A gap analysis can move manufacturing facilities into the future of business intelligence. Forward-thinking owners should consider platforms that provide insight into their data warehouses and will connect various systems in order to gain data traceability. While the return on investment might be more conceptual than quantitative, value is found in the details. Do you know where your data is?
Anatomy of Gap Analysis
The concept of gap analysis has been around for ages. Through an engineered approach, we have figured out mechanisms that simplify the process. Gap analysis involves taking an existing process or system and looking at its current functionality. The next step is to take the desired outcome or change in performance and to look ahead to what the system could or even should look like to achieve new functionality. The delta of the two steps is a gap analysis. Widely accepted in the corporate world, gap analysis often results in a tangible increase in system and process performance, and this performance upgrade is measured by a common term known as a return on investment (ROI).
In manufacturing, ROI is typically seen with improved production performance, more units produced per unit of time. In short, if the execution of a gap analysis results in more production with the same effort, the return on investment is deemed positive. This ROI drives many decisions manufacturers make. Smart manufacturers will think outside the box when it comes to ROI for data and upgrades.
In the Business Intelligence space, I would like to suggest a new term: “virtual return on investment.” With the advancement of IT and the introduction of a virtualized environment, virtual ROI seems fitting. Virtual ROI can be defined as an ROI that may not be seen in a tangible, mathematical form yet is extremely valuable for a company’s vision, strategy, and big-picture point of view. The issue with virtual ROI is that, since manufacturing companies do not see an immediate and tangible ROI, the whole initiative and effort of gap analysis is scrutinized. Management may ask why the company should invest in understanding the gaps of its Business Intelligence system. The answer requires a deeper understanding of the value in data flows.
Putting Information Into Action
If you look across the globe, you see varying levels of infrastructure complexity. Some companies are early adapters and have been proactive with their infrastructures, but some companies are followers and jump into the game later. The truth is that without an infrastructure foundation, Business Intelligence systems cannot exist. That is why early-adopting manufacturers pay close attention to the needs of infrastructure improvement before looking into Business Intelligence and software upgrades.
A common evolution of many manufacturing plants is to plug and play new systems as needs arise. These systems are typically installed separately, and the decision to install these systems is mostly dependent on how they can either improve production or reduce costs. On most occasions, we see companies do design reviews to evaluate how systems will integrate with the other systems on the plant floor. This connectivity between systems is critical to understanding the transformation of intangible ROI into something tangible. It is the formula that will help manufacturing companies see an ROI in gap analysis, and it could propel latecomers to become early adapters.
Steps to Take
1. Definition Phase. The first step is a reverse-engineering approach where an analyst reviews the existing servers, virtuals, and databases/data warehouses and performs a system analysis. Upon this examination, the current design is put on paper. Assuming a systems integrator is hired to perform this activity, the integrator can spend time gathering requirements and then propose a design strategy for moving forward.
2. Design Phase. In this phase, the systems integrator works with the client to formalize a design basis from the gap analysis. In this design basis, the systems integrator can create relational database design, ETL methods (Extract, Transform, Load), connectivity data flows between other data warehouses if they exist, or new relational databases in a design format to get to the end goal. The last step in this phase is to define and design the visualization needs. This will vary a lot with the type of dashboards, trends, charts, and analytics the manufacturer desires. If the goal is to get advanced data analytics with statistical modeling and predictive analytics, there is software designed to do that. If the goal is basic dashboarding, there is software for that as well.
3. Execution. A critical part of this process is perfecting the execution process and getting results. The hard work is done, but more complex work is now necessary to plan and execute design strategies. In this phase, the team works together to find downtime where upgrades can occur and new processes can be tested.
4. Monitoring and Support. When the previous steps are complete, manufacturing companies should figure out the best strategies moving forward for supporting their new, fully digitized, integrated, and traceable system. Manufacturers with plants nationwide or across the globe will need to consider the logistics of locations and time zones and seek out service providers that can provide support and monitoring assistance around the clock.
A firm understanding of the value gained through gap analysis (be it virtual or not) can separate manufacturers on the forefront of technology from those lagging behind. Letting your data work for you and having a fully integrated system are smart steps on the path toward improved performance.