Consumer products companies face many significant challenges. As markets and supply chains continue to become more global, and as customers continue to demand more convenience and personalization from consumer products companies, we see increased pressure to improve shrinking margins and meet customers’ evolving needs. One of the key processes that enables this balance is demand planning, which determines the anticipated demand for a company’s products and services. The demand planning process is linked to annual operating and financial planning processes and when done right, it helps provide the plan and flexibility to better leverage the supply chain, meet customer needs and ensure that the right product is available in the right sales channel at the right time. When properly linked to the end-to-end process, a consumer products company can derive both its operational plan and its consolidated corporate plan and forecasts from this process. As implied, there are many dependencies as sales estimates are adjusted based on consumer demand, other accounts such as sales and other variable costs will also be impacted, all of which trickles down to a company’s bottom line. Ideally, a demand planning model would incorporate all these changes so that any change in demand will result in an output of corresponding change to the operating plan and key metrics such as operating profit, earnings, EBITDA and net income.
In a well-defined demand planning model, business users can see the impact of changes by business unit and, more importantly, see what changes can be made to improve future results. This process requires a well thought out end-to-end design and a technical solution with the ability to calculate changes to the model quickly and efficiently, while also allowing key users such as the sales team, or those driving demand, to give input into the process. We often see our consumer products clients try to leverage an Excel-based process, but the complexity and volume of data and calculations quickly outpaces Excel’s capabilities.
Introducing SAP Analytics Cloud
This is where SAP Analytics Cloud (SAC) for planning comes into play. SAC is a system that allows demand planners in consumer products companies to change key assumptions via an easy-to-use Excel and/or web interface using a sophisticated calculation engine with the power of HANA to quickly calculate results. This allows for rapid iterations and what if analysis that helps companies to better respond to changes in demand (like we saw in the early part of last year when the pandemic shut down large portions of the economy overnight). In addition, SAC provides predictive capabilities giving additional insights not readily apparent in other tools. This key functionality gives planners additional insight into potential trends and or issues and allows for early intervention to deal with increases or decreases in demand effectively. This enables CPG companies to quickly anticipate and respond to changes in demand for hot products, reduce obsolescence for products with reduced demand or that are being phased out, and better manage stable selling items in their product portfolio.
To demonstrate, we recently embarked on using SAC for planning at a large national food products company that needed both a process and technology solution to optimize, enhance and integrate demand planning activities with their financial plan and forecast. This company starts its demand planning process by forecasting units sold by product groupings and customers. Actuals are used as a baseline and then projections are made within SAC. Forecasted units are reviewed and modified by the sales team, which is critical since it gives those closest to the demand the ability to weigh in on projected sales and to provide subjective feedback. The implemented SAC solution provides sophisticated calculations and predictive capabilities, giving a more automated, and objective business view. The combination is the best of both worlds, offering a highly automated process to generate a first pass forecast as well as enabling business users to provide critical on-the-ground input.
After sales unit forecasts are solidified, the next question is: “what will be the financial impact?” This is a calculation-intensive process and requires a large volume of accurate and diverse data points. Master data plays an important role in deriving gross sales, discounts, COGS and other measures from units sold, unit prices/costs and various combinations of units of measure. This is where tight integration of SAC with SAP is critical. By automatically loading in all these factors, SAP bypasses the mundane yet critical step of synchronizing master data across systems. This allows business users to perform higher value tasks like determining the estimates for sales units, and what to sell to whom through which channel. SAC is then used to generate the end financial result, allowing decision-makers to review and identify the most profitable combinations and then optimize the plan for production. The process is significantly simplified with advanced features such as smart insights and other analytic features.
A real-life success story
Our team encountered something interesting when we ran a simulation through SAC while using master data from SAP. The forecast financial results came to a number that was quite different than the existing Excel-based manual process. The question was asked, “why is SAC not correct?” Upon further review, we found that SAC was, in fact, giving the right answer, and the offline Excel-based result was incorrect (with the potential to significantly impact financial results). This was an eye-opener for management and pointed to the value of using SAC and a more automated process. A more accurate result was generated because of SAC integration, and it was generated more quickly and efficiently. More importantly, this is a repeatable process, allowing for multiple iterations. In addition, important assumptions such as per unit pricing and cost of sales can be easily adjusted in SAC and allowing for the creation of new versions to test and review impacts to pricing, cost, and other factors to arrive at the optimum price and mix of business to achieve maximum profitability. Finally, the integration provides a layer of data governance, whereby manual processes and human error are reduced. This automation linked with the demand planning processes, provides an incredibly efficient and effective solution, all made possible by the features, integration, and functionality of SAC planning.
In this example, SAC utilized interfaces from SAP to load in unit pricing, unit cost of goods sold and assumptions for discount factors and then used data actions to calculate the result. SAC also used a separate interface to bring in factors for unit of measure from SAP so that units could be calculated and combined in real-time. The net effect for our national food products client was that any change in volume would provide near real-time recalculation of gross margin as well as other key metrics of financial results. The below diagram shows the process, data flow and calculations performed.
This information can then be shared with the relevant stakeholders by running real-time reports or if necessary, this information can be broadcast or distributed to users and other systems.
This client story demonstrates just a small piece of SAC demand planning capabilities. For example, this demand information can be sent to ECC and/or S/4HANA’s product allocation engine/tables when demand exceeds supply. Demand can also be sent to other systems to be consumed and acted upon. How SAC fits into the organization is an individual choice, but every consumer product company should be using or at least consider using SAC for improved:
- Budgeting, planning and forecasting
- Integration with other SAP and third-party systems
- Predictive capabilities
- Real-time calculations
- User experience
- Business insights