When deciding to implement a demand planning cycle, one of the crucial elements of getting your demand plan right is the data you use to create it. Is the information accurate? How to gather the RIGHT data for demand planning? If you can’t trust the data, the demand plan is a useless endeavor.
This is why it’s important to know how you gather data and how to keep it clean. Otherwise, you’re left reacting to changes rather than planning for them. Considering demand planning relies heavily on information, here is how you could gather data for demand planning:
Sources: Multiple-ERP – CRM – PLM
Multiple ERPs, CRM and PLM are common in global businesses. You may have acquired new facilities or expanded to new territories. You also might have different departments using different tools to house their information. It’s fair to say that your information is likely not unified in one system. But, it doesn’t mean that you’re stuck with siloed or disjointed data by department, facility, and/or function.
By moving to a cloud-based supply chain management system, you can interconnect these siloed data pools to create a data lake for your demand planning. By plugging in multiple systems, you take your data from flat file to web API. In other words, you’re able to connect different apps together to sync up your information. This prevents manual processes, loss of information, and allows you to interconnect apps for modern and dynamic data gathering. Data management allows you to easily pull up information and become proactive rather than reactive to changes in your business environment.
Unifying your data is only the first step in gathering your data for demand planning. The quality of your information is key. Your demand plan is only as good as the data it’s using. And, if you can’t trust your data, you can’t rely on the demand plan. Your data should be consolidated so you can keep it up-to-date, remove duplicates, regularly audit the information, and maintain a level of quality that you can trust.
You can significantly improve data quality when you include machine learning and artificial intelligence (AI) in the solution you use. Machine learning can automate processes through simple, recurring tasks and use pure data analysis. It can also augment and suggest a course of action based on ‘what-if’ analysis as the data becomes available. By including machine learning in your data quality endeavours, you create an environment where your demand plan is actionable and proactive.
Finally, by having a demand planning cycle in place you’ll be able to improve your business accuracy, save time, and ensure better revenue figures. Effective demand planning starts with your data. With accurate data you can assist supply chain managers to accurately forecast product production and save them time in crunching data. What’s more, you can see issues before they impact the business negatively.
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