In this article we provide a novel perspective on risk pooling approaches by characterizing and comparing their asymptotic performance, highlighting the conditions under which one approach dominates ...the other. More specifically, we determine the inventory policy and the expected total costs of systems under physical and information pooling as the number of locations grows. We show that physical pooling dominates information pooling in settings with no additional per-location costs for operating the centralized system. In the presence of such costs, however, information pooling becomes a viable alternative to physical pooling. Through asymptotic analysis, we also address the grouping problem, the division of a given set of non-identical locations into an ordered collection of mutually exclusive and collectively exhaustive subsets of predetermined sizes and demonstrate that homogeneous groups, comprising locations with similar demand volatility, achieve a lower expected total cost. Finally, the convergence of the expected total costs and the base stock levels under the two pooling approaches is demonstrated through a simple numerical illustration. Our analysis supports the assertion that it is important to consider not only the individual characteristics of each location in isolation, but also the interactions among them, when designing pooling systems.
This paper investigates the dynamics of communication on social media, related to the spread of rumours, by studying the impact of micro-level agent interactions within social media discussions, on ...macro-level outcomes related to the diffusion of rumours. An agent-based framework is used to model social media discussions, modularly describing heterogeneous agents with differentiating characteristics, their interaction dynamics, rumour state transitions, and the evolution of networks on which these agents interact. Studying the effect of population, agent, interaction, and network characteristics, we find that some unobservable characteristics, like the initial distribution of opinions, play a significant role in rumour outcomes, particularly the homogeneity and polarisation of opinions. We report our findings on the mechanism and interactions and suggest heuristics for managers to counter the spread of unfavourable rumours.
•An MMFE-based accurate response framework can improve a commercial seed manufacturer’s profitability significantly.•Implementation of the MMFE in practice can be challenging due to scarcity of ...historical data and the inconsistent number of forecast updates.•Seeds with low to moderate demand uncertainty should be processed in a few batches relatively early in the planning horizon.•Seeds with high demand uncertainty should be processed according to the multi-ordering strategy following forecast evolutions.
In this paper, we introduce an accurate response framework in the context of commercial seed production by deploying the multiordering newsvendor model with dynamic forecast evolution to determine the timing and the quantity of production. We also demonstrate the challenges associated with applying the Martingale Model of Forecast Evolution (MMFE) to real data and propose practical remedies. More specifically, we fit the MMFE to the data for a variety of seeds (SKUs) produced by a major seed manufacturer and rank these SKUs based on their demand volume and volatility. We then assess the value of the MMFE-based accurate response by benchmarking it against the classic newsvendor model. We find that the MMFE-based accurate response can considerably increase the seed manufacturer’s profits by neatly dividing the product portfolio into four quadrants, according to demand volume and volatility, to determine the production timing and quantity. Such portfolio categorization would also enable the salesforce to better allocate their efforts to increase forecasting accuracy for the most critical products in their portfolio.
In the absence of a clear command and control structure, a key challenge in supply chain management is the coordination and alignment of supply chain members who pursue divergent and often ...conflicting goals. The newsvendor model is typically used as a framework to quantify the cost of misalignment and to assess the impact of various coordination initiatives. The application of the newsvendor framework, however, requires the specification of some probability distribution for the sources of uncertainty, and in particular, for the market demand. The specification of an adequate demand distribution becomes difficult in the absence of statistical data. We therefore consider a fuzzy approach to the newsvendor problem. We use several fuzzy parameters in the model for the demand, the wholesale price, and the market sales price. We solve the fuzzy newsvendor problem to study three coordination policies: quantity discounts, profit sharing, and buyback. For each coordination policy, the optimal order quantity of the retailer is computed. The possible profits of the members in the supply chain are calculated with minimum sharing of private information. We further extend the fuzzy newsvendor model to a setting with a single manufacturer and multiple retailers under the assumption of ample capacity for the manufacturer. Detailed numerical examples are also provided.
Novel Approaches to Feasibility Determination Solow, Daniel; Szechtman, Roberto; Yücesan, Enver
ACM transactions on modeling and computer simulation,
02/2021, Volume:
31, Issue:
1
Journal Article
Peer reviewed
This article proposes two-stage Bayesian and frequentist procedures for determining whether a number of systems—each characterized by the same number of performance measures—belongs to a set Γ ...defined by a finite collection of linear inequalities. A system is “in (not in) Γ” if the vector of the means is in (not in) Γ, where the means must be estimated using Monte Carlo simulation. We develop algorithms for classifying the systems with a user-specified level of confidence using the minimum number of simulation replications so the probability of correct classification over all
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systems satisfies a user-specified minimum value. Once the analyst provides prior values for the means and standard deviations of the random variables in each system, an initial number of simulation replications is performed to obtain current estimates of the means and standard deviations to assess whether the systems can be classified with the desired level of confidence. For any system that cannot be classified, heuristics are proposed to determine the number of additional simulation replications that would enable correct classification. Our contributions include the introduction of intuitive algorithms that are not only easy to implement, but also effective with their performance. Compared to other feasibility determination approaches, they also appear to be competitive. While the algorithms were initially developed in settings where system variance is assumed to be known and the random variables are independent, their performance remains satisfactory when those assumptions are relaxed.
We propose a computing budget allocation scheme for feasibility determination in a stochastic setting. More formally, we propose a Bayesian approach to determine whether a system belongs to a given ...set based on performance measures estimated through Monte Carlo simulation. We introduce two adaptive approaches in the sense that the computational budget is allocated dynamically based on the samples obtained thus far. The first approach determines the number of additional samples required so that the posterior probability that a system's mean performance is correctly classified is at least 1-δ in expectation, while the second approach determines the number of additional samples so that the posterior probability that the system mean lies inside or outside of the feasible region is at least 1-δ with a desired probability. Preliminary numerical experiments are reported.
Inventory management models help determine policies for managing trade-offs between customer satisfaction and service cost. Initiatives like lean manufacturing, pooling, and postponement have been ...proven to be effective in mitigating the trade-offs by maintaining high levels of service while reducing system inventories. However, such initiatives reduce the buffers, exacerbating supply chain issues in the event of a disruption. We evaluate stocking decisions in the presence of operational disruptions caused by random events such as natural disasters or man-made disturbances. These disruptions represent different risks from those associated with demand uncertainties as they stop production flow and typically persist longer. Thus, operational disruptions can be much more devastating though their likelihood of occurrence may be low. Using stochastic simulation, we combine the newsvendor model capturing demand uncertainty costs with catastrophe models capturing disruption/recovery costs. We apply data analytics to the simulation outputs to obtain insights to manage inventory under disruption risk.
This article presents results from a Delphi study on the future impact of enterprise resource planning (ERP) systems on supply chain management (SCM). The Delphi study was conducted with 23 Dutch ...supply chain executives of European multi-nationals. Findings from this exploratory study were threefold. First, our executives have identified the following key SCM issues for the coming years: (1) further integration of activities between suppliers and customers across the entire supply chain; (2) on-going changes in supply chain needs and required flexibility from IT; (3) more mass customization of products and services leading to increasing assortments while decreasing cycle times and inventories; (4) the locus of the driver’s seat of the entire supply chain and (5) supply chains consisting of several independent enterprises.
The second main finding is that the panel experts saw only a modest role for ERP in improving future supply chain effectiveness and a clear risk of ERP actually limiting progress in SCM. ERP was seen as offering a positive contribution to only four of the top 12 future supply chain issues: (1) more customization of products and services; (2) more standardized processes and information; (3) the need for worldwide IT systems; and (4) greater transparency of the marketplace. Implications for subsequent research and management practice are discussed.
The following key limitations of current ERP systems in providing effective SCM support emerge as the third finding from this exploratory study: (1) their insufficient extended enterprise functionality in crossing organizational boundaries; (2) their inflexibility to ever-changing supply chain needs, (3) their lack of functionality beyond managing transactions, and (4) their closed and non-modular system architecture. These limitations stem from the fact that the first generation of ERP products has been designed to integrate the various operations of an individual firm. In modern SCM, however, the unit of analysis has become a network of organizations, rendering these ERP products inadequate in the new economy.
Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. Earlier papers dealing with transshipments either do not incorporate ...replenishment lead times into their analysis, or only provide a heuristic algorithm where optimality cannot be guaranteed beyond settings with two locations. This paper uses infinitesimal perturbation analysis by combining with a stochastic approximation method to examine the multi-location transshipment problem with positive replenishment lead times. It demonstrates the computation of optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys a duality-based gradient computation method to improve computational efficiency. From an application perspective, it solves transshipment problems with non-negligible replenishment lead times. A numerical study illustrates the performance of the proposed approach.
We analyze the optimal production and inventory assortment decisions of a seed manufacturer facing increased yield variability triggered by extreme weather conditions in addition to long supply lead ...times as well as supply and demand uncertainty. We also investigate the limits of operational flexibility in the form of postponement using simulation models that are calibrated through field data. Our analysis shows that a minor increase in future yield variability leads to a large increase in the optimal seed production quantities. Such a rise would not only significantly increase the seed manufacturer's working capital requirements, but may also make its current supply capacity inadequate to fulfill its optimal production plans. We also show that the value of postponement decreases with higher yield variability, which also renders low-margin seeds more susceptible to this volatility.
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