How a Data Strategy can Help in Times of Crisis
Now that we are all living with the business implications of COVID-19, companies need to figure out how to maneuver through the depths of the crisis and put their best foot forward once the situation begins to normalize. Actions taken during these “crisis modes” can have a large impact on your business in the long run
I live in the bay area, where mandatory work from home began in early March, and schools are canceled. The current situation reminds me of a similar extended stay-at-home situation that we went through in my home country of Venezuela back in 2002–2003. This article follows my experience during that crisis. Hopefully, the lessons I learned through this event can offer some guidance for other companies to turn this crisis into an opportunity.
Let’s start with some background on what was happening in Venezuela during this period. Following civil unrest in 2001 and early 2002, two unlikely organizations agreed to a strike. The first organization was “Fedecamaras,” an aggregation of the national chamber of commerce, chamber of manufacturing industries, chamber of banking companies, etc. The second was the “CTV” (Confederation of Venezuelan Workers), the national aggregator of the most powerful worker unions of the country. Together these two organizations, and the political parties in opposition to the regime of Hugo Chavez, called for a national strike until a new presidential election could be held. The strike lasted for three months from December to February. It was one of the largest strikes in the history of Latin America, and the largest in Venezuela, once the state-owned oil company, PDVSA, joined the strike. Their goal was to stop the country’s economy and force the hand of the central government to call for a new election. For most Venezuelans, this meant people couldn’t work as companies voluntarily agreed to stop production, schools were canceled, and everyone sheltered at home for three months. Sound familiar?
At the time, I was involved with a company that manufactured steel welded pipes for oil and gas lines. The company was struggling before the strike and had significantly decreased its payroll. Company management decided to use the halt in production as an opportunity to invest the time and resources into improving the company’s core processes. We began this three-month journey by studying automation and how to provide data transparency to all employees of the company. We created and started reporting the right key performance indicators (KPIs), and provided everyone in the organization with the ability to check the status of the factory and manufacturing process in real-time. We added sensors to every machine to measure the time the machines were producing. We added computer stations throughout the factory floor so that plant operators could easily update the production numbers in our information system.
We also added early-stage data science in two parts of the operations. First, we aggregated sensor data to visualize KPIs in a live dashboard for operators, shareholders, and members of the board of directors. The dashboard was available online, as well as displayed on huge screens placed on the factory floor for everyone to see. Second, we implemented a very early application of computer vision to automate the external welding station of the pipe formation machine. This task typically required a human operator to make sure that the weld always followed the correct path, but with computer vision, we were able to automate this process entirely. Armed with all this knowledge and new data streams, factory machine operators could sit in control booths with access to computer touch screen systems and cameras delivering a live feed of the manufacturing process.
These changes led to a significant increase in quality, and the company, which was struggling before the crisis, grew to capture ~80% market share in a few years.
When the strike was over, unfortunately, without achieving its objective, workers came back to a completely different work environment. Their work stations had live access to the performance of each unit and could aggregate these streams to understand the performance of each production line and the company as a whole. These changes led to a significant increase in quality, and the company, which was struggling before the crisis, grew to capture ~80% market share in a few years. The work environment also improved significantly as everyone from the factory workers to the CEO could now see how their actions directly impacted the company’s performance. Management used the newly available information to add a quarterly performance bonus based on production efficiency. Because everyone had access to the data, the bonus system was transparent, and employees knew when they would hit milestones and how that would translate to bonuses. The bonuses were structured per production line to incentivize teamwork and paid out to the entire team. This small change to implement a win-win strategy made our company one of the highest paying companies in the country.
These lessons stuck with me, and I have spent my career implementing similar data strategies in other companies I’ve worked with. In supply chains, I’ve modeled the behavior of large Goods to Person (GTP) warehouses and provided algorithmic recommendations for how to improve the flow of orders. In healthcare, I’ve helped organizations make information more readily available and digestible to enable personalized patient decisions support and improve the quality of the decision making process. The approach remains similar regardless of the industry, and the best time to start thinking about how to leverage these systems is when some external factor has disrupted the normal flow of business. A “business as usual” mindset makes it much harder to take the time to stop and think about how to improve on data strategy and impact the core parts of a business.
We all find ourselves in a tight spot today, struggling to survive in the massive uncertainty ahead. But history shows us that those who can swim against the current reap the highest rewards when a crisis subsides. Companies that reinvent themselves with technology during this time will be one step ahead of their competitors once the crisis has passed.
Alejandro Martinez has a Ph.D. from Stanford University in Decision Analysis. His research focus has been on how to automate decision analysis and data science techniques to help companies use them more often in their operations. He is a co-founder and CEO at MatrixDS and focuses on assisting companies in understanding business performance and making better, real-time decisions. To see how decision modeling can improve your business, you can contact him at alejandro@matrixds.com or visit https://www.matrixds.com
