Forecasting and Procurement at Le Club Français du Vin Business 361 B- Technology and Operations Management Armine Kalan & Triska Lee 5 May 2011 Kalan & Lee |1 Honors Pledge: As a student of the Dr. Robert B. Pamplin Jr. School of Business Administration I have read and strive to uphold the University’s Code of Academic Integrity and promote ethical behavior. In doing so, I pledge on my honor that I have not given, received, or used any unauthorized materials or assistance on this examination or assignment. I further pledge that I have not engaged in cheating, forgery, or plagiarism and I have cited all appropriate sources. Student Signature: Armine Kalan & Triska Lee Kalan & Lee |2 Problem As Directeur Général of the Club Français du Vin, Stéphane Zanella must forecast the demand for particular bottles of wine and place orders with the wine growers, while attempting to maximize profit. In the past, Le Club’s forecasting has not aligned with consumer demand, illustrated by the many outliers (See Figure 1 below). Ideally, he must minimize the difference between the order quantity and the forecast, based on historical customer demand. Forecasts and Actual Demand 7000 6000 Actual Demand 5000 4000 3000 2000 1000 0 0 1000 2000 3000 4000 5000 6000 Forecast Figure 1. Forecasted and Actual Demand from January 2004: All wines. Analysis Principles Applicable This problem calls for the Newsvendor forecasting model, applicable because there is only one procurement opportunity several months before the release of the catalog. The Newsvendor model balances the cost of ordering too much against the cost of ordering too little. Kalan & Lee |3 Implementation First, the A/F Ratios from historical data were calculated to provide a measure of the forecast accuracy from the January 2004 catalog. Then, the wines were ranked from lowest to highest A/F ratio and assigned a ranking (from 1 to 45). Additionally, each wine was assigned a percentile based on rank. Then, the wines were categorized according to color and type. The rosé and white wines were paired together because they follow a similar demand seasonality. Red wine was analyzed separately according to its season, as were the “carton” wines because they contained a variety of red, white, and rosé wines. In addition, the number of bottles in each carton was not specified so it was assumed that each holds four bottles. In order to determine the Co and Cu values for the Critical Ratio, the procurement cost and salvage values first had to be determined. The cost of capital (15% of purchase cost) and the shipping (€1.25) were constant for the red, white, rosé and carton wines. However, the holding & warehousing costs and salvage values varied due to the shelf life of the wine. Since the number of wine bottles included in the cartons was not specified, it is assumed that there are four bottles in each carton. Therefore, the cost of capital, the shipping, and the holding and warehousing costs for cartons are all multiplied by four. White wine has a shelf life of up to 8 months, while red wine shelf life of up to 15 months. Since the shelf life of rosé wines was not specified, it was assumed to mirror that of the white wine due to their similar seasonality. Since there is a variety of wines included in the cartons, their shelf life is limited to the shortest shelf life (8 months) for the white/rosé wines. This means that each bottle of white/rosé wine and each carton have holding & warehousing costs of €0.80. Each bottle of red wine costs €1.50 in holding & warehousing costs. The salvage values for red and white wines were 30% and 40% of the retail price, respectively. The salvaged values of the rosé and carton wines were not specified. Since the demand for rosé wine mirrors Kalan & Lee |4 that of white wine, the salvage value was assumed to be 30% of the retail price. The carton wines were assumed to consist, on average, of equal parts red and white/rosé wines; therefore the salvage value was the average of the two salvage values, 35%. Next, the Co and Cu were computed to determine the Critical Ratio. The Co was calculated by subtracting the salvage cost from the procurement cost (including holding & warehousing cost, cost of capital, and shipping cost). The Cu consisted of the retail price minus the cost of actually buying the bottle (50% of the retail price). The Critical Ratio was then used for both the empirical and normal distribution methods under the Newsvendor model. Newsvendor Model Empirical Distribution A beneficial feature of the empirical distribution is that it reflects the historical forecasting capability. A disadvantage is that, as a discrete distribution, it predicts only a limited number of possible outcomes. The historical data is limited to the wine list from the January 2004 catalog, totaling a mere 45 items, which further limits the possible outcomes. If more historical data were available or if there was a greater number of products to analyze, it is likely that the empirical method would yield a higher forecasting accuracy than in this case. The Critical Ratio of each wine was viewed against the percentile ranking of each wine for the January 2004 catalog. Since the Critical Ratio does not perfectly match a percentile ranking, the higher percentile ranking was chosen, leading to a greater order quantity. Each percentile ranking corresponds to an A/F ratio, which was then applied to the current forecasted quantity. This resulted in the expected profit maximizing quantity (See results table on next page). Kalan & Lee |5 Appellation Color Retail price (€ per bottle) Forecast Order Qty BOURGOGNE ALIGOTE Blanc € 7.20 1100 1056.0000 ENTRE DEUX MERS Blanc GRAVES Blanc € 5.15 1500 1337.1429 € 9.90 750 788.5714 SANCERRE VDP du Comté Tolosan Blanc € 12.00 1800 1944.0000 Blanc € 3.30 2300 1762.1538 BORDEAUX CLAIRET Rosé € 5.50 4000 3638.0952 CABERNET D'ANJOU Rosé € 5.60 3000 2728.5714 VDP des Côteaux de L'Ardèche Rosé € 3.30 2900 2221.8462 ALOXE CORTON Rouge € 21.90 1200 2813.3333 BORDEAUX Rouge € 4.65 6000 4968.0000 Bordeaux Rouge € 4.50 2900 2387.6667 CDR Vill RASTEAU Rouge € 8.90 900 946.2857 CHINON Rouge € 5.85 4500 4092.8571 CORBIERES (6) Rouge € 5.70 1300 1182.3810 COTEAUX DU LYONNAIS Rouge € 5.35 3000 2674.2857 CÔTES DE BOURG Rouge € 7.20 1300 1248.0000 CÔTES DU VENTOUX Rouge € 5.60 1200 1069.7143 FAUGERES Rouge € 7.30 2000 1920.0000 FAUGERES Rouge € 6.80 6000 5568.0000 FAUGERES Rouge € 6.30 4000 3706.6667 GAILLAC Rouge € 5.80 2500 2273.8095 GIGONDAS Rouge € 13.90 1000 1275.6522 GIVRY Rouge € 12.90 900 1125.0000 GRAVES Rouge € 8.40 1000 1051.4286 MADIRAN Rouge € 9.50 5000 5400.0000 MADIRAN Rouge € 8.55 7000 7360.0000 MINERVOIS Rouge € 5.21 4000 3520.0000 PESSAC LEOGNAN Rouge € 18.90 1300 3046.3333 VDP des Côteaux de L'Ardèche Rouge € 3.25 3500 2551.1111 CARTON PANACHE € 12.15 1600 1088.0000 CARTON PANACHE € 10.90 1600 1088.0000 CARTON PANACHE € 5.95 6000 3528.0000 CARTON PANACHE € 9.47 3000 1920.0000 CARTON PANACHEE € 3.59 3000 351.0000 See Appendix for full results. Normal Distribution The advantage of the normal distribution is that there are an infinite number of possible outcomes. The difficulty lies in finding a normal distribution that accurately fits the data. Kalan & Lee |6 First, the historical data was separated according to each particular category of wine (red, white/rosé, and carton) in order to ensure a higher forecasting accuracy. The data was then used to calculate the average and standard deviation of the actual demand. These were applied to the forecasted values for the current season to calculate the expected actual demand and the standard deviation of expected actual demand. The Critical Ratio was used to determine the z-value for each wine. The profit maximizing order quantity was formulated by adding the expected actual demand to the product of the standard deviation of actual demand and the z-value (See results table below). Appellation Color BOURGOGNE ALIGOTE ENTRE DEUX MERS GRAVES SANCERRE VDP du Comté Tolosan BORDEAUX CLAIRET CABERNET D'ANJOU VDP des Côteaux de L'Ardèche ALOXE CORTON BORDEAUX Bordeaux CDR Vill RASTEAU CHINON CORBIERES (6) COTEAUX DU LYONNAIS CÔTES DE BOURG CÔTES DU VENTOUX FAUGERES FAUGERES FAUGERES GAILLAC GIGONDAS GIVRY GRAVES MADIRAN MADIRAN MINERVOIS PESSAC LEOGNAN VDP des Côteaux de L'Ardèche CARTON PANACHE CARTON PANACHE CARTON PANACHE CARTON PANACHE CARTON PANACHEE Blanc Blanc Blanc Blanc Blanc Rosé Rosé Rosé Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge Rouge See Appendix for full results. Retail price (€ per bottle) € € € € € € € € € € € € € € € € € € € € € € € € € € € € € € € € € € 7.20 5.15 9.90 12.00 3.30 5.50 5.60 3.30 21.90 4.65 4.50 8.90 5.85 5.70 5.35 7.20 5.60 7.30 6.80 6.30 5.80 13.90 12.90 8.40 9.50 8.55 5.21 18.90 3.25 12.15 10.90 5.95 9.47 3.59 Forecast 1100 1500 750 1800 2300 4000 3000 2900 1200 6000 2900 900 4500 1300 3000 1300 1200 2000 6000 4000 2500 1000 900 1000 5000 7000 4000 1300 3500 1600 1600 6000 3000 3000 Expected Actual Demand 931.2806 1269.9281 634.9641 1523.9138 1947.2232 3386.4751 2539.8563 2455.1944 989.4742 4947.3708 2391.2292 742.1056 3710.5281 1071.9303 2473.6854 1071.9303 989.4742 1649.1236 4947.3708 3298.2472 2061.4045 824.5618 742.1056 824.5618 4122.8090 5771.9326 3298.2472 1071.9303 2885.9663 1373.3392 1373.3392 5150.0218 2575.0109 2575.0109 SD of Actual Demand 242.5239 330.7144 165.3572 396.8573 507.0955 881.9052 661.4289 639.3813 566.7507 2833.7535 1369.6475 425.0630 2125.3151 613.9799 1416.8767 613.9799 566.7507 944.5845 2833.7535 1889.1690 1180.7306 472.2922 425.0630 472.2922 2361.4612 3306.0457 1889.1690 613.9799 1653.0229 755.6676 755.6676 2833.7535 1416.8767 1416.8767 Order Qty 1,030 1,331 738 1,823 1,891 3,587 2,698 2,385 2,719 5,071 2,415 1,004 4,203 1,201 2,696 1,326 1,100 2,052 5,975 3,856 2,327 1,363 1,183 1,088 5,734 7,677 3,555 2,151 2,507 1,137 1,096 3,208 1,951 1,225 Kalan & Lee |7 Recommendation Le Club Français du Vin should use the profit maximizing order quantities derived from the normal distribution. The order quantity under the normal distribution is more accurate because it provides a greater number of possible outcomes. It leads to a smallest variation between forecasting and actual demand so underage costs and overage costs are minimized. With less over stocking Le Club Français du Vin saves on cost of capital, holding & warehousing costs, and procurement costs. With less understocking, Le Club Français du Vin will be better able to match customer demand and therefore increase profit from bottle sales. Increased availability of desired wines will also lead to higher customer satisfaction. Overall, by using the normal distribution order quantity to determine order quantities, Le Club Français du Vin will maximize profit from inventory and improve customer satisfaction. In addition to implementing the normal distribution to achieve the profit-maximizing order quantity, Le Club Français du Vin should also consider reducing its lead time so that it can reduce inventory and increase the availability of wine for customers. A reduction in lead time would also provide an opportunity for Le Club to take into account other important factors when it comes to forecasting, like the wine critics’ reports on particular wines. If Le Club Français du Vin could order later, after the reviews are released, or even place an additional order in the catalog’s season, it would be able to adjust forecasting based on the reviews.
