and collaborative planning. It’s important to continuously monitor forecast accuracy and adjust planning processes and models as necessary to ensure that they are producing accurate forecasts

As mentioned above, there are several statistical metrics that can be used to measure forecast accuracy in supply chain planning, including:

Mean Absolute Deviation (MAD): MAD measures the average deviation between the actual demand and the forecasted demand. It is calculated by taking the absolute value of the difference between the actual demand and the forecasted demand, summing these differences, and dividing by the number of observations.

Mean Absolute Percentage Error (MAPE): MAPE measures the percentage difference between the actual demand and the forecasted demand. It is calculated by taking the absolute value of the difference between the actual demand and the forecasted demand, dividing it by the actual demand, summing these percentage differences, and dividing by the number of observations. 

Mean Squared Error (MSE): MSE measures the average of the squared differences between the actual demand and the forecasted demand. It is calculated by squaring the difference between the actual demand and the forecasted demand, summing these squared differences, and dividing by the number of observations.

Symmetric Mean Absolute Percentage Error (SMAPE): SMAPE is similar to MAPE, but it takes into account the magnitude of the actual demand and the forecasted demand. It is calculated by taking the absolute value of the difference between the actual demand and the forecasted demand, dividing by the sum of the actual demand and the forecasted demand, multiplying by 2, summing these percentage differences, and dividing by the number of observations.

These metrics can be used to assess the accuracy of different forecasting models, compare the performance of different supply chain planning strategies, and identify areas for improvement in demand forecasting processes.

Improving forecast accuracy using AI-based software can be achieved through several steps: 

Data preparation: Collecting and organizing data is the first step towards improving forecast accuracy. Ensure that the data is clean, accurate, and relevant to the forecast model. AI models can help prepare the data, reduce noise, and make it ready for stat forecasting.

• Choosing the right algorithm: Different algorithms may work better for different types of forecasting problems. Understanding the characteristics of the data and the forecast problem can help choose the most appropriate algorithm.
• Market Intelligence: AI models can learn from market inputs and an open source of data can help improve the inputs to the overall demand forecasting process
• Training the model: Training the AI model takes a few iterations. The AI-based software needs to be trained on historical data to learn the patterns and relationships between variables.
• Fine-tuning the model: Fine-tuning the model involves tweaking the parameters to achieve the best possible accuracy. This can involve experimenting with different algorithms, tweaking hyperparameters, and adjusting the input variables.
• Testing and validation: Testing the model on a hold-out dataset can help validate its accuracy and assess its generalization performance.
• Continuously monitoring and updating the model: Markets and conditions change, and it’s important to monitor the performance of the model over time and update it as necessary to ensure its accuracy.

Overall, improving forecast accuracy using AI-based software requires a combination of data preparation, algorithm selection, training, fine-tuning, testing, and continuous monitoring and updating

There are several applications currently available in the market that claim to use AI models to enhance forecast accuracy. However, the full benefits of these applications are yet to be realized by integrating access to open pools of market data and integrating with B2B and B2C point of sale systems to accurately predict consumer behavior. Although demand planning in the supply chain is leading the way in artificial intelligence, there is still a lot of work to be done on the supply side to automate production planning, procurement, and supplier Integration.