Remaining competitive in tomorrow’s markets demands a different management perspective and capability. This must move beyond traditional performance management practices, based on making decisions by using historical operational performance data alongside spreadsheet methods and classic continuous improvement methods.
The new era of IoT and Industry 4.0 technologies can provide both increased data availability and analytical insights, but realizing the full potential of such technologies requires a different style of management. A new breed of manager is needed who is capable of using predictive data to anticipate tomorrow’s business issues and opportunities in order to make earlier and smarter business decisions.
Characteristics of tomorrow’s predictive managers
Predictive managers are comfortable in regularly interacting with predictive BI dashboards, rapidly running many scenarios to determine the best available decision. They are comfortable in doing so because they have already established "model faith" and are confident in the underlying data models that support such predictive BI. Such managers must operate collaboratively within a team that understands the value of making early decisions with the aid of predictive-analysis tools.
Competence and confidence in such analytical techniques is important because making decisions early requires an element of risk. While the best available answer may not always be 100% correct in reality, it is often better than taking a gamble on the outcome. Tomorrow’s predictive managers can only flourish when supported by teams that recognize that making early trade-off decisions (which are data- and evidence-driven) is preferable to making late or tentative decisions (based on poor data and gut-feel instinct). Predictive-management teams operate at a higher decision frequency than traditional management teams in order to ensure that decisions are dynamically optimized. This level of decision agility becomes more vital when operating in increasingly volatile markets.
Collaborative behavior is crucial
Using predictive-management tools and technologies to create a longer-term perspective provides valuable thought-space to enable a more strategic management perspective. This, in turn, encourages higher-level, collaborative-management behavior internally within the business and with key customers. A predictive-management team knows that the latter is vital, since this collaboration will provide continuous validation of predicted demands and needs. Call it short-interval control. A technical analogy for this management perspective is the difference between two companies managing their plant-maintenance functions very differently--one with a reactive, firefighting approach and the other pro-actively using a predictive-maintenance regime. One situation exists in a permanently stressed state (although may feel heroic at times), while the other leads to a more controlled and innovative management state.
Beyond continuous improvement
In recent decades there have been many different brands of Continuous Improvement (CI) initiatives: JIT, TQM, Lean; 6-Sigma and TPM. I’ve been involved in successfully implementing these change programs within several industries and fully recognize their value when implemented by a capable change-management and leadership team. These programs incorporate the mobilization of a wide cross-section of the workforce in operational-performance-improvement activity. This usually involves a structured approach to training, basic data capture, analysis, improvement planning, implementation and results tracking. Well-managed programs (usually encountered within well-managed firms!) have progressively embedded such practices into business-as-usual working, integrating them into a high-performance team culture.
Many such programs and management philosophies originated from within leading automotive and manufacturing companies, forming the basis of best-practice management approaches which built momentum and were then cascaded down to other companies and sectors. The implementation of these methodologies has traditionally begun with a performance-improvement focus within factory walls, progressing to exploring supply-chain opportunities some time later. The performance impact of such activities can only be maximized when the end-to-end process is considered at all times.
In reality many firms have seen the actual business-performance impacts of such initiatives constrained because they have been implemented either at a functional silo level, at a low technical/operational level within the business, or without sufficient focus on real external customer outcomes.
Predictive improvement within digital business
Many companies are considering implementing digital transformation as a solution to fight off new, more agile competitors, but such major change must be built on strong management foundations. The basis for increased business agility must be a more dynamic approach to operations management, covering the end-to-end demand chain, with digital-IoT technologies enabling this agile working style. Progressing towards an outwardly focused perspective is important within the context of digital as the whole point of smart-industry technologies is to provide a more agile customer response and deliver a smoother customer experience across the entire value chain. The most sensible application of IoT and Industry 4.0 technology will seek to implement digital technologies that have been planned to progressively deliver these objectives in an aligned and balanced manner across the value chain. Any such chain is obviously only as agile as its most cumbersome link.
Technology is not enough
Innovation and culture are important management factors. An increasing range of digital technologies have become available to help deliver new levels of forward-looking predictive management information, however many vendors are presenting them as solutions in their own right, rather than as a component within a wider digital solutions ecosystem. Many are complex and require specialist data science, modelling and analysis skills that are not readily available. Yet again, it’s not what technology that is implemented, but the way it is implemented that is likely to be a key success differentiator. Implementing innovative digital technologies such as predictive analytics is a high-level change management task, requiring for success the right blend of education, training, skills acquisition, communications, management and patience. As has always been the case, no matter what vendors tell you, a technology splash-dump approach will not work.
Collaboration helps create a forward-looking perspective
Companies will progressively evolve to become more collaborative as they realize the value of such behavior and gain access to the more accurate forecast data and insights into their customers’ needs. This predictive customer insight is CRM gold-dust, more valuable than any data a third-party market-research firm could ever provide. Over time it can develop the mutual trust and confidence necessary to more fully involve the level of openness that demands strong customer-delivery confidence, based on previous experience and solid operations management.
This is a direction that some companies are taking as they move toward establishing servitization models that aim to secure longer term, outcome-based relationships. Some companies have already found the levels of commitment required (on both sides) challenging, but there are also many advantages, as this two-way commitment naturally creates a more innovative mindset and a barrier for competitors.
People & skills are key
People are a key part of any collaborative culture and this new digital age brings enormous change and skills-management challenges across all business functions. Collaboration vertically through business layers requires a collaborative senior-management team and a connected workforce. Within an effective predictive-management team, each member must be aware of relevant predictive-business intelligence and end-to-end business-process performance to fully participate in predictive-business decisions, often centered around running comparative-scenario evaluations driven to answer key business questions.
Moving to a predictive-management culture is a serious change-management program. Major people challenges exist. Identifying and securing new data science, data modelling and predictive-analysis skills isn’t easy, as it involves moving into unfamiliar skills-acquisition and development territory. Integrating the unfamiliar language and style that is introduced to the business with the arrival of new data science and analytics specialists presents a major cultural challenge to legacy organizations and staff, however well-intended.
Most firms don’t have the working environment or management culture of a Silicon Valley start-up, and this must be considered at the start to avoid mutual rejection and expensive employee churn. I’ve personally been involved in management, in consulting and in IT-management roles, where I’ve experienced how different the language, thought processes, styles and priorities can be between, say, a manufacturing manager, a data analyst, a management consultant and a software developer. Successfully and sustainably managing the skills and personality types involved is often more important than connecting the enabling smart tech.
The emerging breed of predictive manager must be capable of this synthesis of hard and soft management attributes to create an effective team that is capable of transforming data foresight into action. Teams must be capable of making fast, balanced judgement calls, without the protective benefit of hindsight. It’s not easy, but it can pay rich rewards to those firms that have the vision, commitment, technical capability and change-leadership skills needed to succeed.
Andrew Aitken is COO of Lanner.