Analytical planning platforms are solutions that support companies in the decision-making process for their complex operations. In general, such platforms have an “analytical brain” capable of processing large volumes of data and combining them through predictive and/or prescriptive analytical techniques to suggest plans that meet their needs and goals, raising the standard of achieved results.
Main Drivers
The adoption of this type of solution has grown considerably over the last decade. With the increase in processing capacity and the advancement of cloud computing, it has become possible to use a greater amount of data to support the necessary analyses and obtain results within an acceptable timeframe for planners.
In addition to computational capacity, customers are becoming increasingly demanding, requesting customized products with high quality, affordable costs, and the shortest possible lead time. This makes company operations planning complex and personalized — clearly a promising environment for implementing analytical solutions.
The Major Challenges
However, caution is needed to succeed in implementing this type of solution. It is necessary to balance flexibility and customization with ease of use. Many companies, when beginning the adoption process of analytical platforms, are already conducting their planning through spreadsheets, which do not allow the creation of detailed plans and consequently limit scenario generation for building a more robust plan.
When specifying the requirements of the models that will support planning, it is common for users to request a set of features that go beyond the company’s real needs. This often creates the impression that once the platform is implemented, all decisions will be made automatically without human intervention — which is not true in practice. Whatever the solution, it exists to support the decisions of someone who is not capable of processing the same volume of information or correlating data at the same speed and sophistication that such models can provide.
As a result, it is very common to see analytical platform implementation projects exceeding their planned timelines due to unclear requirements. In many cases, these solutions never actually go into production, generating the perception that they “don’t work” and that it is easier to continue working with spreadsheets.
How to Succeed
To avoid this undesirable outcome, a good practice is to conduct a collaborative effort between the company and the solution provider. On the company side, it is essential to have a multidisciplinary team capable of clearly communicating the organization’s needs and objectives to the platform provider.
On the provider’s side, the team should include professionals with both technical and functional skills who can act as the client’s advisor, guiding them on the best path to ensure the implementation process is well executed and that the company can extract maximum value from the deployed solution.
dhauz has emblematic cases of building analytical platforms that support decision-making in integrated planning processes. Contact us — your business challenges are what drive us.
