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Sales management with the help of value driver trees

Sales Management with the help of value driver trees

The challenge

Achieving a transparent sales management across different business units continues to be a major challenge for many companies. This blog article offers a possible approach and details the selected procedure for it.

The company presented in this example operates internationally in the field of complex lighting solution projects, its national subsidiaries have great independence and the individual branches and offices are mostly self-sufficient.

Therefore, the idea was to develop a system that, on one hand, would provide more transparency in sales activities, success rates, success drivers and processes. On the other hand, the company operates in a highly competitive market. The business mainly consists of project work in which many participants are involved: architects, lighting planners, property developers, building owners, craftsmen and official institutions. So, in many cases you don’t know “the end customer”. For example, it has not yet been possible to measure customer profitability.

Due to the complexity of the lighting solution projects, it is crucial for the company to push only those projects that will actually be implemented. When the hit rate is high, the sales department works effectively.

Long project durations will make traceability even more difficult. Long after the order has been booked, employees of the company and their partners – often from different national subsidiaries or offices – are still busy implementing projects. The invoicing then takes place with a corresponding delay. How is the performance of the sales department to be controlled considering the many different participants and times at which services were provided that are necessary for success? Everyone works on a project at a certain percentage and in a different phase.

In this case, performance in sales does not only mean measuring profitability based on incoming orders. The subsequent processes and work – i.e. the traces that an employee leaves in the system – should also be taken into account when determining performance. This is because, often, credit notes are granted, or complaints processed during implementation, and even payment defaults may occur.

The solution

In the early phase of the project, a sophisticated technical concept emerged, in which the nature of the presentations, analyses and reports was described in detail. There was a very clear idea of ​​what the system should do later. Particularly important was the ability to capture performance at a glance. How good is Munich compared to Berlin, how successful are teams, what explains the differences in EBIT, why do some products achieve higher margins than others? What was the overhead cost and what is it that participants can learn to be even better on the next project?

In order to answer these questions, it had to be possible to consolidate key figures and compare performance directly at various levels: national company, region, branch, from the team to the individual employee.

The foundation for the technical part is the concept of Value Driver Trees. In a nutshell: with the help of a hierarchical structure, it is possible to analyse very precisely which levers need to be moved in order to be even more successful. A value driver tree can be used to identify dependencies on which adequate measures can be based, for example to better exploit customer potential.

Therefore 2 examples:

Example 1: From Quotation to Order

From-quotation-to-order-value-driver-trees

Each element of the driver tree always contains 4 values: minimum, maximum, average and the value of the selected organizational unit in relation to the unit to which it is compared. The green bar emphasizes that the selected organizational unit has a better performance than the average of the comparison units, the red bar indicates that the organizational unit is worse than the average.

The selection is made for: a specific period, the unit to be considered and the unit to be compared.

From the individual sales employee to the sales office, region, national subsidiary and the entire company, all values can be compared with each other.

Example 2: EBIT Analyse

EBIT Analysis value drivers trees

A special feature of the EBIT analysis is the consideration of wholesale and net sales. Gross sales are the sales invoiced in SD, while net sales also include the actual incoming payments, bad debt losses, discounts granted subsequently, etc. in the calculation.

The evaluations were presented in the enterprise portal using a JAVA application developed especially for this purpose, which displays the data of the underlying OLAP query results of the BW server. The data was transferred from the BW server to the enterprise portal using XML, which was generated by a specially developed function module on the BW server and then transferred to the portal.

The procedure

As part of the project procedure, an in-depth analysis of the technical requirements was initially carried out together with the department involved in the process.

The characteristics and key figures in the technical requirements were broken down into a set of more than 100 basic key figures and characteristic structures, which was verified together with the IT staff of the company and compared with existing data structures. Among other things, these basic key figures and feature structures formed the basis for the elements of the driver tree structures.

This was followed by the parallel development of the info providers and transformations in the BW backend system as well as the frontend JAVA application for the enterprise portal. The required key figures were largely pre-calculated to minimize OLAP runtimes. At the same time, the corresponding frontend application was designed.

After extensive tests and data verification, the application was handed over to the department and IT. As part of the Post Go Live Support, last errors were eliminated, and various optimizations were carried out.

If you have any questions regarding this topic, please do not hesitate to contact us. We at Inspiricon are looking forward to hearing from you!

Sources of the images: SAP SE

Author
Oskar Glaser Lead Consultant BI Reporting
Phone: +49 (0) 7031 714 660 0
Email: info@inspiricon.de

Inspiricon keeps growing!

From 1st February 2018, you can also find uns in our new office in Freiburg, Schwarzwaldstraße 78b.

Close proximity to the market and our clients are of high importance for us – thus, we establish the new office in Freiburg.

Our consultants work not only with the classical SAP BI topics, but mainly with Big Data, Machine Learning and new SAP technologies.

We are looking forward to exciting new projects!

Inspiricon Office Freiburg

Author
Linda Schumacher Marketing
Phone: +49 (0) 7031 714 660 0
Email: info@inspiricon.de
Welcome to the World of Predictive Analytics

Welcome to the World of Predictive Analytics

Hello and welcome to our first article of the Predictive Analytics blog series!

Let’s start with two definitions that you will have to understand in order to successfully navigate through the articles:

  1. Predictive analyticsencompasses a variety of statistical techniques from predictive modellingmachine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
  2. Machine learning is a continuous process where algorithms and models learn continuously to adjust the perception (the way we take in information) and processing (how we deal with information).

This Is What You Can Expect From Our Blog Series

  • The current article will serve as an introductory article, it is basically the big picture. Its goal is to give you an insight on the middle ground between predictive analytics, machine learning and business strategy, technology and company culture.
  • In the second article, we will present a predictive model developed in SAP Predictive Analytics.
  • In the third article we will go a bit deeper theoretical- and technical-wise as well. The technical level required will increase with each article.

By taking a look into the business background we see that business leaders who keep their digital strategies updated in the face of ever-evolving technologies can help ensure that their workforce remains focused on future innovation and improves business performance. By placing strategic bets on disruptive technologies, decision makers can upend the market landscape by creating entirely new products, services, or business models while shifting value between producers or to consumers.

Looking in the rearview mirror may help keep the lights on, but it is very limiting when it comes to success and growth. Once a competitor moves into a market domain by adopting emerging technology, the rest of the marketplace risks losing their position if they are not digitally maturing at the same rate.

Strategic Vision and Data Driven Decision-Making

While most decision makers are not data scientists, they are indeed beginning to understand the power of their data. By adopting machine learning, big data, advanced and predictive analytics, small and mid-sized businesses are further capitalizing on their speed and agility to serve customers better, deflect or embrace market disruption, and innovate their way to market leadership.

The fact is, there is a lot of data available these days. Companies have invested billions in big expense items like enterprise resource planning (ERP), operational technology from plant equipment, point-of-service (POS) systems, and information services. All this data does not necessarily mean it is put to good use.

While the success of Machine Learning and Predictive Analytics is very well documented and high regarded in areas such as marketing and consumer behavior, this is only a fraction of its possible applicability.

Quite a lot of companies these days still plan their revenues (part of their financial forecast) by manual input, based on personal experience – more or less on ‘gut feeling’. Maybe with a fudge factor applied afterwards which still as you can imagine is never accurate. Creating and considering a predictive model based on this scenario can give a significant edge in terms of planning accuracy.

Predictive and Machine Learning is a field which is new to most people, but it comes in multiple flavors that require different solutions and business practices so the possibilities of leveraging this advantage are endless.

SAP’s Current Architectural Paradigms on Predictive Analytics

The first thing that comes to mind when training a predictive model on data is the co-location of the data and the algorithms implementation (the predictive modeling engine).

Two scenarios are available:

  1. First, to bring the modeling engine to the data (this is something that the database vendors and the Big Data platform have well understood), including SAP with machine learning libraries such as SAP Predictive Analytics Library and SAP Automated Predictive Library, R and TensorFlow integration under SAP HANA (the two later cases will need tighter integration in the future than what is available now).
  2. The second solution is to bring the data to the modeling engine (and most of the predictive and machine learning environments on the cloud are based on this principle), and this includes the SAP Leonardo machine learning environment. These cloud environments will soon need to be opened to hybrid scenarios in order to make this challenge not a problem but just a design option.

Besides Technology, Company Culture Plays a Very Important Role

Irrespective of how advanced a company’s analytics solutions are, these solutions cannot yield significant benefits unless they are deployed with the right people talent. If all the finance team knows is counting the beans and preparing rear-view mirror dashboards, then all they will do is automate the same old processes on newer technology.

A leader needs to make a concentrated effort to develop, through training and mentorship, the skills around predictive analytics around his team. If the team does not respond to this challenge, then obviously he will need to acquire this talent from someone who does.

In the accelerating pace of business, businesses are thriving to be data driven. To be a data-driven business means to relentlessly measure, monitor, predict, and act on the pulse of the business in a continuous and automated manner. To do so, an organization needs to communicate the value of data across the entire organization, act as catalyst to persuade cultural change to be data driven, and – most importantly – employ and deploy machine learning across the organization.

Technology, data strategy, and organizational culture all need consideration. The most important thing however is to start the journey.

Be sure to come back for our next Predictive Analytics article – follow us on facebook, twitter, XING or LinkedIn to never miss our latest article.

Sources: Wikipedia, http://blog-sap.com

Author
Ovidiu Costea Consultant SAP BI
Phone: +49 (0) 7031 714 660 0
Email: cluj@inspiricon.de
Big Data and Artificial Intelligence

Big data and artificial intelligence – a powerful duo that will shape our future

SAP makes artificial intelligence a reality

Using intelligent machines to analyze big data is no longer just wishful thinking. SAP has turned yesterday’s dreams into today’s reality. Based on SAP HANA, SAP has breathed life into multiple software components capable of thinking autonomously and analyzing vast amounts of data. As you are reading this, its software is recognizing faces and objects and carrying out large-scale analyses that would neither be possible nor conceivable if done manually.

With SAP, artificial intelligence (AI) has made the leap from vision to reality.

SAP Clea and SAP Leonardo: artificial intelligence is gaining momentum

SAP has added a new version to its HANA platform. Along with it, a number of AI and IoT (Internet of Things) services were introduced to the market.
SAP’s new SAP Clea software runs in the SAP cloud and is capable of autonomous learning without requiring any explicit programming. Its analytical intelligence is already being leveraged by companies like Munich Re and the European Space Agency. The insurance giant Munich Re is constantly calculating the risk associated with forest fires using data on vegetation lines. These calculations are supported by intelligent software and their capability for large-scale big data analytics.

SAP Leonardo is the name of SAP’s IoT solution portfolio. Just like its namesake Leonardo da Vinci, SAP Leonardo takes a broad and interdisciplinary approach – a fundamental requirement for the Internet of Things. Information from across the company is taken into consideration, paving the way for the development of novel solutions and business models.

SAP Leonardo is designed to assist potential customers in crafting an IoT strategy and in identifying the solutions that will best meet their specific needs.

Because one thing is for certain: There is no such thing as the one piece of software for IoT and AI. It usually takes a combination of multiple applications.

The digital core: The S/4HANA SAP Enterprise Suite

S/4HANA is already being leveraged by 4,000 companies in 25 countries. It forms the digital core for the transformation and can be used for IoT and AI/machine learning applications. SAP has introduced several additions in the cloud, such as Connected Logistics, Connected Vehicles, Connected Manufacturing, Connected Assets, Connected Retail, and Future Cities. These allow companies to, for example, manage their fleets, control quality levels, and calculate routes.

In the field of artificial intelligence and machine learning services, SAP offers a range of services on the SAP Cloud Platform:

  • Resume Matching to streamline recruiting
  • Cash Application to analyze payment behavior
  • Social Media Customer Service
  • Brand Intelligence
  • Fraud Detection for insurers and banks

SAP Fiori – the new SAP UI

SAP Fiori is an initiative that aims to enhance usability (for more information, please refer to our website and our blog). With Fiori 2.0, SAP wants to harmonize the user experience for all SAP applications and has included a number of improvements in its visual design and usability.

This is yet another area where AI is leveraged. The user is assisted by a co-pilot that anticipates user actions and prepares them accordingly. Its built-in voice control, for example, streamlines maintenance and warehouse workflows. Artificial intelligence is used to analyze suppliers and categorize them according to a predefined requirements profile.

Inspired: straightforward big data analytics

The combination of artificial intelligence and big data supported by SAP Fiori makes for more effective automation and analysis. The software tries to foresee what actions the user wants to take, enhancing effectiveness and boosting speed. SAP Fiori enables companies to conduct large-scale analyses of big data and to automatically monitor important business metrics. Anomalies, trends, and patterns are automatically communicated to the responsible staff in an interface that puts the user first. The available data is analyzed in a central user interface that allows for intuitive operation. There is no longer a need for complex modifications of input and output parameters.

Artificial intelligence ensures that the algorithms deliver meaningful results without requiring input from the business departments.

The main objective of machine learning is to identify data patterns and relationships and to apply them to new sets of data. The underlying algorithms are based on statistics, the calculation of probabilities, and algebra. With SAP Application Intelligence, data is the fuel that powers machine learning.

Deep learning: where machines outperform humans

One discipline of machine learning is known as deep learning: Here, neural networks are flooded with vast amounts of data. The intention is to enable the software to recognize faces, classify objects, and understand language. Capabilities that are constantly being refined and increasingly used in robotics applications.

This also opens up new medical applications:
Soon, the data of individual cancer patients will be compared to millions of medical records to enable the customization of healthcare through precision medicine. Artificial intelligence works for the broad mass and delivers very promising results. The software is increasingly becoming an all-rounder that can be leveraged across the board. SAP Application Intelligence is capable of unearthing relationships that would otherwise stay hidden from the human eye. Working under time constraints, human employees often overlook crucial data that then remains undiscovered in day-to-day business.

Conclusion

Artificial intelligence has already found its way into business intelligence solutions, where it is used to control data analytics, for example in order to detect anomalies or automatically structure and interpret data. On top of that, AI algorithms are also used to monitor data streams.

The intelligent and integrative interaction between new SAP components such as Clea, Leonardo, or Fiori on the basis of SAP HANA Cloud and on-premises solutions are continuously inspiring Inspiricon to search for innovative services for our customers.

You are curious to learn more and explore new potential business? Do not hesitate to contact us now!

Author
Claudio Volk Member of the Management Board
Phone: +49 (0) 7031 714 660 0
Email: info@inspiricon.de