Managerial insights

Considering the main effect of lead time variability on the expected opportunity cost, it is evident that the supply chain would perform better by reducing this variability. This task calls for strong collaboration among the supply chain members. The brewery needs to share demand forecast information with the can supplier to facilitate the supplier’s production schedule. In turn, the can supplier needs to show commitment to comply with

the fluctuating weekly quantities. When lead time variability is unavoidable, decisions should then be made in accordance with the insights that the interaction effects of lead time variability with the other factors provide.

As we discussed above, lead time variability intensifies the impact of the risk aversion level on the expected opportunity cost. That is, with higher lead time variability, the improvement in the expected opportunity cost becomes more pronounced when the risk aversion level is lower. This observation is explained by the ability of less risk averse supply chain to exploit uncertainties to minimize its expected opportunity cost. With lead time variability, the uncertainty increases and, in turn, the payoff from exploiting this uncertainty would increase. Similar interaction is observed with demand uncertainty. A supply chain can reduce the expected opportunity cost by being less risk averse, and the reduction would be larger under high demand uncertainty than under low demand uncertainty. The regression analysis confirms this argument. In the event that the supply chain cannot reduce the lead time variability, being less risk averse would balance the negative effect of lead time variability. Figure 5.2a illustrates this case with numerical example. If the supply chain is more risk averse, the expected opportunity cost increases by $ 64,000 when lead time variability increases. On the other hand, if the supply chain is less risk averse, the difference in the opportunity cost would be only $ 24,000. Furthermore, based on the three-way interactions discussed above, the supply chain would be even more compelled to be less risk averse when the aluminum price volatility is higher.

The decreasing impact of lead time variability regarding the effect of demand uncertainty on the opportunity cost makes it less compelling for a supply chain operating

under high demand uncertainty to work on reducing the lead time variability. Figure 5.2b, for example, shows that under low demand uncertainty an increase in lead time variability increases the opportunity cost by $ 53,000. This increase drops to $ 32,000 under high demand uncertainty. This is explained by the connection between the response of the supply chain to an increase in demand uncertainty and its response to an increase in lead time variability. When demand uncertainty increases, the supply chain would increase the beer quantity in the distribution center. Such increase necessitates a corresponding increase in cans quantity. The latter increase would also be necessary to mitigate higher variability in lead time.

5.5 Conclusion

In this Chapter, we study the changes in the product flows due to variability in the lead time of the supply of empty cans to the brewery. We also examine the impact of stochastic lead time on the expected opportunity cost and on the hedging decisions. In this model extension to the base model, we change the four-week deterministic duration to supply empty cans to the brewery to a stochastic duration following discrete probability distribution with a mean of four weeks lead time.

We generate 16 treatments from the permutations of the four factors: value-at-risk, demand uncertainty, aluminum price volatility, and lead time variability. Each factor is represented at two levels. We solve these treatments using the integrated model and the sequential model. In the analyses of results, we focus on the effects of lead time variability as this is the main purpose of this Chapter. Based on experimental findings, we make a number of observations. While it is expected that lead time variability increases the opportunity cost, the results reveal that this increase may not be significant under

certain conditions. For example, under low risk aversion level, only high lead time variability would significantly increase the opportunity cost. Furthermore, while expecting that a high lead time variability would result in a larger increase in opportunity cost than a lower lead time variability, results reveal that this may not be the case under high demand uncertainty and low risk aversion level. This is explained by the dominating effects of the latter two factors, at these respective levels, on the expected opportunity cost, as revealed in the regression analysis.

Knowing that the risk of stochastic lead time is traditionally managed with operational tools, it is important to note that the integrated model is found to outperform the sequential model under lead time variability. The superiority of the integrated model is, however, not influenced by the lead time variability level when the supply chain is less risk averse. In both the integrated and the sequential models, the results reveal that more risk averse supply chain would use operational hedging more as lead time variability increases.

The statistical analysis sheds more light on the interaction effects of lead time variability with the other factors on the opportunity cost, and hence allows us to draw some managerial insights that can support decisions made by practitioners. In the base model, it was found that lower risk aversion level would decrease the opportunity cost, and that a higher demand uncertainty would increase the opportunity cost. The results in the extended model show that lead time variability amplifies the former effect and reduces the latter. In turn, this impact of lead time variability on the effect of risk aversion level on opportunity cost depends on the aluminum price volatility and on the model used.

The analysis also allows us to better understand the direct effect of lead time variability on the opportunity cost. According to the results, the impact of lead time variability on the opportunity cost is higher in the integrated model than in the sequential model. Furthermore, this impact is found to be positively correlated with the aluminum price volatility.

Chapter 6