Features International Sugar Journal

Addressing productivity challenges through data analytics

During the early ‘80s, I worked as a volunteer at an agricultural development project in Malkerns, Swaziland training and rehabilitating young adults with physical handicaps and minor mental disability. A much larger neighbouring mixed farm boasted a large dairy unit. Only women were employed to milk the cows there. This was because, men, who were previously employed to do the work, would get inebriated every two weeks on the payday – the following morning, cows went un-milked. The management did not need the support of advanced data analytics to elicit what the problem was and how to address it. In less extreme circumstances, unearthing and exploiting data analytics offer businesses significant opportunity to drive productivity and profitability while driving operational excellence. In a recent piece1 by consultants at the Boston Consulting Group, they noted that during an assignment with one manufacturer, “welders productivity [was] 15% lower on Fridays”.

The game changers facilitating data analytics is the power of computing and leveraging machine learning techniques to support data processing and get some inkling of underlying trends betraying performance.

In their thought piece2, the management consultants McKinsey note advanced data analytics is a “discriminating tool to identify, and then implement, a value driving answer.” But the caveat is, there has to be clarity of purpose. ‘How is each mill performing in tandem?’ ‘How can we reduce avoidable losses?’ By specifying priorities clearly can a business respond with greater precision. Sugar production is process intensive. Because you are dealing with large volumes, incremental gains in productivity translates into significant increase in profitability. For example, a factory producing 100,000 t sugar increasing its productivity by 3%, increases its revenues by some US$1.5 mln (at current sugar prices). As McKinsey notes, if a factory “can systematically combine small improvements across bigger, multiple processes, the payoff can be exponential”.2 The question that is begging is whether factory managers or CEOs periodically “atomize a single process into its [components] and implement advances where possible”.

McKinsey cites a case study where a series of processes including demand planning and forecasting, procurement, and inventory management at a large steel manufacturer were “decoupled analyzed and resynched together in a system”. “In each process, it isolated critical value drivers and scaled back or eliminated previously undiscovered inefficiencies, for savings of about 5 to 10 percent. Those gains, which rested on hundreds of small improvements made possible by data analytics, proliferated when the manufacturer was able to tie its processes together and transmit information across each stage in near real time. By rationalizing an end-to-end system linking demand planning all the way through inventory management, the manufacturer realized savings approaching 50 percent—hundreds of millions of dollars in all.”

I have not heard of any anecdotal evidence as to whether factory managers in the sugar industry are exploiting advanced data analytics to drive their businesses. One would assume that large, multi-national sugar companies, producing million tonnes or more sugar with deep pockets would have the means to do so. But this remains an assumption.

The EU beet sugar industry has made significant strides over the past few decades which has catapulted its competitiveness globally. It is not just that beet sugar yields have increased significantly and consistently, this has been mirrored by the reduction in capital inputs (e.g. seed, fertilizers, crop protection) driving costs by some €1200/ha. This has been supported by the industry-led R&D infrastructure.

The challenge awaits sugar factory managers to demonstrate similar levels of productivity gains through data analytics.

References

1 Ravi Srivastava, Vladimir Lukic, Simon Miller, Michael Dallimore, Rohin Wood, and Adam Whybrew (December 2015) Using advanced analytics to improve operational decisions. BCG Perspectives https://www.bcgperspectives.com/content/articles/operations-big-data-using-advanced-analytics-improve-operational-decisions/?utm_source=201701&utm_medium=Email&utm_campaign=Ealert

2 Helen Mayhew, Tamim Saleh, and Simon Williams (October 2016) Making data analytics work for you—instead of the other way around. McKinsey Quarterly