Leveraging Big Data from Florida to Toronto
Unlike apples or potatoes, sweet corn is an item that could
really benefit from a reduction in travel time from field to
consumer. Ongoing research has taught us that improved taste
is a key driver in getting consumers to purchase more sweet corn.
Optimal eating quality lasts up to fourteen days from
harvest, provided it has been stored correctly.
A key grower challenge has always been matching production with
demand. Predicting demand is very complex, driven by a number
of factors including weather, price point, ad/display placement,
and more. Optimizing production can be equally complex.
Given all of this, how do you build the optimal supply chain to
satisfy consumer desire for better tasting sweet corn?
Let's start with a Florida grower trying to meet demand for
sweet corn in Toronto(for more information about suppliers and
retailers in Toronto and Ontario, see our supplement). His
goal is to sync production with demand. In Toronto, the
retailers are making merchandising decisions related to sweet corn
while the grower is planting his crop. Both sides are working
independently and while both are intent on improving sales, neither
is optimizing the use of data to improve results. Enter Big
Big Data,discussed in this quarter's feature article, starts
with the consumer. Each retailer will craft a strategy to
grow the corn category. With Big Data, they can craft
micro-strategies by consumer segment with targeted promotions in
the extremely diverse Toronto market. A more varied consumer
segment makes predicting demand more difficult as each segment acts
and reacts to price and promotion in a different way.
Retailers can couple this information with online data measuring
the frequency of sweet corn recipes in social media, and even
track Toronto's weather, as experienced merchants know that outside
temperatures can impact consumption. Retailers can also track
competitive activity on sweet corn as it is often a featured item
during key summer selling periods. Finally, by blending or
synthesizing all this information with their basic merchandising
decisions on price, placement, and promotion,, retailers can
predict demand better than ever.
Down in Florida, the grower can use this forecast data to build
a production plan that creates supply just in time for demand.
The grower can integrate data by field, crop input, and
predicted weather patterns. For instance, he/she may find
that one field can produce sweet corn in 78 days while another may
need 81 days. All the while, the grower can implement
sustainable measures by micro-targeting inputs like water,
pesticide, and fertilizer to avoid over use. In the event of an
unexpected weather pattern, the grower can analyze the data to
determine its effect on harvest date. Imagine the impact of
sharing this information with the retailer and the trucking
company. "Sweet corn will be three days early-adjust your
marketing strategies to assure your shoppers get great flavor, and
shift your trucks to load earlier."
After harvest, the Florida grower can track progress of his/her
product through the supply chain. Temperature data can be gathered
while it is in the cooler, and once loaded, the carrier can provide
location, temperature, and transit time data. During each
step of the process, the grower can give the Toronto retailers
feedback on product quality and timing. This precision could
make the difference between delivering good or optimal quality to
The ultimate goal of Big Data is to drive demand.
Encouraging Toronto consumers to buy more sweet corn by increasing
their confidence that they will receive great-tasting product is
key. Applying Big Data can reduce the amount of time from
field to shelf, resulting in better tasting sweet corn. The secret
is not just gathering the data, but analyzing it and sharing it
with supply chain partners in a timely manner. Undoubtedly,
Big Data has the potential to transform the produce supply chain
and drive demand.
Originally published in Blueprints July 2014.