Demand Accuracy In Your Supply Chain
During the pandemic, a lot of the focus on supply chain tended to be on the supply side, and rightly so, due to the highly publicized sourcing, production, distribution, and transportation issues. As a result, organizations continue to look to increase productivity, flexibility, and agility through programs such as Lean, increased automation, robotics, artificial intelligence, and others.
However, the supply side shouldn’t be the only area of focus. To one degree or another, human behavior has tended to exacerbate the issue, resulting in volatility on the demand side.
The impact of panic buying, hoarding, and last-mile delivery option changes has created a clear example of the bullwhip effect, where variations at the consumer demand end of the supply chain result in a ripple effect that highlights and exaggerates weaknesses in upstream processes.
Improving Timing and accuracy
This brings us back to visibility, collaboration, and communication as the keys to improve demand timing and accuracy. These strategies can help minimize the impact of a global pandemic or other local and global events that seem to be regularly occurring.
Companies that still primarily rely on using historical demand data to create forecasts are, in effect, driving while looking in the rear-view mirror. We know the result of that.
Not only do we need to collaborate with our key customers, it is also critical to drill down further toward the customer. Using data such as retail and e-commerce point of sale (POS) and customer warehouse withdrawals helps determine what is really going on.
It also helps to monitor events such as weather, environmental issues, changing tastes and preferences (sometimes triggered by and found in social media) that can cause fluctuations in demand and assess their possible impacts.
Luckily, we are in a time when we finally have a better set of technological tools to deal with the ever-increasing amount—and speed—of new events and changing tastes and preferences that can increase demand volatility,
Today, organizations need to develop highly robust demand sensing and shaping processes with the aid of technology to flatten out and better predict this volatility, at least to some degree. Organizations also then need to share these resulting, more accurate forecasts up and down the extended supply chain to gain maximum benefit or “surplus” to all participants.
Enabling Accurate Forecasts
However, as the volume of data coming at us is exponentially increasing, now is the time to finally start embracing the following concepts to enable more accurate forecasts through better, more informed decision making:
Supply chain data analytics. This consists of:
- Descriptive analytics (what happened)
- Diagnostic analytics (why it happened)
- Predictive analytics (what is likely to happen)
- Prescriptive analytics (what action to take)
Machine learning, which is based on the idea that machines should be able to learn and adapt through experience.
Artificial intelligence. AI is a broad idea that machines can execute tasks “smartly.” AI applies machine learning, deep learning, and other techniques to solve actual problems.
Improving demand processes not only requires using new and better technologies, but it also requires employees who are trained, willing, and ready to use them.