In late 2010, Supply Chain Matters introduced our readers to Paris based Lokad, a rather unique technology services provider which at the time we coined as mathematicians on-demand. After our initial briefing with Founder Joannès Vermorel, we came away with an impression that industry supply chain teams had an interesting and somewhat cost affordable alternative in generating much more sophisticated timely and accurate forecasting techniques.
The company differentiated itself on the sophistication of its staff of highly trained mathematicians who take on challenges reflected in difficult forecasting problems. Customers are provided alternatives in loading product demand data “as it is” via the cloud, leveraging a Microsoft Azure platform, avoiding the need to perform tedious data formatting and pre-analysis. Vermorel and his team described themselves as rather pragmatic in the view that the goal is not to have the most accurate forecast, but rather a more automated means to determine the best response to fulfilling product demand under challenging constraints.
We checked-in with founder Vermorel in 2013 to learn about Lokad’s diversification efforts in quantile forecasting services and supporting software. As opposed to deterministic or mean-driven forecasts where respective forecast weighting are averaged, quantile forecasts introduce a purposeful bias in the forecasting algorithm and can be viewed as a stochastic method for forecasting. Our 2013 briefing notes reflected that Lokad continued to test its quantile methods on many industry verticals including the production of auto parts, electrical supplies, textile products, spare parts and packaging materials. Lokad consultants work with customers to fully understand their planning needs and develop a more sophisticated planning approach utilizing their cloud-based software platform.
A lot has occurred in advanced supply chain planning methods since 2010, most notably the notions of predictive and/or prescriptive analytics being applied to supply chain product demand and resource needs. The demand for trained individuals in analytics and Big-Data analysis in-fact has become so intense, that we called our readers attention to a Wall Street Journal report in August of last year indicating that one of the hottest jobs in tech was that of a data scientist. The WSJ noted that in certain cases, data scientists were commanding $200,000 – $300,000 annual salaries due to the shortage of such skills. Many supply chain teams as well as business teams would view that full-time expense as expensive or burdensome.
Kicking off 2015, we were thus very eager to include a check-in again with Lokad.
To little surprise, we learned that the company has now positioning itself as “Quantification Optimization for Commerce” and has since moved into offices twice its original size. The technology provider has now amassed hundreds of customers, has branched into a number of quantitative services and has developed its own next generation programming language specifically for supply chain planning and forecasting needs. We were informed of the firm’s first 7 figure engagement and its efforts to dive far deeper into challenging and industry-unique supply chain planning challenges.
What is rather unique and refreshing is that Lokad continues with its model of on-demand mathematicians providing ongoing analytical services for clients periodically during any given year. The Lokad cloud-based forecasting engine generates product forecasts predicated on probabilities and a range of predictions predicated on operational business metrics and/or operational risks. The explosion of Omni-channel commerce in retail sectors has especially fueled such needs and requirements as well as the unique needs of service focused supply chains related to highly sophisticated equipment.
We explored some current observations regarding the state of certain industry forecasting, specifically that Lokad has amassed over hundreds of engagements, The provider continues to observe fixed vs. fluid or more agile focused assumptions related to planning. For instance, top management at some firms has not taken the time to change inputted assumptions related to the cost of capital. A forecasting model for a U.S. firm continued to run with the assumption of a 6 percent cost of capital when cash is available at a far lower rate. Such a rigid assumption can often derail the accuracy of more predictive decision-making methods.
Our briefing included an in-depth discussion on the current state of Big-Data and predictive analytics initiatives across various industry settings. Vermorel apparently shares in our belief and prediction that many initiatives can well be de-railed in the coming months and years because of a lack of proper design. According to Vermorel, they include a “naïve rationalism” and actually fail at truly capturing the true drivers of the business and of the supply chain.
This author was so captivated by these observations that we extended an invitation for a Supply Chain Matters guest posting so that our readers can specifically learn from such observations.
Thus, what follows this updated commentary on Lokad is Founder Joannes Vermorel’s gracious guest posting, The Challenges and Obstacles of Big Data and Analytics Applied in Supply Chain and Commerce Decision Making.
We sincerely thank him for his contribution and insights.
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