This opinion piece was written by Leon Voon, Innovation Enabler at Singapore’s Lab of Forward Thinking (LOFT). It also appears in our government innovation newsfeed.

Bicycle-sharing startup Obike closed down in June, leaving bikes strewn throughout Singapore’s streets and over 100,000 upset users unable to claim back an estimated US$4.6 million in deposits.

Dealing with unknown and disruptive new technologies and business models, like the sharing economy model of Obike, is always going to be a challenge for policymakers.

But could scenario planning — an approach to policymaking that forecasts how different possible future scenarios could evolve — help policymakers to better anticipate crises and formulate plans in such rapidly changing sectors?

Quiet arrival

In early 2017, bicycle-sharing appeared quite suddenly and without much fanfare in Singapore. By the middle of the year, three start-ups were operating: Mobike, Obike and Ofo. These were companies with global expansion plans, backed by millions of dollars in venture capital.

But before long, Singaporeans started to see negative spill-overs: rusting bicycles with missing seats and broken chains left unused on the sidewalk or grass, and indiscriminate parking which blocked public spaces. As the number of bicycles grew, so did the scale of these problems and calls from the public for better regulation.

By late 2017, five operators met with a group public agencies and town councils to sign a Memorandum of Understanding (MOU) around proposed guidelines. The guidelines included the removal of abandoned bicycles within a day, using technology like geo-ring fencing to encourage customers to park in bays and educating customers about civic behaviour.

The beginning of the end

Initially, government did not intervene. Singapore has always strived to be very pro-business and, in recent years, has applied a light-touch approach to regulation of start-ups and fintech companies so as not to stifle innovation.

However, the MOU did not do much to improve the situation and, by 2018, the problem of abandoned bicycles was island-wide. In six months, over 2,000 removal notices were issued and over 340 bicycles were impounded.

By March 2018, the government moved to table a bill that spelt out fines, ranging from S$10,000 to S$100,000 (US$7,302 to US$73,020), and other legal action for bicycle-sharing companies. These companies were also required to apply for an official license by July.

Two weeks before the deadline, one of the biggest bicycle-sharing companies, Obike, announced it would immediately stop operating in Singapore, citing the impending regulations as a key reason for exiting the country’s market.

This meant its fleet of 14,000 bicycles was left idle in the streets, clogging public spaces. The company underwent liquidation, and users were unable to reclaim the deposits they had paid to use the bicycles. In total, an estimated S$6.3 million (US$4.6 million) in unreturned deposits was held by the company.

In addition to Obike’s sudden departure, two more companies have also just announced that their operations will cease, both indicating that the new licensing rules made it harder for their services to operate.

Foreseeable crisis?

What does this tell us about the government’s role in regulating new, potentially disruptive, business models? Could this result have been foreseen and avoided?

The Singapore government has had a long history of using the policymaking tool of “scenario planning”. In the 1980s it sought Shell’s assistance to develop its capacity, and since then it has been used by policymakers in ministries and units like the Centre for Strategic Futures and Risk Assessment and Horizon Scanning program, primarily for large-scale strategy and forecasting sessions on economic and security issues.

But what if it had applied some of the scenario planning concepts to the bicycle-sharing case? One such concept, forecasting (a kind of table-top war game outlining how possible scenarios could involve), can be simplified into three steps.

First, looking externally and scanning the horizon. There were reports as early as 2016 from China of bicycle-sharing enterprises closing down and thousands of bicycles being left on the streets and sidewalks.

Second, looking internally at the operating environment. Singapore is a hot and humid country, there are few dedicated cycling paths and the cycling culture is minimal. It was unclear that sufficient numbers of people would use the bicycles to sustain so many new competing firms. And, beyond profitability, it was not clear that Singaporeans would take care of the bicycles and park them in designated spaces.

Third, thinking ahead. The potential problem of indiscriminate parking could have been mitigated in advance by infrastructure investments in parking bays and regulation to clamp down on bad behaviour. The government could have invested in additional infrastructure to support cycling in general, such as bicycle paths and education for road users.

Perhaps it seems clear because we are viewing it in hindsight, but I do believe that this form of scenario planning policy exercise could have been valuable in developing potential policy and tactical responses before the actual tabling of the new laws and bills.

Future policymaking

Regulators may well want to continue to keep a light touch around start-ups and innovation. But from a practical point of view, our policymaking units are also unable to keep up with the pace of change brought about by the new technologies and disruptive business models of companies like Obike, Airbnb, Uber and Grab.

Beyond the Obike case, we see this today in Singapore with policymakers struggling to formulate policies for Airbnb and struggling to come terms with the sudden merger of Grab and Uber.

Will we be better prepared to react if we start to consider “What ifs?” and “feared future” scenarios earlier, and start to develop early-warning sign-posting and possible responses? I believe so. Especially when governments can combine it with data analytics to better stay ahead of shocks and changes, such forecasting will, in fact, I believe, soon become a key capability of future policymaking and regulation. — Leon Voon

(Picture credit: Flickr/Kyong Ahn)