Daniel Fowler, senior vintner at Napa Winery, had been put in charge of developing an optimal blending plan for the upcoming season. This assignment was the result of a recent Napa Winery board meeting where the CEO had presented her ideas regarding the use of analytics for enhancing profits while at the same time not affecting quality. Industry reports indicated that a growing number of the major wineries were using analytics to assist in the wine-blending process. The board meeting had concluded with the CEO tasking Fowler to develop an analysis and report his findings to the board at next month’s meeting.

The United State has become the largest wine market in the world, with sales approaching $40 billion annually. Typically, two types of wines are produced: varietals and blends. Wine blending is the process of combining several grape varieties to achieve a characteristic that is lacking in the original grapes. There are several reasons why a vintner might want to blend wines, including: (1) enhancing aroma; (2) improving the color; (3) raising or lowering the acidity level; (4) raising or lowering alcohol levels. The process of wine blending contains both objective and subjective components. Alcohol level is an example of an objective standard.

Napa Winery was one of the premium wine producers in the state and had recently begun to sell its products on a global basis. The winery produced and distributed a wide range of premium wine, including its flagship – CS Wine. The firm’s management was considering employing prescriptive analytics as a means of improving the wine-blending process. Typically, wines were produced from a blend of several types of grapes. In producing these blended wines, the vintner had to take into consideration both grape characteristics and government regulations. Each of the candidate blends was then subject to a series of taste tests. In those cases where the candidate wines were found to be unacceptable by the tasters, a set of new products was often produced. The vintner planned to use prescriptive analytics to help develop an optimal blending strategy and assumed that all bottles produced could be sold. More specifically, the vintner was going to undertake a comparative assessment of Napa Winery’ premier CS Wine product sector. The three specific production wines from this sector were:

Vintage CS Wine, which wholesaled for $9 per bottle

Non-vintage CS Wine, which wholesaled for $5.50 per bottle

Non-vintage M Wine, which wholesaled for $2.95 per bottle

Listed below are the winery objectives and government regulations.

Winery objectives and specifications

Maximize net profit.

The acidity level of CS Wine cannot exceed 0.7 grams per 100 milliliters.

The vintage CS Wine must not contain more than 0.2 per cent sugar.

The non-vintage CS Wine must not contain more than 0.3 per cent sugar.

The acidity level of M Wine cannot exceed 0.3 grams per 100 milliliters.

Government regulations

All wines labeled varietal (e.g. CS Wine) must contain at least 75% of the named grape type.

All wines must contain at least 10% and no more than 15% alcohol level by volume.

All vintage-dated wines must contain 95% blending grapes from the year on the bottle label.

All vintage-dated wines must also report the viticulture area on the label and must contain at least 85% blending grapes from this area.

Presented in Exhibit 1 are the characteristics of the four blending grades along with available quantities and associated costs.

Exhibit 1. Grape type characteristics, quantities and costs

Grape Type	Viticulture	Vintage	Acidity (gm/100 ml)	Sugar (%)	Alcohol (%)	Quantity (bottles)	Cost ($/bottle)
CS grapes	Zone 1	2011	0.35	0.12	13.5	50,000	2.35
CS grapes	Zone 2	2010	0.75	0.25	15.3	60,000	2.60
CS grapes	Zone 2	2011	0.55	0.30	11.5	30,000	2.10
M grapes	Zone 1	2010	0.25	0.08	15.7	200,000	1.55

Questions:

What is the optimal blending plan that will help Napa Winery achieve simultaneously its own objectives and specifications, and meet the government regulations?

Are the government regulations adding more pressure on the company? What will be the optimal blending plan if those regulations will not exist? Are certain regulations more restrictive than others? Conduct a comparative analysis to identify which government regulations will be the most beneficial to company’s business.

What other aspects should Daniel Fowler take into consideration in his modeling approach before presenting the results to the CEO during the board meeting? (Hint: New optimization models might be required to sustain your recommendations)