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We've all been there.
One day, demand is high, forecasts are rosy, and life is good. The next, demand has plummeted, and you're sitting there with a mountain of inventory and booked capacity with no demand to support it.
Wild swings in demand have always been a headache. But with supply chain volatility and complexity increasing, it's becoming harder and harder to prevent these swings.
For those looking to understand this problem and do something about it, it may be time to consider whether you're looking at the right problem. And when all is said and done, your operation may not have a demand problem; it may have a data problem.
Human analysis of big data sets is severely limited, and the sheer volume of data that needs to be analyzed is overwhelming. Today's manufacturing equipment generates unprecedented data points that can be overwhelming and make it difficult for humans to process.
While more data is always a good thing, you must be able to manage it, and it must be accurate. That leads to the second problem, that manual data processing and analysis are highly error-prone, subject to bias and omissions, and are usually time-lagged.
With supply chain visibility, human analysis is also impacted by the complexity and complexity of the supply chain network. This complexity, combined with less than reliable data, makes it challenging to identify and predict supply chain disruptions and uncover trends in demand.
This reality is essential in global supply chains where raw materials and components may travel globally to reach producers. Without visibility, problems arise without warning at any point in the supply chain.
Accurate supply chain visibility requires access to transactions and data from each department in production and across the entire supply chain. Historically, this data has resided in silos making data management and analysis even more complex.
Machine learning (ML) is an outgrowth of artificial intelligence that processes machine and system data to "learn" and predict outcomes like a demand. It’s a powerful tool for analyzing big data sets to leap beyond the limitations of human analysis to deliver accurate forecasts, predict demand, and mitigate the errors of human-based analysis.
Machine learning algorithms identify trends and patterns in data not detectable to humans. By using historical and current production data, machine learning's performance improves over time. The more data is added to the system and analyzed, the more accurate its forecasting model becomes.
ML identifies patterns and trends and considers variables like seasonality, promotional activity, and market trends for a more accurate forecast of future demand. Supply chain managers can leverage ML to predict demand accurately and adjust their supply chain strategy. This capability reduces the risk of stockouts or excess inventory, lowers costs, and creates an agile and flexible supply chain.
Depending on the software solution used, ML consists of two different approaches. One ML type is supervised learning, and supervised learning requires users to enter parameters for how the algorithm should analyze data.
This is useful where numerous unknowable variables must be accounted for. For example, in the case of supply chain management, customer responses to questionnaires or long-term weather impacts on seasonality may be considered. ML would use techniques such as linear regression to define a forecast path.
Another machine learning type is unsupervised learning. Unsupervised learning is best for predictable data sets to look for less obvious patterns to make their determination.
Machine learning can help businesses predict demand accurately so that they won't be surprised by shortages or unexpected fluctuations in demand. It can also help supply chain managers make proactive decisions.
When production is on the line, don't be surprised by your demand. Your data and the advanced machine learning features of the Plex DemandCaster Advanced Business Planning Software will help you eliminate the errors and surprises that threaten your business.
Instead of reacting, use machine learning to manage your demand and create accurate forecasts that reflect true supply chain visibility.
Contact us to learn how our software will help you.