Controlling in Agile organizations

Organizations are striving to secure a future competitive advantage through (1) innovative design of services and products impacted by the role of digital transformation which is rapidly transforming industries, (2) organization’s capability of being lean and agile to respond to market trends and plan in a more accurate way, and (3) the efficiency of data and analytics usage to lean and predict results (See World Economic Forum, 2016, p.9). However, that doesn’t happen by only adopting new technologies and methodologies, rather than it requires that strategic planning and controlling should be done in different ways than how it was traditionally done (see Figure 7: Strategic management process). That includes serious considerations of external factors (e.g. technology, market trends, competition, innovative business models, etc.) and internal factors (e.g. compliance, vision, mission, product and digital leadership, operational excellence, technology know-how, strategic management, skills, investments, etc.) (see Simerson, 2012, p.105).

Figure 7: Strategic management process

Let’s take for example adoption or implementation of a new Enterprise resource planning (ERP) system within a global organization. Since these systems touch both control and productivity as well as processes automation, hence its successful implementation would imply “extensive redesign of planning and decision processes as well as resolving changes in management philosophy, approaches to empowerment and communication patterns”. (Nolan und Bennigson, 2010, p.14). However, Zeng (2018) argues that adopting or implementing new technology or software to manage its business is not the key for a smart business as “traditional software makes processes and decision flows more rigid” when replicating the same processes using software without optimizing them (see Zeng, 2018, n.p.). 

Another important technology factor that’s impacting the way planning and controlling are being done is the rise of “Process Mining Automation”. It has become a very popular and critical to measure real-time processes efficiency as it analyzes business process performance to achieve real process improvement that’s including “Business Process Intelligence (BPI), Business Activity Monitoring (BAM), Business Process Management (BPM), Business Process Analysis (BPA), Automated Business Process Discovery (ABPD) and Workflow Mining” (Alves de Medeiros et al., 2004, Chang, 2006, van der Aalst et al. 2007, Gartner 2008, Schimm, 2004, cited in Turner u. a., 2012, p.1). On the contrary of what’s been mentioned by Polke (2007) on the role of humans “to gain the maximum information about the process and intervene in it directly” (see Polke, 2007, p.2), digital systems capability is changing this interference in processes, in which systems can to a certain extent “automatically” identify the bottlenecks and deliver corrective actions in real-time based on predefined criteria and algorithms using big data and analytics models.

Impact of Digital Transformation on operations controlling

Objectives and Key Results (OKRs), Agile and Lean Methodologies, Operational Excellence, Design Thinking, Digital Leadership, Digital Transformation and other modern practices are not just applied to the information technology (IT) sector, but also “play a crucial role in delivering the right products and services, accelerating decision-making and speed to market, while also improving the customer experience and staying ahead of the competition.” (see Panditi, SVP, GM, 2018. n.p.).  On the same hand, manufacturing is one of the major industries transforming to Agile to cover important “key strategies include commonality, lean thinking, modular design, postponement, setup time reduction, and virtual organizations” (see Hill, 2012, p.24). The “market pull” and the need for “shorter product lifecycles” is stressing to having an agile operational strategy in place, in which “agile controlling” (see Figure 8: Decentral planning and controlsystems in manufacturing) represents a key success factor for a successful Agile operations model (see Assen, Hans und Velde, n.d, p. 2).

Figure 8: Decentral planning and control systems in manufacturing

Additionally, in Agile organizations, the results become more obvious when the “agile philosophy” is considered as a “set of [flexible] operational rules rather than as a movement guided by principles and values” (LeMay, 2019, p.28). It’s even more adopted by organizations that are looking to improve their performance, be innovative and future-oriented (see Dikert, Paasivaara, Lassenius, 2016, p.87). As an example, a German chemical company has been recently adopted the Agile approaches which enabled it to produce “rapid iterations” in its R&D processes which in its turn increased its productivity by 20%  (see Fiore, West und Segnalini, 2019, n.p.)

On the other hand, being agile means being a short-term focus in operations. That includes planning and delivering results within very short timeframes in an environment where autonomy is given to teams to make “tactical” self-decisions. However, that might introduce some other risks such as “neglecting or even steering away from long-term (strategic) objectives” (M. Drury, K. Conboy and K. Power, 2012, pp. 1239-1254 cited in C.J.M. Hattink MSc CISA, 2016). Therefore, Agile operations cannot be successful without having an aligned controlling process and framework in place to secure operation which requires “educating stakeholders and reviewing contracting practices” (Boehm and Turner, 2005). Similar to Agile operations, large and global organizations including Google and Amazon have adopted the Objectives and Key Results (OKR) framework (see McGinn, 2018, n.p.) even in some public sector areas such as GOV.UK (see Messer, 2019, n.p.). OKR framework has proved its efficiency in terms of linking company strategic objectives to key results that are measured monthly or quarterly based on a continuous feedback loop and crafted to fulfil the overall company strategy (see Clearpoint Strategy, n.d., n.p.). However, the challenge remains in finding the best approach to do controlling in agile and dynamic organizations without slowing down innovations, creativity, and future-oriented visions (see C.J.M. Hattink MSc CISA, 2016, n.p.).

Impact of Agile on risk management controlling

For an effective internal and external organizational objective, reliable risk-management controls and processes are critical for the success of the organization. In organizations, there are avoidable or mitigate risks which can be planned and/or avoided by having controlling processes in place such as bribery, legal and compliance, operations, technology, strategic risks, etc. and those which are beyond organization control such as macroeconomic forces touching the whole economy or country such as political changes, new regulations, or financial crisis (see WILLIAMS, 2009, p.20-22). Strategic risks management focus mainly on the legal side of operation (e.g. insurance, taxes, regulatory issues) as well as on strategic risks, those which could impact the whole business (e.g. market changes, products, services, competition, etc.) (see Kunc 2019, p.283). Additionally, risk management strategies in today’s world require on the contrary of the traditional approach a various number of “disciplines” including “finance, history, insurance, marketing, political science, psychology, sociology, and the decision sciences” have to work together to have an efficient risk management approach (see Kunreuther, 2019, p.2) in which scenario-based risk modelling and risk intelligence capabilities (see Figure 9: Risk management evolution) are required in order to project and interact between planned transactions, market data and model parameters (see Liermann und Stegmann, 2019, p.135).  

Figure 9: Risk management evolution

As a result, risk management methodologies and techniques are evolving to be more proactive and predictive approach instead of reactive management approach empowered by quantifiable data and statistical analysis driven by the capability of machine learnings and digital transformation (see Scardovi, 2017, p.145). Another similar driver impacting risk management methodologies is the shift in organizations’ architecture design. That includes the transition in speed, flexibility, integration and innovation in order to be lean and agile which are considered as key success factors (see Pappas u. a. 2019, p. 707,708). Therefore, the transformation of organizational structure is impacting all kind of controls including risk management as a key backbone for successful, sustainable and trustful organizations.

Future prospects of controlling

Dynamic managerial capabilities, task dimension, cognitive dimension, behavior dimension, integrating management science and strategic management, data modeling, behavior with and beyond models, modeling systems, big data analytics capabilities are all transforming the future of strategizing, operations and business models. While controlling is critical for successful organizations, digital business model innovations require crucial adjacency of controlling activities to be aligned.  That can be seen as a two-ways relationship (see Figure 10: Controlling relationship to digital transformation) in which, on one side controlling activities have to promote the development of innovation to secure organization’s future prospect, and on the other side and in order to secure this success, the implementation of innovations and innovative business models will carry out the required adaptation of controlling processes and methods to include participation on various levels including “the overall innovation process, covering the phases of brainstorming, assessing ideas, implementation and ongoing operations” (see International Association of Controllers u. a. 2018, p.24).

Figure 10: Controlling relationship to digital transformation

On the contrary of the old ways of controlling which more focused on the accounting and financial performance side, modern organizations require new approaches of controlling that involve different business functions and stakeholders on a continuous basis. That’s especially substantial for future business models and innovative products and services (see Sljivic, Skorup und Vukadinovic, 2015, p.2). To achieve that, it is required to have a strong data-driven framework to (1) act systematically and collaboratively as a connector between the controller and the different stakeholders, and (2) to be driven by data analytics, machine learning, and Robotic Process Automation (RPA) as “Input” for the controllers and controlling process, in which an “Output” of results including actual and predictive controlling measures can be produced as an outcome in order to regulate and control efficiently (see Paine, 2015, p.40). That can only be achieved by delivering information transparently and in real-time which in its turn could be leading to making decision-making at all levels of the organization easier and providing greater transparency and contribute to the overall success of the organization.

In essence, and as can be seen, there is a need to have new controlling frameworks in place, that should be dynamically accommodating the agility of organizations and be continuously customized in order to walk with the organizational changes driven mainly by digital transformation and technology enablers. However, the challenge remains in filling-in the gap between the theoretical aspect of controlling and the continuously fast-pace organizational structure in terms without slowing down growth, innovation, and creativity which are required to secure competitive advantage. The same challenge has been discussed in the past by Simons (1995) when he mentions that the “problem facing managers in the 1990s is how to exercise adequate control in organizations that demand flexibility, innovation, and creativity.” (Simons, 1995, n.p.). Because of these gaps and challenges, the implementation of the new controlling frameworks will be a bit slow especially with standards and regulations are being as well in a steady change to cover new aspects such as General Data Protection Regulation (GDPR) and other data-compliance aspects. Another challenge hitting the aspect of future controlling, is the strategic management approach which is still seen as “dissociated” from the use of the digital and technology capabilities caused by the absence of a clear strategy for data management and information systems (see Kunc, 2019, p.28-30) which is considered as the “biggest barrier to digital maturity” (see VAILLANT, 2002, p.5). As a conclusion, since digital and technology transformation a has direct impact on costs, productivity, customer service, decision-making, innovation, future business models, etc. we see as well a direct impact on how controlling will be made in the future to cover an end-to-end efficient controlling life-cycle.

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