The study has been undertaken to design an Operational Excellence (OE) Strategy Implementation Model for Growth in a VUCA (Volatile, Uncertain, Complex, and Ambiguous) Environment. The study was conducted using Johannesburg Stock Exchange (JSE) listed companies.
To develop an OE strategy implementation model for organisational growth in a VUCA environment.
Illuminating problems such as the prevailing coronavirus disease 2019 (COVID-19) pandemic are impeding OE strategy implementation by JSE listed organisations, suggesting the fact that existing models do not address changes in VUCA environments.
This research collected quantitative data using the questionnaire instrument rolled out through SurveyMonkey to 1000 people working for JSE listed companies implementing Operational Excellence. The data collected from the 430 responses of the sampled 1000 was acceptable according to Krejcie and Morgan (
The study found that OE strategy can only drive growth through proper implementation, maintenance, and improvements utilising management review of best practices, policies, and procedures on key performance metrics such as revenue, profits and return on investment.
Shortcomings of the existing models were interrogated and gaps found in order to design a more suitable model for growth which takes cognisance of the VUCA Environment.
A new model was designed that can be used successfully as a holistic tool in OE to drive growth by practitioners of the industry.
The current world order has been disrupted and reset by the coronavirus disease 2019 (COVID-19) pandemic (Dunford & Qi
The acronym VUCA stands for volatility, uncertainty, complexity, and ambiguity. These characteristics of the environment demand intricate and multivariate systems comprising of resource acquisition, mobilisation and rationalisation to achieve desired results (Mutizwa
Waskvi (2019) posited that the US military coined the term VUCA in the 1990s to describe a myriad of challenges troubling a diagnosis of military situation. However, VUCA recently found its way into the business lexicon (Raghuramapatruni & Kosuri
Definitions of a volatile, uncertain, complex and ambiguous business environment.
Date | Author | VUCA environment definition |
---|---|---|
2016 | Kuznik | A holistic risk system that calls for countermeasures is designed quickly and easily to maintain organisational power to act in the face of greatly reduced reaction times and increased turbulence. The function of risk management must be commensurate to the risk of gravitational requirements. |
2017 | Mutizwa | A network or ecosystem exhibiting harsh external environments that needs the execution of all disciplines with instinct and coherent orientation by leadership to mitigate external exogenous pressures while exerting significant influence on internal dynamics to advance organisational goals. |
2019 | Dombrowski and Henningsen | An ever-changing discontinuous dynamic business environment where organisational growth is affected significantly by events that demand flexibility and versatility in pursuit of strategies to remain abreast of developments. |
2018 | Veldsman and Pauw | An organisational landscape characterised by an ever-changing prevalence where traditional mechanisms and methodologies have become obsolete. Focus shifts towards constructing a holistic adaptable critical coherent approach that allows talent to reinvent more agile and sustainable. |
2019 | Khurana and Singh | A confusing business environment that is constantly changing due to external disturbances or internal unrest. Security in decision-making and choices of actions are illusions. New discovery or development makes the environment more volatile, uncertain, complex, and ambiguous. |
2020 | Rimita, Hoon and Levasseur | A relentless business environment with diversity, intensity, and rapidity changes, which renders existing leadership or operating methods, proves inadequate resulting in hermeneutic phenomenological experiences. |
VUCA, volatile, uncertain, complex and ambiguous.
One can therefore summarise a VUCA environment as a set of interdependent dynamics of PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) which relates together in an intricate, intertwined, or inter-connected ecological system and between systems and their environments.
In a volatile environment, extreme fluctuations characterise the multi-layer of the econometrics (Prensky
Uncertainty is characterised by erosion of predictability of the future based on experience. There is no more confidence in the steadiness of decisions (Raghuramapatruni & Kosuri
While the complex engineering of landing on the moon and spacecraft puzzled the non-scientific world years back, modern science has broken protocols from genetics to disruptive innovation such as robotics and automation to futuristic artificial intelligence. The sheer volume and nature of the problems reflect the complexity. Typical VUCA environment complexity instances are unique tax and regulatory tariffs and cultural expectations associated with monetary policies (Raghuramapatruni & Kosuri
The advent of technology adopted by the younger generation, faster than the elderly, has caused ambiguity as to who teaches who. Prensky (
There is an abundance of academic material published to address the challenges of efficiency, effectiveness, and OE strategy implementation, but very few focus on business performance for growth in the VUCA environment (Mutizwa
Existing OE models are accused of lacking clarity, systematic implementation roadmaps and do not guide the organisations towards growth. They often only state what it takes to achieve OE rather than explain how to do it (Markins & Bolboli 2015). Published OE Strategy Models seem inadequate for organisational growth in a VUCA environment (Cousins
In an endeavour to match and supersede competition, organisations in a VUCA environment are compelled to do better by implementing innovative strategies (Millar, Groth & Mahon
In an increasingly dynamic business environment where change seems to be the only constant feature, organisations have resorted to quality and OE frameworks in their efforts to adapt and obtain competitiveness (Carvalho, Carvalho & Sampaio
Although OE is a critical factor for competitiveness, maximum benefits are obtained when managers decide on the most important processes that add value and develop instruments to sustain those (Carvalho et al.
The genesis of operations improvements, traced back to the 18th century, focused on efficiencies and productivity improvement for customer satisfaction. The architects of the systems introduced scientific management and time and motion studies as methodologies for achieving the set ultimate goals. The early 19th century saw the introduction of the Just in time (JIT), Six Sigma, Total Quality Management (TQM) and lean manufacturing operations improvement systems, based on the Toyota productive systems in Japan. The waste reduction studies at Toyota embraced Kaizen (continuous improvement) processes as the core of the Toyota production system. Further studies by Shingo over a decade culminated in the guiding principles of OE strategy implementation, which is dubbed the Sony (
Operational excellence definitions.
Date | Author | Definition |
---|---|---|
2013 | Crawford | OE is a philosophy of organisational leadership that stresses the application of a variety of principles, systems, and tools toward the sustainable improvement of key performance metrics. OE is said to be a philosophy of: ‘doing things right, in the right way, in the right order, at the right time, consistently’ |
2013 | Oakland | OE is all about having the right capabilities, the right level of competencies, clear business direction, productive processes, and a toolbox of efficient and effective techniques and methodologies. |
2014 | Duggan | OE is the execution of the business strategy more consistently and reliably than competitors do and the achievement of desired results. It entails striving for continuous improvement that leads to customer satisfaction. |
2014 | Shingo | OE is the successful transformation architected by leaders through a culture of continuous improvement. The change processes are built on fundamental principles that govern the values, behaviours, systems, and subsystems embedded in the tools that enable alignment towards desired results. |
2015 | Mitchell | OE is a high performance, success-oriented operation. The operative phrase, of course, is ‘work culture’. Senior executives engage across domains rather than focusing on sales, sales, and sales; the focus is now generating value. Of importance is high-level leadership in areas like human resources, day-to-day operations, and asset integrity. |
OE, Operational Excellence.
There seems to be no one-size-fits-all strategy for achieving OE. Different organisations either adopt or tailor-make and implement models which can deliver their desired goals. Operational Excellence implementation must be justified by results consisting of quantifiable benefits, namely, empirical competitive advantage, customer satisfaction, good employee remuneration, and stakeholders’ value. A close analysis of the discussed models shows that OE success is never an easy journey, albeit there are positive financial and non-financial outcomes (Dahlgaard et al.
Crawford (
Oakland (
Duggan (
The Shingo Institute illustrated five key fundamentals paradigm shifts: OE focuses as a requirement for successful behavioural and culture change, the manifestation of ideal behaviours to influence desired outcomes, strong cultural foundations for the construction of long-term sustainable principles, management systems that positively affect behaviours to align with principles and embracing strategy enablers as Lean, TQM, JIT, and Six-sigma (Sony,
The approach in this research was to view OE as a toolbox of organisational transformation architecture that involves executing work consistently in the right way, in the right order, and in the right time. The execution involves total commitment across domains from top management to cultivate a holistic work culture that governs values and aligned performance metrics for excellence. The leadership style enables operations improvements across all departments, primarily focusing on structural systems, process optimisation, product quality, differentiation, customer care, people engagement, and development for growth (Stoyanova & Iliev
Hruska (
Obama (
This research investigated the impact of key components of OE strategy implementation and the components’ effectiveness in driving growth in a VUCA environment as can be expressed by the following hypotheses:
A survey approach was adopted to collect data in order to construct an OE model fit for the VUCA environment. The empirical data was collected using questionnaires submitted to Johannesburg Stock Exchange (JSE) listed organisations, within the mining and metallurgy, heavy engineering, food and beverages manufacturing, clothing and textile, plastic and chemical engineering manufacturing. This sector was selected due to its focus on OE. One thousand questionnaires were deployed via SurveyMonkey to LinkedIn contacts whose profiles reflected JSE listed organisations. Participants consisted of top, middle, and lower-level employees to enable extraction of OE understanding, awareness, behaviour changes, success and barriers, and anticipations at all organisational levels. Using the sample size determination by Krejcie and Morgan (
The Bentler-Raykov test was used to establish the impact of independent variables on the dependent variable. The correlation tests were used to indicate the linearity of variables in the study. Furthermore, a structural equation modelling (SEM) technique was used to determine structural relationships observed and to identify components of the model. A confirmatory factor analysis (CFA) was conducted to measure how well the latent variables ‘human centricity’, ‘leadership’, ‘competitive advantage’, ‘technological advancements’, ‘systems’ and ‘strategic alignment and fit’ are measured by observed variables. Observed variables are variables contained in the data and described in detail below in the descriptive analysis of the study. Latent variables are combined scores of observed variables and variables which the researcher constructed, based on theory. A structural model was developed to test causal relationships proposed in the constructs section. This model (also referred to as path analysis) considered the way constructs are related to each other.
The structural equation model.
Measurement | Coefficient | Robust std. error | 95% confidence interval |
||
---|---|---|---|---|---|
Lower bound | Upper bound | ||||
Human centricity | 1.00 | (constrained) | - | - | - |
_cons | 3.52 | 0.06 | 0.00 | 3.41 | 3.63 |
Human centricity | 0.94 | 0.12 | 0.0000 | 0.71 | 1.18 |
_cons | 3.62 | 0.05 | 0.00 | 3.51 | 3.72 |
Human centricity | 1.07 | 0.14 | 0.0000 | 0.80 | 1.33 |
Competition | 1.00 | (constrained) | - | - | - |
_cons | 3.82 | 0.05 | 0.00 | 3.72 | 3.91 |
Human centricity | 1.16 | 0.15 | 0.0000 | 0.87 | 1.44 |
Competition | 2.19 | 2.21 | 0.3230 | −2.15 | 6.53 |
_cons | 3.71 | 0.05 | 0.00 | 3.61 | 3.82 |
Human centricity | 1.17 | 0.15 | 0.0000 | 0.87 | 1.47 |
_cons | 3.62 | 0.05 | 0.00 | 3.52 | 3.72 |
Human centricity | 1.19 | 0.14 | 0.0000 | 0.93 | 1.46 |
_cons | 3.68 | 0.05 | 0.00 | 3.58 | 3.78 |
Human centricity | 1.20 | 0.13 | 0.0000 | 0.93 | 1.46 |
_cons | 3.29 | 0.05 | 0.00 | 3.18 | 3.39 |
Human centricity | 1.04 | 0.11 | 0.0000 | 0.81 | 1.26 |
_cons | 3.29 | 0.06 | 0.00 | 3.18 | 3.40 |
Systems | 1.00 | (constrained) | - | - | - |
Leadership | 0.48 | 0.09 | 0.0000 | 0.30 | 0.66 |
_cons | 3.63 | 0.04 | 0.00 | 3.54 | 3.71 |
Systems | 2.09 | 0.48 | 0.0000 | 1.15 | 3.03 |
_cons | 3.64 | 0.05 | 0.00 | 3.54 | 3.73 |
Systems | 2.46 | 0.66 | 0.0000 | 1.16 | 3.75 |
Strategic fit | 1.66 | 0.51 | 0.0010 | 0.67 | 2.66 |
_cons | 3.66 | 0.05 | 0.00 | 3.56 | 3.76 |
Systems | 2.26 | 0.71 | 0.0020 | 0.86 | 3.66 |
Strategic fit | 3.07 | 2.09 | 0.1410 | −1.02 | 7.15 |
_cons | 3.93 | 0.05 | 0.00 | 3.83 | 4.02 |
Systems | 2.36 | 0.59 | 0.0000 | 1.19 | 3.52 |
_cons | 3.62 | 0.05 | 0.00 | 3.53 | 3.72 |
Systems | 2.54 | 0.66 | 0.0000 | 1.23 | 3.84 |
_cons | 3.64 | 0.05 | 0.00 | 3.54 | 3.73 |
Systems | 2.48 | 0.63 | 0.0000 | 1.24 | 3.72 |
_cons | 3.63 | 0.05 | 0.00 | 3.53 | 3.72 |
Systems | 2.67 | 0.70 | 0.0000 | 1.29 | 4.04 |
_cons | 3.62 | 0.05 | 0.00 | 3.52 | 3.73 |
Systems | 2.17 | 0.61 | 0.0000 | 0.97 | 3.36 |
_cons | 3.71 | 0.05 | 0.00 | 3.61 | 3.80 |
Competition | 10.78 | 11.77 | 0.3600 | −12.29 | 33.84 |
_cons | 3.79 | 0.05 | 0.00 | 3.69 | 3.88 |
Competition | 13.31 | 14.77 | 0.3680 | −15.64 | 42.25 |
_cons | 3.88 | 0.05 | 0.00 | 3.79 | 3.98 |
Leadership | 1.00 | (constrained) | - | - | - |
_cons | 3.65 | 0.05 | 0.00 | 3.55 | 3.74 |
Leadership | 1.08 | 0.07 | 0.0000 | 0.94 | 1.21 |
_cons | 3.64 | 0.05 | 0.00 | 3.54 | 3.75 |
Leadership | 1.08 | 0.09 | 0.0000 | 0.90 | 1.26 |
_cons | 3.26 | 0.05 | 0.00 | 3.16 | 3.37 |
Leadership | 1.19 | 0.11 | 0.0000 | 0.97 | 1.40 |
_cons | 3.27 | 0.06 | 0.00 | 3.16 | 3.38 |
Leadership | 1.35 | 0.11 | 0.0000 | 1.14 | 1.57 |
_cons | 3.43 | 0.06 | 0.00 | 3.32 | 3.55 |
Leadership | 1.36 | 0.11 | 0.0000 | 1.15 | 1.57 |
_cons | 3.39 | 0.06 | 0.00 | 3.28 | 3.51 |
Leadership | 0.29 | 0.07 | 0.0000 | 0.16 | 0.42 |
_cons | 4.34 | 0.04 | 0.00 | 4.26 | 4.43 |
Technologic advancements | 1.00 | (constrained) | - | - | - |
_cons | 3.91 | 0.05 | 0.00 | 3.82 | 4.01 |
Technologic advancements | 1.03 | 0.13 | 0.0000 | 0.77 | 1.29 |
_cons | 4.21 | 0.04 | 0.00 | 4.12 | 4.30 |
Technologic advancements | 1.04 | 0.09 | 0.0000 | 0.86 | 1.21 |
_cons | 4.06 | 0.05 | 0.00 | 3.97 | 4.15 |
Technologic advancements | 0.33 | 0.10 | 0.0010 | 0.13 | 0.53 |
Strategic fit | 0.43 | 0.33 | 0.1940 | −0.22 | 1.09 |
_cons | 4.43 | 0.04 | 0.00 | 4.35 | 4.51 |
Strategic fit | 0.00 | 0.46 | 0.9990 | −0.91 | 0.91 |
_cons | 4.01 | 0.04 | 0.00 | 3.92 | 4.09 |
Strategic fit | −0.05 | 0.66 | 0.9350 | −1.36 | 1.25 |
_cons | 3.72 | 0.06 | 0.00 | 3.61 | 3.84 |
Strategic fit | 0.77 | 0.44 | 0.0830 | −0.10 | 1.63 |
_cons | 4.08 | 0.04 | 0.00 | 4.00 | 4.17 |
Note: Number of observations = 374; Overall R-squared = 0.999; SRMR = 0.202; _cons, represent the regression’s intercept.
OE, Operational Excellence; IT, Information technology; SRMR, Standard Root Mean Squared Residual; VUCA, volatile, uncertain, complex and ambiguous.
Noteworthy in this model is the statistically significant and positive relationship that was observed between human centricity and variables. ‘OE has transformed work culture’, ‘employees take ownership to improve operational efficiencies’, ‘OE has built high-performance teams’, ‘job satisfaction’, ‘OE has opened new opportunities for employee growth’, ‘reviewing KPIs to meet employee needs’ and ‘morale is very high between employee and management’. This means an increase in human centric approach will result to an increase in the discussed aspects/variables.
Likewise, there was a significant positive relationship between systems and variables: ‘structure and governance systems support OE strategy’, ‘Sales and operational planning is synchronised for cost-effectiveness’, ‘implemented OE is continuously evaluated for improvement’, ‘OE strategy is aligned with organisation vision’, ‘throughputs have improved’, ‘product quality is good’, ‘procurement and supply management has improved’, and ‘planned maintenance schedules are adhered to’ and ‘deliveries are now on times, and customers are delighted with our services’. Variable ‘implemented OE is continuously evaluated for improvement’ was also positively associated with latent variable ‘strategic fit’, thus indicating an increase in the former would lead to an increase in the systems efficiency and strategic fit in the organisation.
Furthermore, ‘OE strategy implementation roll-out was steered and supported by top management’, ‘A comprehensive consultative brainstorming was conducted in the adoption of new strategies’, ‘holistic effort across all stakeholders’, ‘leadership motivated employees to own OE strategy’, ‘leadership cultivated an OE culture through shared values’, ‘leadership cultivated an OE cultured through shared values’, and ‘leadership through governance is key for OE in VUCA environments’ had a statistically positive relationship with latent variable ‘leadership’. Similarly, the variables such as ‘innovation drives OE strategy’, ‘Information technology/systems influenced the success of OE strategy implementation’, and ‘continuous innovation is key for OE sustainability’ were positively associated with ‘technology’. Other variables like ‘OE requires more funding’ were found not to be statistically associated with their respective latent variables and hence, omitted from the interpretation of the output.
The test of the variables resulted in the prediction of the regular Standard Root Mean Squared Residual (SRMR) of 0.202. The multiple coloration MC, also known as Bentley–Ryabkovsquared, between dependent variable indicators, was 0.999 for the observation number of 374.
The goodness of fit at equation level.
Dependent variables | Variance |
MC | MC2 | |||
---|---|---|---|---|---|---|
Fitted | Predicted | Residual | ||||
Training | 1.17782 | 0.3488378 | 0.828982 | 0.296172 | 0.5442172 | 0.2961724 |
Employee ownership | 1.116547 | 0.3111307 | 0.805416 | 0.278654 | 0.5278773 | 0.2786544 |
OE has transformed work culture | 0.8024266 | 0.4104069 | 0.39202 | 0.511457* | 0.7151624* | 0.5114573* |
Morale is very high between management and employees | 1.019261 | 0.5011835 | 0.518078 | 0.491713 | 0.7012222 | 0.4917126 |
Reviewing KPIs to meet employees needs | 1.240442 | 0.375685 | 0.864757 | 0.302864 | 0.5503307 | 0.3028638 |
Implemented OE is continuously evaluated for improvement | 0.9115125 | 0.5663259 | 0.345187 | 0.621304* | 0.7882281* | 0.6213035* |
Plant efficiencies and throughputs have improved | 0.8845191 | 0.4876628 | 0.396856 | 0.551331* | 0.7425167* | 0.551331* |
product quality is good | 0.8861633 | 0.5647058 | 0.321458 | 0.637248* | 0.7982782* | 0.637248* |
procurement and supply management has improved | 0.9267765 | 0.5406874 | 0.386089 | 0.583406* | 0.7638105* | 0.5834064* |
Planned maintenance schedules are adhered to | 1.110523 | 0.6252468 | 0.485276 | 0.56302* | 0.7503467* | 0.5630202* |
There was holistic effort across all stakeholders | 1.134173 | 0.6336862 | 0.500487 | 0.558721* | 0.7474764* | 0.558721 |
Leadership motivated employees to own OE strategy | 1.224137 | 0.8200862 | 0.404051 | 0.66993* | 0.8184925* | 0.66993 |
Leadership cultivated an OE culture through shared values | 1.222514 | 0.8340378 | 0.388477 | 0.682232* | 0.8259731* | 0.6822315 |
Technology takes centre stage | 0.9540726 | 0.4264875 | 0.527585 | 0.447018 | 0.668594 | 0.4470179 |
Innovation drives OE strategy | 0.7268632 | 0.4523777 | 0.274486 | 0.62237* | 0.7889041* | 0.6223697* |
Information technology/systems influenced OE success | 0.7897038 | 0.4578618 | 0.331842 | 0.579789* | 0.761439* | 0.5797893* |
Overall | - | - | - | 0.999961 | - | - |
OE, Operational Excellence; KPI, key performance indicator; MC, the correlation between dependent variable and its prediction; MC2, (mc-squared) the Bentler-Raykov squared multiple correlation coefficient.
It was observed that the variables, namely, ‘OE has transformed work culture’, ‘OE has built high- performance teams’, ‘job satisfaction’, ‘implemented OE is continuously evaluated for improvement’, ‘OE strategy is aligned with organisation vision’, ‘throughputs have improved’, ‘product quality is good’, ‘procurement and supply management has improved’, and ‘planned maintenance schedules are adhered to’ were good predictors in this model as they explained over 50% of the variance in their respective latent variables. Other variables that were good predictors of their latent variables were ‘OE implementation was steered and supported by top management’, ‘a comprehensive and consultative brainstorming was conducted in the adoption of new strategies’, ‘holistic effort across all stakeholders’, ‘leadership motivated employees to own OE strategy’, ‘leadership cultivated an OE culture through shared values’, ‘leadership cultivated an OE cultured through shared values’, ‘innovation drives OE strategy’ and ‘Information technology/systems influenced OE success’. The variable ‘OE strategy is aligned with organisation vision’ had the highest R-squared among all. When individually interpreted, we would say the goodness of fit at equation level showed that ‘OE strategy is aligned with organisation vision’ was a good predictor of leadership in this model as it explained over 86% of the variance in the latent variable ‘leadership’. The same could be said for all the above-mentioned predictor variables and their latent variables. The hypothesised meditational effects of variables and predictors and mc 2 have been established using the Bentler-Raykovsquared multiple correlations. The linear regression of the variables was followed by the structural equation illustrated in the
Structural equation modelling.
The structural equation demonstrates OE variables linkages. This also confirms the need for a holistic approach and concerted efforts across all organisation functions. Fontes (2016) viewed OE as an involving process centred on the integration of all organisational efforts towards cost effective and efficient mapping and operations of process systems to gain competitive advantage and drive growth.
The derived model in
Operational excellence model for growth in volatile, uncertain, complex and ambiguous environment.
Kakko, Kaivo-oja and Mikkelä (2020) denoted volatility as mitigated by ‘vision’, a clear-cut direction to desired destiny. Operational Excellence is a journey without a destiny, but the endpoint is a mirage. A vision gives everyone a sense of common drive to achieve goals by having a clear sense about where the organisation is going (Raghuramapatruni & Kosuri
The VUCA characteristic of Uncertainty – the lack of events predictability, yields to ‘understanding’ (Kakko et al. 2020). The management in OE implementing organisations have to deliberately step back and look at things differently; take a bird’s eye view to have a full picture, understand, and make informed and comprehensive decisions that yield desired organisational results. Most important is an effective communication system that enables full understanding across the organisation, leading to excellent performance and yielding of growth (Boya & Rao
Confounding complexities are checkmated by ‘clarity’ (Kakkoet al. 2020). For successful OE, deliberate efforts make ‘sense of the chaos’. Clarity helps to have alignment across the organisation on all grey areas. Operational excellence requires all organisational personnel to do things right the first time without room for seeking to ask for second attempts.
Drivers of OE implementations need to have the ability to think and act instantaneously. The new world order calls for the ability to anticipate sense arrival and respond quickly to dynamic changes in the business environment. The outbreak of the COVID-19 pandemic in recent times caught many governments and organisations unready to face the challenges associated with the pandemic.
Setting out standards procedures, systems, and controlled documentation of practices is good, but timeous reviews for improvements are great. Benchmarking organisational practices against the best gives reference to finding better ways to do things in product manufacturing or provision of service. The improvements must be strategically orientated in an analytic methodological approach to have a full bisect of organisational strengths, weaknesses, opportunities and threats (SWOT). Because OE is a philosophy of leadership strategy choice, problem solving, and teamwork focused on satisfying customers’ needs, it takes empowerment of everyone involved to own and continually improving all work facets (Al-Ansari et. al. 2015). Modern-day survival OE implementation calls for systemic development, evaluation, and mapping, re-designing and reengineering of systems to produce better products and services. Continuous improvement should then become the most important feature of all the key elements (Human capital, value optimisation, recapitalisation, governance and sustainability) that drive OE, as depicted by the red circle in the model in
While the advent of innovation and technology can be acknowledged as a fundamental drivers of growth, this cannot be possible without human input. Therefore, it is critical to cultivate a new culture with a shared excellence identity, norms, and values to implement successful OE. Of essence also are cooperation, trust, and phenomenal communication within the organisational structures (Mumby & Stohl
Management of change in product lifecycle, market demand and market volatility, uncertainties, complexities, and ambiguities remain the principle to sustain a competitive edge in the market. Organisations ought to have a proper procurement system that enables economies of scale and synergistic logistics, cutting intermediaries in more than three-tier supplier links. Operations must be of high efficiency in maintenance, aided with computerised maintenance management systems (CMMS). New and unique combinations of these competencies are called value chain optimisation, which entails the profitable execution of every activity with reduced costs, reduced lead times, and reverse logistics.
It emerged that it takes money to make money: a good deal of investment in OE results in tangible growth. Over 60% of the experts pointed out that although budgets set for OE are always adequate, sustainability after implementation does fail due to budgetary constraints wherein resources are now being withdrawn slowly. Yajid et al. (2018) pointed out that traditionally, organisational performance was measured by financial success and profitability. However, in modern-day with OE strategy implementation, variables such as return on assets (ROA), and ROI, return on equity (ROE) are key indicators of growth. It, therefore, remains prudent to plough back a fair share capital injection into the organisational operations, thus, recapitalisation.
Liang, Lee and Sang (
The model for using OE to achieve growth in the VUCA environment can be seen as consisting of attributes of leadership, systems and competitive advantage. These attributes reside in the intersecting domains of human capital engagement and value stream optimisation and support the organisation’s growth to achieve growth through enablers such as recapitalisation and governance.
In an environment that continues to become even more volatile, uncertain, complex and ambiguous, growth can only be achieved by ensuring that the attributes and enablers above are in place.
Operational Excellence strategy can only drive growth through proper implementation, maintenance, and improvements using management review of best practices, policies, and procedures on key performance metrics. The periodic audits to weed out non-conformances for ratification and realignment with the acceptable standard are also fundamental. Eradication of cognitive dissonance is paramount to drive the OE initiatives for achieving growth.
The OE space, and schools of thinking behind operations improvement, has always been a neglected subject. This study bisected the length and breadth of OE strategy implementation for growth in a specified environment (VUCA) covering mining, manufacturing industries and related services. There is room for further research on operational improvements in sectors such as banking, hospitality, education and even in government institutions. Studies can still be carried out on SME in an endeavour to drive findings to be used as a motivational advantage for SME to adopt OE for rapid growth in a VUCA environment, thereby actively contributing new knowledge and quick field results. Futuristic anthropologists and work antagonists can always cast their net wide to look at the effectiveness of OE implementations with the combination of advanced technologies and the Internet of things (IoT) against the backdrop of transforming the industrial landscape due to 4IR.
Thriving in the prevailing VUCA world calls for adapting to the ‘new normal’ in the business contexts, thus, vucability. In such chaotic environments, organisations have to create an internal environment of openness that values discovery, diverse perspectives, and embraces experimentation of new ideas. Thinking outside the box enables the detection of signals that foretells shifts in markets behaviours and customer loyalty. Organisations need to discover opportunities, enabled by new technology and continuous dialogue to put forward new social needs of the human capital into the context of the organisational work. The proper Human capital engagement translates to the enriched fabric of relationships that keep the dual prosperity of individuals and the organisation simultaneously. Communication of new information is also crucial for keeping everyone empowered to differentiated and informed decisions to articulate business challenges and turn them into opportunities. Thoughtful decision making in the organisation should emerge at all levels of responsibility and self-driven high-performance teams that stand to own and drive OE for growth against all odds (VUCA).
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
E.J. and R. P. both contributed equally to this work.
The research was approved by the MANCOSA Institutional Research Ethics Committee.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
The data was collected as part of a doctoral research following the institution’s research data management policy.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.