The essence of this report reflects on a recent quantitative survey commissioned by a Washington DC non-profit that recommended that ride-hailing services such as Lyft and Uber share more data about pick-ups and drop-offs with city public transit agencies who could use that data to better serve overall public transit needs. The study itself, involving over 4000 consumers in seven U.S. cities, revealed that people often utilize ride-hailing services on weekends to attend special events or to insure reliable transportation while relying on public transportation for daily commute needs. A little over 20 percent of respondents indicated that they used ride-hailing services for commuting to work.
The “Walled Garden” of Non-Data Sharing
The authors of this study commented that a “walled garden” of non-data-sharing will hamper both constituencies if they expect to take part in a wider transportation mobility ecosystem. In essence, while insuring that personal identity data is protected, information or knowledge reflecting the demand patterns of the broader ecosystem or enterprise is far more meaningful and important toward meeting changing consumer and customer support and fulfillment needs.
We note the above because it provides yet another analogy to the obstacles and needs that constantly occur across the end-to-end supply chain, and indeed the enterprise. It reflects the challenges of the analytics maturity curve where notions that individual-knowledge-is-power clash with requirements for organizational wide insights and needs for various forms of prescriptive analytics based decision-making.
In the ride-hailing survey noted by the WSJ, the survey data uncovered the opportunity for communities to support what was termed as “supersharers”, people less likely to own a car in a city, and more inclined to utilize a combination of ride-hailing services such as car and bicycle sharing for a certain amount of trips at certain times. In essence, the opportunity is to better identify and serve quickly changing transportation needs within cities because of the existence of newer services introduced.
The Power of Prescriptive in Ride-Sharing
But what if the ride-sharing context was anchored in survey data of usage patterns of ride sharing services to identify future service growth needs accomplished by time-series extrapolation or regression?
In other words, more prescriptive connotations, for instance:
- Modeling the entire network of ride-sharing services including public, private or super sharing services.
- Utilizing data from predictive tools to feed the network described above
- Modeling and identification of the various constraints associated with the problem at-hand; e.g. the number of total vehicles, drivers or no drivers required, passengers per vehicle or known capacity constraints related to demand for transportation services.
- Identify the best mix of people transportation services that optimizes the objectives at-hand- in this case, customer satisfaction needs of ride-sharing passengers, optimizing budget or operational constraints for public or governmental transit agencies, maximizing constrained government budgets or profit opportunities for private ride-sharing firms.
Moving along the analytics maturity curve often involves a number of maturity phases. That includes demonstrating the business value and power of sharing data across business functions in certain context. It further involves incorporating more predictive analytics to convert data into insights as to what can or should be expected, given the current pattern of events.
However, at the same time, there is sometimes a reality, namely that enterprise sharing of data does not lend itself to a cookie-cutter approach.
Ditching the Cookie-Cutter Approach for Better Supply Chain Insights
Too often, teams approach broader data and information sharing from the perspective of force-fitting existing business systems. As an example, there are applications supporting supply chain related process support needs, financial management and budgeting as well as overall customer support needs. The obstacles for both integrating all of such business process data into more prescriptive based insights often presents itself in Sales and Operations Planning (S&OP) processes which by their existence, represent cross-business representation and advocacy. The context of data knowledge is the dominant business application supporting the organization or function, such as the ride-hailing example. The need for deeper, more prescriptive insights is satisfied when similar and added data is shared with common context, for example, profiles of overall customer demand contrasted to fulfillment channels, or categories of various customers in-context to fulfillment needs and cost-to-serve needs.
How We Can Break Down the Barriers to Analytics Maturity
Moving along the analytics maturity curve has the same People-Process-Technology implications as other major enterprise-wide change management initiatives.
Improve Analytical Skills
Teams and individuals need to improve their analytical skills because most industry environments are moving quickly towards quicker, more analytics driven decision-making needs. They need the ability to formulate more cross-functional approaches to enterprise wide analytics and decision making that is not hampered by existing business applications that are singularly functionally anchored. Business leaders need to advocate and actively support not only the cross functional sharing of data, but more prototyping approaches to needs in supporting more prescriptive decision-making vs. reactive decision-making.
Include All Business Silos
Processes need to move their context and information sharing beyond a single functional umbrella such as supply chain management and instead include context for product management or design, individual customer service process and channel fulfillment expectations, as well as financial and business outcome goals and metrics.
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This blog post originally appeared on Analyst Bob Ferrari's blog, Supply Chain Matters. To access the full blog, please click here.
