Impact of technology and digital transformation on Controlling

With the age of digital transformation, digitization, emerging digital business models, and automation are triggered by the evolved modern technology including Artificial Intelligence (AI), data science and analytics, robotics automation, Blockchain and the machinery capacity to absorb and process big data in real-time. All in all, have contributed to the born of new business models, the transformation of existing ones, and to shaping major processes including controlling.  However, the question remains how the development of evolving technology and the continuous digital transformation is impacting the controlling processes and if this impact is efficiently accelerating organizations’ development and improving operations. Similarly, future-driven business models require innovation to survive and remain competitive in which controlling activities play a major role as either a promoter and a driver for innovations, or as a blocker by strengthening controlling processes and placing more governance around it, or it could be a mixed balanced of both approaches. 

Types of controlling

Mainly controlling is divided into two categories strategic controlling and operative controlling (cp. ICV, n.d., n.p.). According to the International Group of Controlling (IGC) strategic controlling covers strategies drafting, strategies examination, enforcement of corrective actions, and continuous monitoring and usually it’s not limited to a time horizon. On the other hand, operative controlling targets formation, planning and management of liquidity, profit, and stability and it’s usually a medium-term process mainly refers to the one-year time horizon (cp. International Group of Controlling IGC, n.d., n.p.).

Contribution of Controlling to the overall organization’s success

Controlling in organizations represents critical function internally and externally. Failures in controlling could “lead to large financial loses, reputation damage, and possibly even organizational failure” (Merchant und Stede, 2007, p. 3). One of the examples is when a company decides and plan to improve its profit margins to the average level for the industry including the effective use of its assets. This improvement relates to the accounting management control and can be implemented by improving sales or by cutting costs, or through both. Successful margins can only be reachable by having effective operations and strategic controlling functions in place (see Van De Poel 1991, p.5). Strategic planning does not only increase the company’s value on the long-run but also it secures the company’s existence (see International Group of Controlling, 2012, p.19). On the other hand, failing to achieve strategic objectives will likely dissatisfy investors and impact their confidence in the management team which could lead to serious implications including markets collapse.  Similarly, in the year 2000 and 2008, the American industry faced major collapses due to continuous corporates mismanagement across many business functions. This gap was mainly caused by a lack of efficiency in controlling processes including corporate governance and operations (see Stimson,  2012, p. 1). Another case related to GE when it “was forced to suspend $4.5 billion as a supplier to the U.S. government in 1985”. Internal measures have been taken by CEO Jack Welch by “strengthening internal controls” as a response to the incident (see Simons, 1995, n.p.).

Figure 1: controlling activity in the company’s process map

To elaborate more, the table below (see Table 1: Objectives of main controlling processes) delivers the list of main objectives behind each of the main controlling processes.

Table 1: Objectives of main controlling processes

Controlling Process	Objectives
Strategic Planning	Design and setup and process strategy design and analysis assessment and adjustment of the vision, mission, values, and strategic objective assessment and adjustment of underlying business model identify deviation of strategic orientation and provide corrective actions defined strategic targets and measures monitor and evaluate financial strategies including several years financial planning make strategy available and align it to relevant stakeholders Communicate strategy and major objectives with different corporate management levels monitoring strategy execution
Operational Planning and Budgeting	Design and setup the process plan and communicate top-down targets define individual plans and budgets for different corporate functions and units create a master plan combining individual pleasure planning results and adjusting plans when required design and approve plans
Forecasting	Design and setup the process design and implement a data model for forecasting compared planned budged and forecasts with results and analyze variancesFormulate countermeasuresApprove forecasts
Cost Accounting	Create and update master data catalogueConduct cost type and cost centre accounting including cost allocationConduct preliminary cost calculation for offers, orders and plans calculate actual and undergoing costs calculate results for defined periodsDraw-up accounts for the period in cost accountingAnalyze variances
Management Reporting	Design and setup the process maintaining reporting and data systems create and make reports availableDefine and create variance analysis reportsEvaluate management performance and initiate measures
Project and Investment Controlling	Design and setup the process plan projects and investments (i.e. feasibility, profitability, business cases, etc.)Support approval process Report on projects and investments create documents support decision-making on project and investments create and discuss a project or investment reports after delivery
Risk Management	Design and setup the process identify, classify, analyze, and evaluate risks assess the impact of risks on the overall strategy, plan, project, or investment suggest and implement risk measures and support in mitigation plans report on risks
Function Controlling (Group, R&D, Production, Sales controlling, etc.)	Plan and set up strategic R&D planning create operational plan for implementation and monitor R&D budget Run R&D cost accounting Evaluate R&D initiatives and projectsCoordinate R&D subsidiesReport on R&D results and milestones
Management Support	Support and optimize decision processes“Actively” take corrective actions in terms of “result/cost” management analyze and optimize processes initiate management projects (organization, restructuring, etc.)Actively promote corporate and cross-functional business know-how within the organization
Enhancement of Organisation, Processes, Instruments and Systems	Examine continuously the quality of control. Provide and follow-up on improvement measuresDevelop controlling processes organize variously interlinked controlling levels “centralized and decentralized” Create and maintain documentation, guidelines, and standards for controlling dept new instruments for successful management controls

Source: Own table based on International Group of Controlling, 2012, p. 20-40.

Impact of technology and digital transformation on controlling

Impact on information controlling

No doubt that controlling processes as seen in “Table 1: Objectives of main controlling processes” rely in the first place on the information, results, and measures. The speed and availability of these inputs provide the basic into processes. However, in order to understand, predict, regulate and control, information must be correctly collated, sorted, categorized, matched, profiled, and modelled so that information can serve the most value for controlling (see Paine, 2015, p.40). On the contrast, the mass of information’s flow a company is facing has been accelerated by digital transformation on all levels. That makes controlling activities within large organizations more complex. The challenge lies in extracting and processing the information as accurate as possible to serve to take optimal decisions, not only to accelerate time-to-market reactions, but also to predict potential future services, products, and market trends so that organizations can remain competitive.  Recihmann (2012) argues that “an efficient company Controlling can only be realised if its formal mathematical and methodical basis is taken into account and is applied to the fundamental ideas of a Controlling concept” (Reichmann, 2012, p.3). Similarly, [corporate] analytic methodologies, the tools and techniques in use in order to extract, and process data are all interlinked and have major “economic” implications in which corporates are strongly becoming data-driven future (see Paine, 2015, p.5). Accordingly, from the author’s point of view, these implications are more visible in controlling processes which require qualifiable and accurate quantitative data (e.g. financial figures, sales, performance, etc.) in order to drive solid decisions, identify deviation of strategic orientation (e.g. evaluation of products or services), monitor and evaluate financial strategies, or even provide automatic forecasts based on historical performance and data.

Real-time data flow and availability

Many areas in controlling are critical for an organization to remain competitive such as sales reporting and forecasts where “the speed of data creation is even more important than the volume.” Additionally, the near real-time availability of information allows organizations to be more agile (see McAfee and Brynjolfsson 2012, n.p.) and lean in terms of mix planning, sales ordering and forecasting, as well as production scheduling (see Iyer, Seshadri und Vasher, 2009, p.17). That becomes more relevant in the controlling processes where corrective measures could be taken immediately to avoid potential loss such as in calculating actual and undergoing costs, monitoring financial results, analyze accounting variances, and manage risks (see Table 1: Objectives of main controlling processes).

Data, analytics capacity and efficiency

Big Data and data science are providing dynamic strategic managerial capabilities empowered by data analytics models and concepts. On the same, strategizing mainly rely on a combination of planning and analytical methods (quantitative and qualitative). However, to outline complex strategic challenges, managers need to develop their abilities to “integrate strategic concepts with analytical tools” (see Kunc, 2019, p.45). Wrong practices on data analytics and data integrity can only be avoided by proper adapted and maybe “digital” controlling processes in place. Likewise, falsified data and analytics could harm strategies and cause major losses to an organization. In one of the examples, “a retail company had been making inventory and mark-down decisions based on the falsified data, a practice that resulted in significant losses.” ( Simons 1995, n.p.). In 2016, the International Journal of Production Economics has launched a study discussing the impact of data analytics on performance and business strategy performance and alignment including controlling. The study discussed how big data analytics capability impact three primary dimensions: (1) management, (2) technology, and (3) talent capability. As a result, big data analytics capability showed a very major impact on major “subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge).” (see Akter u. a., 2016, p.35). Therefore, companies can leverage their analytics capabilities to cover customer and operational data in order to serve some major controlling processes such as fraud and compliance, security breach, financial metrics and forecasts, behavioural KPIs to detect market trends, and last but not least performance measurements (see World Economic Forum, 2016, p.5). Likewise, leveraging the usage of data analysis is being used by large companies like Google to “drive innovation” and to “track trends in ad pricing” (see HarvardX, 2019, n.p.). Hence Google’s controlling processes have been adapted to be aligned with its operational model by covering “three basic control methods: feedback control, concurrent control and feedforward control” (see Djajali, 2014, n.p.).

Ad hoc and data-driven decisions

Decision-making processes are becoming more complex and critical in today’s world due to economic dynamics and agility.  Intuitive and individual approaches do not promote scientific approaches compared to what today’s technology advent can offer through business intelligence (see Figure 6: Impact of a business intelligence system). Systems with the help of Artificial Intelligence (AI) are delivering complex analysis using advanced analytical methodologies and mathematical data models  (see Vercellis, 2009, p.1-6) in which data is the main driver for effective and timely decisions.

Figure 6: Impact of a business intelligence system on the decision-making process

Additionally, ad hoc decisions support in decision-making processes for problems or opportunities that are not usually in prospect or repeated such as in services and products development, merger offers, impact of new legislation, price changes, or new technology (see, Donovan, Madnick, 1977, p. 81). These decisions are timely critical when addressed as “the decision maker’s perception of the problem and even the inherent nature of the problem may change during the process (Pounds, 1969 cited in Donovan, Madnick, 1977, p. 81)”. A good example is PTC Therapeutics – a US pharmaceutical company – which has created a digital “pooling functions” system that allocates resources “on-demand” to “drive” decision-making process based on the predefined scope (see Fiore, West, Segnalini, 2019, n.p.). Since decisions are being more and more digital-based, controlling processes have to adapt to guarantee an effective end-to-end process.

On the contrary, there are some drawbacks driven by digital transformation on the decision-making process. L. Alter (1976) argued that decision support systems are mainly developed by non-managers who has limited knowledge on their usage within an organization and the success or failure of digital systems depends mainly on how managers do use them for the purpose of reaching the most effective and scales within an organization (see Alter, 1976, n.p.).

Bibliography

Akter, Shahriar, Samuel Fosso Wamba, Angappa Gunasekaran, Rameshwar Dubey und Stephen J. Childe. 2016. How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics 182, Nr. October 2017: 113–131. doi:10.1016/j.ijpe.2016.08.018, http://dx.doi.org/10.1016/j.ijpe.2016.08.018.

Assen, M.F. van, E.W. Hans und S.L. van de Velde. An agile planning and control framework.

Dikert, Kim, Maria Paasivaara und Casper Lassenius. 2016. Challenges and success factors for large-scale agile transformations: A systematic literature review. Journal of Systems and Software 119: 87–108. doi:10.1016/j.jss.2016.06.013, http://dx.doi.org/10.1016/j.jss.2016.06.013.

Donovan, John J. und Stuart E. Madnick. 1977. Institutional and ad hoc DSS and their effective use. ACM SIGMIS Database 8, Nr. 3: 79–88. doi:10.1145/1017583.1017599, .

E.U. Strelnik, D.S. Usanova I.G. Khairullin, G.I. Shafigullina, K.T. Khairullina. 2017. Key Performance Indicators in Internal Control.

Fiore, Alessandro Di, Kendra West und Andrea Segnalini. 2019. Why Science-Driven Companies Should Use Agile. https://hbr.org/2019/11/why-science-driven-companies-should-use-agile (zugegriffen: 24. Februar 2020).

Forum, World Economic. 2016. Digital Transformation of Industries – WEF WhitePaper. WEF White Paper, Nr. January: 4.

Gruppe, Haufe. 2012. Controlling Process KPIs A Guideline for Measuring Performance in Controlling Processes. http://dnb.ddb.de.

HarvardX. 2019. Data Science. https://www.edx.org/professional-certificate/harvardx-data-science (zugegriffen: 25. Februar 2020).

Hill, Arthur V. 2012. The Encyclopedia of Operations Management.

Hill, Roberta, Phillip Capper, Ken Wilson, Richard Whatman und Karen Wong. 2007. Workplace learning in the New Zealand apple industry network. Journal of Workplace Learning 19, Nr. 6: 359–376. doi:10.1108/13665620710777101, .

Hughes, Thomas A. 1899. Measurement and Control Basics Fifth Edition.

International Association of Controllers, International Performance Research Institute, Horvárth und Partners. 2018. Controlling in the Digital Transformation. International Association of Controllers: 1–50.

International Group of Controlling. 2012. Controlling Process Model.

Iyer, A, S Seshadri und R. Vasher. 2009. Toyota Supply Chain Management: A Strategic Approach to the Principles of Toyota’s Renowned System. International Journal of Value Chain Management. Bd. 3. doi:10.1504/IJVCM.2009.028603, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:toyota+SUPPLY+CHAIN+MANAGEMENT#1%5Cnhttp://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Toyota+supply+chain+management%231%5Cnhttp://books.google.com/books?hl=en&lr=&id=JY5wqd.

Kunc, Martin. 2019. Strategy Analytics: Integrating Management. Strategic Analytics. doi:10.1002/9781119519638, .

Kunreuther, Howard. 2019. The Future of Risk Management. https://books.google.de/books?id=G3SdDwAAQBAJ&printsec=frontcover&dq=risk+management+impact+of+digital+transformation&hl=en&sa=X&ved=0ahUKEwjSto-Q8fHnAhUR_aQKHaOLAlkQ6AEIMTAB#v=onepage&q=risk management&f=false (zugegriffen: 27. Februar 2020).

LeMay, Matt. 2019. Agile for Everybody.

Liermann, Volker und Claus Stegmann. 2019. The Impact of Digital Transformation and FinTech on the Finance Professional. The Impact of Digital Transformation and FinTech on the Finance Professional. doi:10.1007/978-3-030-23719-6, https://books.google.de/books?id=rZGzDwAAQBAJ&printsec=frontcover&dq=risk+management+impact+of+digital+transformation&hl=en&sa=X&ved=0ahUKEwjSto-Q8fHnAhUR_aQKHaOLAlkQ6AEIKTAA#v=onepage&q=risk management&f=false (zugegriffen: 27. Februar 2020).

Merchant, Kenneth A. und Wim A. Van der Stede. 2007. Management Control Systems: Performance measurement, evaluation and incentives. Pearson Education Limited Edinburgh. Bd. 20. doi:10.1111/j.1467-9310.1979.tb00011.x, .

Nolan, Richard und Larry Bennigson. 2010. Information technology consulting. Management Consulting Today and Tomorrow: Perspectives and Advice from 27 Leading World Experts: 75–100. doi:10.4324/9780203875070, .

Paine, Drew. 2015. Book Review The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences. Computer Supported Cooperative Work (CSCW) 24, Nr. 4: 385–388. doi:10.1007/s10606-015-9220-y, .

Pappas, Ilias O., Patrick Mikalef, Yogesh K. Dwived, Letizia Jaccheri, John Krogstie und Matti Mäntymäki. 2019. Digital Transformation for a Sustainable Society in the 21st Century. https://books.google.de/books?id=20yuDwAAQBAJ&pg=PA706&dq=risk+management+impact+of+digital+transformation&hl=en&sa=X&ved=0ahUKEwjSto-Q8fHnAhUR_aQKHaOLAlkQ6AEIUzAF#v=onepage&q=risk management impact of digital transformation&f=false (zugegriffen: 27. Februar 2020).

Van De Poel, Jan. 1991. Management accounting. Maandblad Voor Accountancy en Bedrijfseconomie. Bd. 65. doi:10.5117/mab.65.17392, .

Polke, M. 2007. Process Control Engineering. Process Control Engineering. doi:10.1002/9783527615735, .

Reichmann, Thomas. 2012. Controlling: Concepts of Management Control, Controllership, and Ratios. https://books.google.com/books?id=1ab1CAAAQBAJ&pgis=1.

Scardovi, Claudio. 2017. Digital Transformation in Financial Services. https://books.google.de/books?id=tFU0DwAAQBAJ&pg=PA57&dq=risk+management+impact+of+digital+transformation&hl=en&sa=X&ved=0ahUKEwjSto-Q8fHnAhUR_aQKHaOLAlkQ6AEIQjAD#v=snippet&q=risk management&f=false (zugegriffen: 27. Februar 2020).

Simerson, B. Keith. 2012. Strategic planning: a practical guide to strategy formulation and execution. Choice Reviews Online. Bd. 49. doi:10.5860/choice.49-2776, .

Sljivic, Slavoljub, Srdjan Skorup und Predrag Vukadinovic. 2015. Management control in modern organizations. International Review, Nr. 3–4: 39–49. doi:10.5937/intrev1504039s, .

Souza, Francisco und Homem De Mello. 2019. OKRs , From Mission to Metrics.

Stimson, William A. 2012. Corporate Management, A plan for Integration of Governance and Operations.

Turner, Chris J., Ashutosh Tiwari, Richard Olaiya und Yuchun Xu. 2012. Process mining: From theory to practice. Business Process Management Journal. Juni. doi:10.1108/14637151211232669, .

VAILLANT, GEORGE E. 2002. Quantum Change: When Epiphanies and Sudden Insights Transform Ordinary Lives. American Journal of Psychiatry 159, Nr. 9: 1620–1621. doi:10.1176/appi.ajp.159.9.1620, .

Vercellis, Carlo. 2009. Business Intelligence: Data Mining and Optimization for Decision Making. Business Intelligence: Data Mining and Optimization for Decision Making. doi:10.1002/9780470753866, .

WILLIAMS, KEVAN. 2009. Strategic Management.

Internet Sources

Alter, Steven L. 1976. How Effective Managers Use Information Systems. https://hbr.org/1976/11/how-effective-managers-use-information-systems (zugegriffen: 25. Februar 2020).

C.J.M. Hattink MSc CISA. 2016. Being in Control with Agile. https://www.compact.nl/articles/being-in-control-with-agile/#ref (zugegriffen: 26. Februar 2020).

Djajali, Alfiando. 2014. Google Inc. – Controlling. https://mcd1020google.weebly.com/controlling (zugegriffen: 25. Februar 2020).

ICV. What is controlling? https://www.icv-controlling.com/en/about-controlling/what-is-controlling.html (zugegriffen: 4. Januar 2020).

International Group of Controlling IGC. Controlling Wiki. https://www.controlling-wiki.com/en/index.php/International_Group_of_Controlling_IGC (zugegriffen: 4. Januar 2020).

Isa200. 2012. International Standard on Auditing 200. Handbook of International Quality Control, Auditing Review, Other Assurance, and Related Services Pronouncements: 72–100. http://www.ifac.org/sites/default/files/publications/files/2012 IAASB Handbook Part I_Web.pdf.

Panditi, Surya, SVP und GM. 2018. Survey Data Shows That Many Companies Are Still Not Truly Agile. https://hbr.org/sponsored/2018/03/survey-data-shows-that-many-companies-are-still-not-truly-agile (zugegriffen: 26. Februar 2020).

Santos, Gleison, 2019,  Combining GQM+Strategies and OKR, https://www.researchgate.net/publication/336590987_Combining_GQMStrategies_and_OKR_-Preliminary_Results_from_a_Participative_Case_Study_in_Industry, (zugegriffen: on 12. Februar 2020).

Simons, Robert. 1995. Control in an Age of Empowerment. https://hbr.org/1995/03/control-in-an-age-of-empowerment (zugegriffen: 24. Februar 2020).

Strategy, Clearpoint. OKRs Vs. KPIs: Breaking Down The Difference. https://www.clearpointstrategy.com/okrs-vs-kpis/ (zugegriffen: 27. Februar 2020).

McAfee, Andrew und Erik Brynjolfsson. 2012. Big Data: The Management Revolution. https://hbr.org/2012/10/big-data-the-management-revolution (zugegriffen: 24. Februar 2020).

McGinn, Daniel. 2018. How VC John Doerr Sets (and Achieves) Goals. https://hbr.org/2018/05/how-vc-john-doerr-sets-and-achieves-goals (zugegriffen: 27. Februar 2020).

Messer, Steve. 2019. How we use OKRs on GOV.UK. https://medium.com/@stevenjmesser/how-we-use-okrs-on-gov-uk-1a8ac8be7eba (zugegriffen: 27. Februar 2020).

Trinkenreich, Bianca, Gleison Santos, Monalessa Perini Barcellos und Tayana Conte. 2019. Combining GQM+Strategies and OKR -Preliminary Results from a Participative Case Study in Industry. https://www.researchgate.net/publication/336590987_Combining_GQMStrategies_and_OKR_-Preliminary_Results_from_a_Participative_Case_Study_in_Industry (zugegriffen: 21. Februar 2020).

World Economic Forum. 2016. Alternative Investments 2020 – The future of capital for entrepreneurs and SMEs, Nr. February. http://www3.weforum.org/docs/WEF_AI_FUTURE.pdf.

Zeng, Ming. 2018. Everything Alibaba Does Differently — and Better. https://hbr.org/2018/09/alibaba-and-the-future-of-business (zugegriffen: 26. Februar 2020).