An important aspect of the manufacturing executive’s job is to illustrate the connection between operations and corporate strategy, prioritise investments, and identify how to best measure progress and milestones.
It starts with the alignment between operational and financial metrics but it typically requires examining vast amounts of data to identify the key criteria supporting the operational behaviours that significantly impact profits.
In other words, operations must identify the purpose and focus of performance management. If the goal is savings, they focus on cutting cost.
If the goal is growth and market share, the focus shifts to customer satisfaction and revenue-generating activities. If the goal is to ensure performance, the focus is placed on quality and productivity.
And if the goal is corporate social responsibility, the emphasis is placed on ensuring product safety and quality of life.
We’ve seen a significant push in the market towards Big Data and analytics. These tools provide better ways to identify the operational inefficiencies that have the greatest impact on the company’s ability to meet objectives.
Figure 1, based on data from the Aberdeen Group research report Manufacturing Operations Management and Lean, shows how Best-in-Class manufacturing executives understand that aligning performance management with corporate priorities leaves room to be more proactive and able to find new ways to solve problems.
The figures compare the Best-in-Class (defined as the top fifth of the respondents based on margin, new product introduction success rate and completed-on-time deliveries) to All Others (bottom 80 per cent).
Best-in-Class companies are more likely to run business process experiments, try new BI technologies and invest in automated dashboards and analytics to empower employees with incentives and accountability to improve the way that they work.
Once operational goals are understood, the next step is to define the data approach that best supports their ability to execute and measure.
CIOs and manufacturing executives must identify the data connection to support complex decision making.
Figure 2, based on data from the Aberdeen Group Manufacturing Intelligence and Dashboards report, illustrates how top performing companies (Leaders) consistently rely on dashboards, fed by Big Data technologies, as a mechanism to direct optimal execution and compliance, promote collaboration and help measure operational decision effectiveness against business objectives.
Additional considerations to establish alignment between Big Data and corporate strategy include:
- Consolidated data views save money
The benefit of reducing the number of databases with customer information such as orders, invoices, contracts, support tickets and returns are numerous.
Fewer environments make data shopping easier with faster searches and aggregated views to promote user productivity.
The combined data view also dictates maintenance, upgrades, and improvement roadmaps from a central point of view, rather than having disparate investment strategies or duplicated efforts across departments.
- Share data sets across the enterprise
Share data to enable cross-department collaboration. For example, linking repairs, returned material authorisations (RMAs), support tickets, and escalations with sales pipeline improves services and accelerate outcomes.
A full view of customer history can support assessments based on needs and capabilities, identify problem areas and create plans to capitalise on business potential.
Shared data from other parts of the organisation complements a first-in-first-serve queue to understand the urgency and level of importance to ensure customer satisfaction.
- Apply user-driven communication.
Facts, trends and results can mean different things to different people.
A common Big Data platform supported by self-service analytical tools and role-based dashboards enables users to answer questions, take action, and fix problems on their own.
- Use data innovation to drive positive outcomes.
Combined data views can be used to detect non-compliance, anticipate production bottlenecks, and maximise outcomes of operational decisions.
Simplifying data records and views throughout process and customer models helps characterise best behaviours, create decision-making engines with fixed saving or quality functionality, and identify high risks, events or triggers across the operations.
Additionally, the diverse data perspective can help your company identify the return of continuous improvement initiatives, the best way to turn around money bleeding projects, or the strategy to handle recalls.
Manufacturing companies without a Big Data strategy will be at a significant disadvantage compared to their competitors, risking margin by not identifying operational inefficiencies, and struggling with information silos and gaps between processes and functions.
CIOs and manufacturing executives without a Big Data strategy will miss the chance to leverage corporate priorities in complex manufacturing decisions.
Mariela Koenig is research director, Manufacturing at Aberdeen Group