A Complete Guide to Forecast Improvement in SAP Integrated Business Planning

“Improve Forecast accuracy to gain sales, reduction of inventory and as consequences increase customer satisfaction.“


The blog covers all the aspects of forecast implementation. It aims in helping business owners who are looking to fulfil their future demand profitably with real-time supply chain management. Moreover, business owners can learn how to optimize your supply chain and create value for their company.Read this guide and enable your company to save up to 100 Million Euro (scenario of 500 Million sales). An improvement in forecast accuracy gives you significantly more leeway:

At SCPLAN GmbH we apply a multi-level forecast for this purpose, which improves the forecast quality by 5 – 20 % on average. The forecast results include different levels for the best possible forecast outcome.


With Digital Transformation changing at a rapid pace, it is difficult for top businesses to manage and adapt to the change swiftly. After managing high market volatility, shorter product life cycle, and variation in demand, the delivery lead time still remains competitive. Further along, there are various other daily work challenges like inefficient operations, inadequate forecast accuracy, excessive inventory, and unwanted low service level that affect sustainability goals. All in all, the demand forecasting has become an unmanageable task for business owners to perform with satisfactory results.

This is where Forecast improvements comes into the picture. It has helped top industry leaders in establishing long-term partnerships with their most strategic customers. With this, they are easily managing their growth and sustainability goals with a strong focus on the supply chain.

Thus, Forecast improvements in SAP IBP propose new opportunities to adapt to the new normal context.

Let’s dig deep into what forecasting is and how it can benefit your Supply Chain:

What is Forecasting?

The forecast process is a key part of Supply Chain. It relies on statistical algorithms to interpret historical data and extrapolate it into the future. To project demand based on the market, it is recommended to use an as input for forecast the requested delivery date (RDD) and requested delivery Quantity (RDQ).

Keeping the right amount of product in stock at the right time is critical to businesses. Having too little means running out at inopportune times, causing customers to buy elsewhere. Also, having too much means paying unnecessarily high costs for storage and inventory management.

Key Benefits

When you implement forecasting in your supply chain business, you can be ensured that you have enough supply on hand to satisfy demand. Here are some of the key benefits of Forecast improvements that you can consider before making any decision:

Important aspects of forecasting

Key aspects of forecasting are segmentation and lifecycle planning. At any time the planner needs to know about the priority based on revenue and the volatility of planning data. Within this whitepaper we will explode the segmentation and lifecycle planning.


Supply Chain planners are overloaded with a lot of information, which makes decision making a challenge. Segmentation enables grouping of planning objects based on data and thereby increase user efficiency and planning accuracy. It is used in different area like analytics and custom alerts.

SAP IBP provide Segmentation based on revenue (alternative volume) and volatility. It checks the planning data and determines from the highest revenue to the lowest and from very stable to high volatile sales. Those informations are grouped and called segments. Purpose of segmentation is to priorities all products, which has a high value for the company. Furthermore the volatity tells a planner, which products are easy to forecast and where to focus most. From planners perspective to will helpl to identify, which SKUs are most important for the company. Based on this information it is easy to priorities on most important task on a daily level.

Overview of ABC, XYZ Segmentation:

Based practical experience a segmentation is required on product and product location level. Furthermore from sales perspective to identify your priority of customers, consider to segment as well based on customer and country. In addition a separation of division is in some cases is required, when Business sees big variation in weight of the division.

ABC Analysis

ABC analysis is performed to classify planning objects according to their usage value, or several objects. During ABC analysis, the system allows each object one of the following indicators:

XYZ Analysis

XYZ analysis is performed to classify planning objects according to the variance in a particular coefficient. During XYZ analysis, the system assigns each object to one of the following indicators:

Lifecycle Planning

Phase-In / Phase-Out is part of lifecycle Planning (PLC). It covers following phases launch, growth, maturity and discontinuation.

Since every stock Keeping Unit is an Actual SKU, you want to label the “updated/new” SKU as the SKU you want to forecast whereas you want to label the future SKU as the “Planned SKU.” Most of the forecasting software these days allows you to map the old SKUs to the new SKU and call it mapping Planned to Actual. This way, you can label the Planned SKU as Phase-In, and the Actual SKUs mapped to it as Phase-Out.

All in all, if you identify old SKUs to sell first and communicate these inventories weekly, you indicate selling the old SKUs first and avoid working capital costs to write off inventory. You can communicate this list and hence show the progress, that you have saved.

Phase-out SKU:

Phase-out of products is a necessary step to stop generating forecast and therefore ensure no more capital lost for inventory and disposal.

Life cycle Planning

New products starts with a ramp-up, which is called a launch in the market. From the launch a rapid growth is demanded by the market. When the product is on the market for longer period the sales is stable. However after a while a product will be discontinued due to innovation and customer requirements. Therefore a decline of demand is see and companies needs to react quick enough.

Forecast challenges

With delayed shipment, inconsistent suppliers, and inefficient plants, companies across various countries are facing complex challenges. With forecast improvement, companies can overcome the following mentioned challenges:

Seasonal forecasting plays a major role in seasonality. It includes managing the products that have higher demand during a specific time of year. This can help businesses in managing for these peak season and produce sufficient materials to build the final product and have enough inventory to fulfil the demand given at that time of the year.

However, there are some uncertainties that can still happen because of unforeseen events that can occur during a specific year. There is no accurate way to predict these types of events but is a financial risk that a supply chain manager may make. The thing is the majority of the time, these types of events are rare so it does make more sense for managers to go ahead preparing for the season rather than having not enough products to sell during the peak demand. Having an extra inventory is not always a bad approach compared to the option of not having the supply to meeting the demand and missing that window of opportunity to optimizing profits.

By the above-explained point it is clear that forecasting takes into account many changes, such as demand seasonality, by tracing patterns in historical data. Coming to the other challenge – Outliers. They are the points in historical data that diverge from other data points naturally. They can be either be large or small. They are not considered a significant part of the overall demand pattern because they lie far outside of the expected data range. It is one of the main challenges of forecast improvements.

Since, Outliers can be irregular, but they also can occur due to various planned events, such as sales promotions and unplanned ones, including competitor promotions and natural disasters.

It has been observed that as many as 50% of products and services have demand patterns with “lots of zeroes”, which in return creates different challenges for demand estimation. It leads to the failure to handle “lots of zeroes” correctly can cripple the effectiveness of an operational process in a supply chain business.

To avoid this, it can be vital to divide the products with “lots of zeroes” into two groups: 

All in all, demand with lots of zeroes requires special attention and expertise. The idea here is to consider the estimation process as a risk trade-off. Firms that do this with a proper strategy can see a large improvement in performance.

Statistical forecast is generated automatically by the system. It is the ground for further review, adjustment and alignment. At the end of Demand Planning process, the consensus Demand is published. It includes a lot of human interaction and can lead to wrong conclusion. Therefore, a measurement how good is the system versus how good is the forecast result of a human is supporting to continue usage of system intelligence.

Improvement of Forecasting

The business might be surprised when we say, that best forecast result is not generated by spending a lot of time of finetuning forecast parameters, method, considering weather condition etc. Each mentioned point is important, nevertheless the focus should be on forecasting on multi level. This ensures that the overall numbers on aggregated level, which considers the historical data on e.g. product hierarchy and provides a very good outcome to project the demand for the same product hierarchy. Since each project is individual, it is best practise to determine for each client individually on which level to get the best aggregated forecast.

Furthermore, for detail level for instance on product and customer level a forecast is generated. This forecast will be used to disaggregate from product family to product and customer. By this approach we successfully implemented an improvement between 5 to 20 percent. Our clients benefit from it EVERY YEAR.

Input for Forecasting Improvement:

Forecast improvements is impacted by several factors. Each Business has its own view of important inputs. Therefore most common aspect to improve forecast will be explained below.

SAP IBP allows first to assign the predecessor with the successor:

At the next step forecast dates needs to be maintained.

Improvement based on Multilevel Approach:

Forecasting Multilevel overview:


SAP IBP is providing many templates for analytics and dashboards, which can be used as a starting point. Especially for forecast improvements KPI reports are easy to establish for example by using segmentation and forecast accuracy or Forecast error. This report can be compared every demand cycle and planner can learn from experience in the past.

Dashboard example for Forecast accuracy:

Besides the matrix accuracy view, planner need as well the consider the patterns in combination with the segmentation. Important information for instance about how many seasonal pattern where recognized for A products can be displayed, see example below:


Is your Business ready for forecast improvements? When do you want to establish savings YEAR after YEAR?

We are ready to support your business to create value for your supply chain.


For more information, please reach out SCPLAN GmbH:

SCPLAN GmbH Standort Hanau:
Theodor-Groppe-Str. 2
63452 Hanau GERMANY
Telefon:+49 6181-991 2488
Mail: info@scplan-consulting.de

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