Dynamic Resource Planning: Optimizing Business Processes from Material Procurement to Transportation

The more successful that companies are in adapting their resources to meet changing requirements in real time then the greater the efficiency and productivity of their operations will be. By working with paretos they will have the perfect tool – customized Artificial Intelligence (AI)-based and with unprecedented forecasting accuracy.

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March 21, 2023
minutes read
Decision Intelligence
Supply Chain

One of the major challenges of dynamic resource planning is to obtain a reliable picture of future requirements to serve as a basis for available resources and their allocations in real time. This requires the identification of every relevant data system and source that a company uses to record business-critical factors which is then merged, consolidated and evaluated to extract the required heterogeneous data to be used as the basis for predictions to enable optimal resource allocation.

In practise, however, if there are shortcomings in the way data is collected, if it is incomplete and/or if it is inaccurate, it can negatively effect the results of the analysis to varying degrees – sometimes even rendering the forecasts produced completely useless. It is obviously essential to ensure the highest quality and accuracy of data-based processes for successful dynamic resource planning. This is where the industry leader, paretos, demonstrates its strengths to the fullest and offers forecasts with maximum accuracy.

Industry-leading AI for best-in-class solutions

The multidimensional AI-based tool was developed by paretos specifically for highly complex, dynamically changing planning processes and requirements with multiple target variables. It can be applied cost-effectively and tailored to deal with any challenges uniquely associated to your business, using a modern no-code platform that is simple to use and you can trust. This means that any of your employees, with or without a technical background, can create data analyses and optimized forecasts from which they can make resilient and future-proof business decisions.

To do this the Application Programming Interface (API) of the paretos platform accesses all internal and external data sources of the company to collect both historical and master data as well as all business-critical real-time data. Because a solid database is essential for successful dynamic resource planning, paretos works with the client to verify that all sources are accurate, up-to-date and complete and then automatically explores any uncertainties based on legacy data. The paretos AI also independently detects and corrects irregularities and patterns within the data. The finished product results in a complete and reliable picture of future requirements for dynamic resource planning to enable the creation of far-reaching and extremely accurate forecasts.

In one of our success stories with a leading national parcel delivery company, paretos was able to increase forecast accuracy to almost 100% after just five months to provide its industry-leading solution. With the help of automated data analysis, over 95% accuracy was achieved on the first day, over 93% on the second day and on the third day – the day with the highest uncertainty – accuracy was still over 90%. Among other things, this enabled the client to optimize its delivery and improve value creation, which in turn gave it outstanding advantages within its dynamic, highly competitive business environment.

Be the fastest in a fast-moving market

paretos’ dynamic resource planning offers customers invaluable advantages not only in logistics, but across all industries. From labor and materials to machinery and transportation, paretos’ data-informed forecasts enable a wide variety of resources to be optimally matched to current requirements in real time and this leads to efficiently allocated operational sequences to be predicted so that business goals can be achieved in the best possible way. For example, inventory and personnel allocation within companies can be improved by planning and reallocating employee capacities according to workload depending on department, team level, skills and/or customers. And in material procurement, the ordering process can be automated by predicting order volumes and material requirements.

Automation reduces errors caused by manual processes and enables better responses to planning requirements so that, for example, relevant external factors can be reliably identified. Overall, dynamic resource planning enables companies to increase efficiency and productivity within their operations resulting in saving time, money and resources. This is particularly true for companies that work with scarce resources or operate as part of a fast-moving market environment. It is a decisive step on the way to optimized business processes in which complex dynamic planning processes are mastered and constantly changing planning requirements are optimized in a flexible, demand-oriented and transparent manner.

We are the leading AI-based decision intelligence platform for effective, data-driven decision-making processes in companies. No more bad decisions!
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