AI in the procurement process – Budgeting & Planning (Part 2)
Today we continue with the new series of articles. This time we deal with the 2nd process step (according to my definition), “Budgeting & Demand Planning”. Here is the whole E2E consideration again as a reminder:
A. Overall Procurement Area Strategy
B. Budgeting & Demand Planning
C. Category / Cluster Strategy
D. Sourcing Process
E. Vendor Management & Onboarding
F. Ordering Process
G. Contract Lifecycle Management
H. Procurement Reporting & Success Tracking
We will also analyze this sub-process on the basis of the criteria established in advance.
| Criterion | Rating(1 = low; 5 = high) | Comments |
| Data availability | 5 | With this sub-process, we can basically say that there are large amounts of data.For one thing, the budget itself is an extremely valuable source of data. Yes, it always depends on the depth of the budget in which details it is presented. The more detailed, the closer we are to a project pipeline. We should also not forget the historical data from our ERP. Even though these provide us with a retrospective, this can be very important in order to recognise certain patterns and use them for the following year. |
| Process complexity | 5 | As we all know, this process is fundamentally very complex. Especially because all cost-center owners are involved. And honestly, how often are Excel files still used as the main tool? |
| Risk within the process | 4 | Errors in budgeting and demand planning can have significant consequences, leading to either oversupply or shortage of goods and services. AI tools can help reduce these risks by providing more accurate forecasts. |
| Compliance | 3 | In this sub-process, compliance could refer to adhering to the set budget constraints and keeping within forecast accuracy. Using AI tools can assist in this by predicting and managing the budget and demand in a more streamlined manner. |
| Repetitiveness and Consistency | 5 | Budgeting and demand planning is a repetitive task that is required at regular intervals (monthly, quarterly, yearly). AI tools can automate this process, saving time and reducing the chance of human error. |
| Scalability | 4 | The use of AI in this sub-process can be scaled up or down as required, with the capacity to handle large data sets and numerous variables. This is especially beneficial for large organizations or during periods of rapid growth. |
| Scope for Decision Automation | 3 | AI tools can automate certain decisions in this process, such as predicting demand and adjusting budgets accordingly. However, human intervention is still needed to validate and interpret these decisions. |
| Predictive Capabilities Requirement | 4 | AI tools can significantly enhance the predictive capabilities of the budgeting and demand planning process. They can use historical data to identify trends and make predictions about future demand and expenditure. |
| Total: | 83%(33/40) |
From this analysis, it can be concluded that the sub-process “Budgeting & Demand Planning” is highly suitable for the use of AI tools. AI can improve the efficiency, accuracy, and predictive capabilities of this process and limit risks.
Regarding AI tools that can be used in “Budgeting & Demand Planning”, a few include:
- Budgeting tools like Centage, Vena Solutions, and Prophix that use AI to improve the accuracy of budgeting and forecasting.
- Demand planning tools such as Anaplan, FuturMaster, and o9 Solutions which leverage AI for demand prediction and inventory optimization.
- Integrated procurement software like SAP Ariba, Coupa, and Jaggaer which offer AI capabilities for both budgeting and demand planning.
And as always: all the suggestions above are only a very small selection of the solutions currently available on the market. As I said, please be patient until the “AI Procurement Compass” arrives.
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