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“Productivity isn’t everything, but in the long run, it is almost everything.” This well-known quote is attributed to Paul Krugman, the well-known American economist and winner of a Nobel Memorial Prize in Economic Sciences for his contributions to New Trade Theory and New Economic Geography.

In economic terms, a common definition of productivity cites it as the ratio between the volume of outputs and the volume of inputs. It measures the efficiency of production inputs – labor and capital – used to produce a given level of output.

 
For countries and companies alike, productivity gain is a fundamental goal. For countries, productivity leads to higher real income, which contributes to higher living standards and better social services.

For companies, productivity is a key driver of sustainable profits and competitiveness over time. The global economy, with open markets and wide competition, pushes companies for constant productivity gains. Companies that fail in the race for productivity are the perfect candidates for extinction in the near future.

 

Productivity can be boosted in a few different ways, most notably through the innovation of new products or through new business models that guarantee higher scalability and demand. One example is how Starbucks built a sustainable business model with high levels of productivity through the deployment of strong, intangible assets such as a unique brand and efficient business processes.

Another example is Apple, a company that executed its strategy to perfection, creating a legion of fans that constantly run to buy the company’s new products, and sometimes even camp overnight outside an Apple store to get a device before it sells out. Apple succeeded not only in designing some of the most desired smartphones and PCs on the market but also in creating a business platform that generates incremental service and software revenue on top of its products. In 2020, about 15% of Apple’s revenue came from services, leveraged by its platform strategy.

Another important factor in productivity is the innovation inside. That is, how to produce more with fewer resources. While in the past few decades industrial efficiency was boosted by moving factories to low labor cost economies, this recipe is getting exhausted. The cost increase in Asian countries, driven by higher salaries, geopolitical risks and the increase in automation levels is changing the balance of this equation.

In an environment of hyper-competition and open markets, technology is rapidly reshaping manufacturing. The companies that survive in this new paradigm will be those that adopt data-driven models, innovate on their products and services, and embrace the challenge of producing more with less. I believe IoT and Industry 4.0 will be the drivers of this transformation.

Start With Management

Everything starts with management. Managers need to embrace innovation and constant improvement. Processes need to be quantified, and efficiency ratios for each of the individual processes need to be measured. For example, overall equipment effectiveness (OEE) needs to be calculated per machine, line, operator, sector and plant. Such KPIs are important to enable managers to make real-time decisions.

Include Machines

If data-driven management is the goal, then it’s time to think about execution. The ability to collect data from a variety of different machines and from a variety of different vendors is a big challenge. Industrial machines in general don’t have a common protocol and as such, collecting the data in a highly efficient manner can be challenging and daunting.

Beyond connecting machines themselves, machine data needs to be efficiently integrated across different IT systems and software, such as manufacturing execution systems (MES), enterprise resource planning (ERP) software and a variety of database applications. On top of that, there comes the challenge of building and integrating higher-level functionality, such as edge logic for real-time actions, data visualization for operators and managers, data analytics, cloud computing, machine learning and the list goes on. The complexity and associated challenges of machine and data integration cause many companies to fail along the way.

Avoid The Custom Code Trap

Many companies fail in the execution, and one of the reasons is because it is not a simple task. As IIoT is a relatively new concept, the market is not fully matured. Many companies create their own internal team and start to code. The problem is companies may not be prepared – they often lack the right level of skills, people, and expertise. It's not impossible to execute internally, but oftentimes focusing on your core business and finding the best technology tools for your needs in the market is the more efficient choice.

If you're looking at outside teams, a good way to avoid high development costs and operations risk is to find an integrated platform that merges data collection, edge computing and information technology/operational technology (IT/OT) integration. The more vertically integrated, the faster the deployment and the less likely you will need "Band-Aids" to integrate systems. This will provide more flexibility and optimize performance while reducing the cost and risks of the project.

It’s also important to remember that innovation and productivity is more than a task. It is a journey. Processes need to constantly evolve, and your IIoT platform must provide the ability to be flexible when you need to change machines, systems, metrics and processes.

In the end, productivity excellence is a blend of management, creativity and technology. It means pushing people out of their comfort zone and augmenting possibilities with technology. Not easy, but certainly needed.

 

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