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Beaton et al (Am J Clin Nutr 1979;32:2546-59) reported on the partitioning of variance in 1-day dietary data for the intake of energy, protein, total carbohydrate, total fat, classes of fatty acids, cholesterol, and alcohol. Using the same food intake data and the expanded National Heart, Lung and Blood Institute food composition data base, these analyses of sources of variance have been expanded to include classes of carbohydrate, vitamin A, vitamin C, thiamin, riboflavin, niacin, calcium, iron, total ash, caffeine, and crude fiber. The analyses relate to observed intakes (replicated six times) of 30 adult males and 30 adult females obtained under a paired Graeco-Latin square design with sequence of interview, interviewer, and day of the week as determinants. Neither sequence nor interviewer made consistent contribution to variance. In females, day of the week had a significant effect for several nutrients. The major partitioning of variance was between interindividual variation (between subjects) and intraindividual variation (within subjects) which included both true day-to-day variation in intake and methodological variation. For all except caffeine, the intraindividual variability of 1-day data was larger than the interindividual variability. For vitamin A, almost all of the variance was associated with day-to-day variability. One day data provide a very inadequate estimate of usual intake of individuals. In the design of nutrition studies it is critical that the intended use of dietary data be a major consideration in deciding on methodology. There is no "ideal" dietary method. There may be preferred methods for particular purposes.

I n this research, we apply robust optimization (RO) to the problem of locating facilities in a network facing uncertain demand over multiple periods. We consider a multi-period fixed-charge network location problem for which we find (1) the number of facilities, their location and capacities, (2) the production in each period, and (3) allocation of demand to facilities. Using the RO approach we formulate the problem to include alternate levels of uncertainty over the periods. We consider two models of demand uncertainty: demand within a bounded and symmetric multi-dimensional box, and demand within a multi-dimensional ellipsoid. We evaluate the potential benefits of applying the RO approach in our setting using an extensive numerical study. We show that the alternate models of uncertainty lead to very different solution network topologies, with the model with box uncertainty set opening fewer, larger facilities. Through sample path testing, we show that both the box and ellipsoidal uncertainty cases can provide small but significant improvements over the solution to the problem when demand is deterministic and set at its nominal value. For changes in several environmental parameters, we explore the effects on the solution performance.

We study how differences in product demand characteristics affect the strategic value of different types of supply chain flexibility for accurate response. We propose a single-period inventory modelling framework with two ordering opportunities. The second order reflects updated demand information and potentially capitalizes on supply chain flexibility. We consider two complementary forms of flexibility: quantity flexibility in production and timing flexibility in scheduling. In this framework, we analyze the total inventory cost of a firm for alternate demand types. We model functional products through the standard assumption of independent demand over the period, fashion-driven innovative products through a Bayesian model, and innovative products with evolving demand through a Martingale process. The three demand processes exhibit very different behavior with respect to the value of the alternate forms of flexibility. We observe that quantity flexibility is of moderate value for functional goods and of high value for fashion-driven products for all lead times. Quantity flexibility is of low value for goods with evolving demand with long lead times but of high value for short lead times. Alternately, we observe timing flexibility is of highest value for functional goods, especially for cases of high holding cost, and is of lesser value for fashion-driven goods. It is of least value for goods with evolving demand. Both quantity and timing flexibility capabilities are required to significantly reduce the relevant supply chain costs for evolving-demand innovative goods when the lead times are long.supply chain, fashion goods, innovative goods, Bayesian updating, Martingale demand

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