Effect Of Demand Uncertainty Research Articles

Effect of demand uncertainty on omnichannel distribution network design strategies

AbstractTo investigate the effect of uncertain demand on various omnichannel distribution network strategies and analyze the impacts of various online order fulfillment policies, a two‐staged network optimization model is built. The two stages are a deterministic model to define the optimal distribution network and a simulation model to determine the optimal network flow to maximize the total profit for a given distribution network and online order fulfillment policy. This study shows the importance of taking demand uncertainty, requested delivery time, and customer service level (CSL) into account in designing an omnichannel distribution network. The results of the simulation model indicate that shipments from warehouse (SFW) and hybrid strategies more successfully cope with variation because there is a greater profit decline in shipments from store (SFS) strategies. The results also show that applying a dynamic assignment policy plays an important role in handling variation in demand. The study shows that, as demand uncertainty and CSL increase, having products available in central repositories becomes even more important to cope with demand variation. The study also indicates SFS strategies that seem more profitable in the assumed‐to‐be deterministic model might be misleading because SFS strategies become less profitable (compared to SFW strategies) when demand uncertainty is taken into account.

Effect of uncertainty in demand and production for manufacturing industries during COVID-19

Effect of uncertainty in demand and production for manufacturing industries during COVID-19

Quantifying the Likelihood of Exceeding a Limit State via the Displacement-based Assessment Approach

ABSTRACT The displacement-based assessment procedure of Priestley and co-authors was formulated to provide either a pass-fail type of assessment or an indication of the likelihood of exceeding a selected limit state. To account for the effect of uncertainty in demand and capacity as part of the displacement-based assessment, it has also been shown that the SAC-FEMA approach can be adopted. In this work, the displacement-based assessment approach is improved by developing new relationships between intensity and inelastic displacement demand that permits the impact of multiple intensity levels on the likelihood of exceeding a limit state to be assessed contemporarily. The benefit of the new approach is illustrated by comparing the annual probability of exceeding key limit states as obtained through multi-stripe analyses with results obtained from the simplified approach. Results indicate that the simplified method performs reliably for the cases considered. Limitations with the new approach and future research needs are identified.

Managing uncertainty in demand through inventory and workforce development

This paper studies whether and how a manufacturer can mitigate the effects of demand uncertainty during production ramp up by jointly managing inventory and capacity development, determined by the productivity of a heterogeneous workforce. One way the manufacturer can direct its capacity development is by recognising that workers become more productive as they gain experience when making scheduling decisions. We consider a situation in which demands for multiple products are periodically revealed and a manufacturer must decide what each individual does in the production process, how much inventory is accumulated, and how demands are met. To study the potential benefits, we formulate a Markov decision process model which we solve using a one-step lookahead approximation. Results of an extensive computational study show that, given the effectiveness associated with both directing capacity and holding inventory, manufacturers should consider the workforce's capacity to learn when making job assignments during production ramp up. Analysis of the results suggests that inventory and capacity building patterns depend on product value and the workforce's experience levels. Further analysis shows that slower learners and those with the potential to achieve higher productivity should specialise more. Faster learners, on the other hand, are best suited for cross training.

Effect of uncertainty in demand and production for manufacturing industries during COVID-19

Effect of uncertainty in demand and production for manufacturing industries during COVID-19

Commission Production Contracts with Revenue Sharing for a Capacitated Manufacturer and Multiple Retailers

In this paper, we consider a manufacturer with limited production capacity producing multiple kinds of independent products, such that each kind of product is sold by a distinct retailer who is offered a commission production contract with revenue sharing by the manufacturer. We study the contract in the centralised and decentralised systems respectively. Under certain conditions of price elasticities and cost fractions, we show the uniqueness of optimal revenue share for all products. Moreover, by comparing both systems with same capacity constraint, we find that at least one retailer's price in the centralised system is higher than that of the decentralised system, and the order quantity for that retailer is lower under some conditions. As a consequence, the decentralised system's profit is always higher than the centralised system's profit under that condition. Also, the retailers' optimal prices (resp. order quantities) are increasing (resp. decreasing) in production capacity of the manufacturer, whereas the manufacturer's expected profit is increasing in its production capacity in both systems. Finally, we conduct numerical study to justify our theoretical results, and examine the effect of processing cost on both systems' profits, and the effect of demand uncertainty on the optimal prices and order quantities. [Received 22 May 2018; Accepted 28 October 2019]

Commission production contracts with revenue sharing for a capacitated manufacturer and multiple retailers

In this paper, we consider a manufacturer with limited production capacity producing multiple kinds of independent products, such that each kind of product is sold by a distinct retailer who is offered a commission production contract with revenue sharing by the manufacturer. We study the contract in the centralised and decentralised systems respectively. Under certain conditions of price elasticities and cost fractions, we show the uniqueness of optimal revenue share for all products. Moreover, by comparing both systems with same capacity constraint, we find that at least one retailer's price in the centralised system is higher than that of the decentralised system, and the order quantity for that retailer is lower under some conditions. As a consequence, the decentralised system's profit is always higher than the centralised system's profit under that condition. Also, the retailers' optimal prices (resp. order quantities) are increasing (resp. decreasing) in production capacity of the manufacturer, whereas the manufacturer's expected profit is increasing in its production capacity in both systems. Finally, we conduct numerical study to justify our theoretical results, and examine the effect of processing cost on both systems' profits, and the effect of demand uncertainty on the optimal prices and order quantities. [Received 22 May 2018; Accepted 28 October 2019]

The Effects of Demand Uncertainty on the Production of Audit Services

Economic theory suggests that demand uncertainty should influence producer behavior. In this study, we empirically examine the impact of demand uncertainty on the production of audit services. Auditors must make resource allocation decisions in advance of exact demand being known, and because a large portion of an auditor’s capacity-related commitments are fixed and therefore difficult to adjust in the short run, uncertainty in client demand outcomes likely imposes costs on auditors. Consistent with auditors being compensated for these costs, our results indicate that both audit price and audit production timing are affected by a client’s uncertainty in demand for audit services. We also find that these compensation mechanisms act as substitutes used by auditors to alleviate the costs imposed by demand uncertainty. Our study contributes to the growing literature on the underlying economics of the audit market and answers recent calls for analysis of demand-side factors that influence the audit industry.

Implications of risk-sharing strategies on supply chains with multiple retailers and under random yield

Nowadays, supply chain management cannot be overlooked with the existence of uncertainties caused by both the random demand and the random production yield. This paper considers a supply chain which includes a supplier facing random yield and multiple downstream retailers dealing with random demands. Under a Stackelberg game structure, several analytical models are presented in order to investigate the supply risk sharing mechanism within the supply chains. Under the first scenario, the same contracts (either risk-sharing contract (RS) or no risk-sharing contract (NRS)) are non-discriminatively used for all involving retailers. Then, both RS and NRS contracts are offered to the retailers (namely as a non-identical strategy), which results in the segmentation of the retailer groups. Under both scenarios, the analytical solutions to the retailers' optimal order decisions and the supplier's production decisions are obtained. The random yield impact of the supplier is investigated especially on supply chain performances. The effects of demand uncertainty, production costs' sharing, and wholesale price reduction approaches are explored. The key findings include, first, RS contract may significantly reduce supplier's holding costs; second, RS contract benefit both the supplier and the retailers in comparison with NRS contract; third, by process improvement and technology advancement, if the supplier may reduce the supply random yield effect, supply chain performances can be improved. Numerical examples and managerial insights are presented to illustrate the presented models. The findings may benefit both private companies and government agencies regarding decision making and policy setting in industries with high supply risks such as agriculture and farming related industries.

EFFECT OF DEMAND UNCERTAINTY ON COST STRUCTURE OF TURKISH MANUFACTURING FIRMS

Cost rigidity defines the characteristics of cost structure which is defined as the proportion of fixed costs to variable costs of firms. When the proportion of fixed costs are higher than the variable costs, then the cost structure is defined as a rigid cost structure. The structure of operating costs of Turkish manufacturing firms between years 1995 and 2014 is tested in terms of their “rigidity” in this paper. Moreover, the effect of demand uncertainty, as measured by the standard deviation of net sales, on the cost structure of operating costs is also tested. Findings support that higher levels of demand uncertainty are associated with higher portions of fixed costs in the cost structure (i.e., more rigid cost structure). That result contradicts the common wisdom that firms tend to lower the fixed costs in their cost structure as they are confronted with uncertainty.

Optimal Design of Return Policies

Quota-based and partial-refund return policies abound in practice between manufacturers and their resellers. While the literature has provided insights into the design of the partial-refund policy, little attention has been directed at the design of the quota-based return policy. Accordingly, this paper explores the relative preference of a quota-based policy vis-à-vis a partial-refund policy. We do this, first, in the context of risk-neutral channel partners to identify the strategic decisions of each party and the effect of demand uncertainty on the variation of their respective profits. Our results reveal that the manufacturer faces higher profit variation (between the different demand realizations) under the quota policy. The variance in profits for the reseller is, however, higher under the partial-refund policy. We explain the source of profit variations by comparing it across different channel structures (centralized and decentralized). Next, we formally extend the model to include a disutility associated with profit variation and show that when the manufacturer has a variation-induced disutility, the partial-refund contract should be used, as it is the dominating contract. Similarly, when the retailer has a variation-induced disutility, the quota contract should be used. This is consistent with the pattern of profit variations in the risk-neutral case where the manufacturer has lower variation with the partial-refund contract while the reseller has lower variation with the quota contract. Finally, our analysis also shows how the manufacturer may employ a combination policy to better manage its own profit variation while providing adequate overstocking protection for the reseller. The online appendix is available at https://doi.org/10.1287/mksc.2018.1094 .

Reducing the effects of demand uncertainty in single-newsvendor multi-retailer supply chains

Due to fierce competition in today’s global market, businesses are forced to provide customers with high service levels. Typically, vendors produce or order sufficient quantities at the beginning of a selling season to ensure reasonable service levels for the whole season. However, due to the probabilistic nature of demand, high service levels at the beginning of a selling season does not guarantee appropriate service levels during the course of consuming the item. Thus, revision of service levels during a selling season is important and ignoring such revision may lead to serious consequences for businesses like profit loss due to cancelled orders and reduction of the market share of the company. In this paper, we propose a model for a newsvendor supply chain with single vendor and multiple retailers where the vendor has two-ordering opportunities. At the beginning of a selling season, the retailer orders from a vendor a quantity such that a predetermined service level is achieved. At the second-ordering instant, the retailer learns more about the demand pattern and uses the new available demand data to update the coming demand using Bayesian approach. Based on the updated demand, the retailer evaluates the new service level for the remaining portion of the selling season. If this service level is lower than a specific value, a second batch is ordered. We develop the model for general demand distribution and determine the optimal quantities at the beginning of the selling season and at the second-ordering opportunity.

Price Dispersion and Demand Uncertainty: Evidence from US Scanner Data

I use the Prescott (1975) hotels model to explain variations in price dispersion across goods sold by supermarkets in Chicago. I extend the theory to accounts for the monopoly power of chains and for non-shoppers. The main empirical finding is that the effect of demand uncertainty on price dispersion is highly significant and quantitatively important: More than 50% of the cross sectional standard deviation of log prices is due to demand uncertainty. I also find that price dispersion measures are negatively correlated with the average price but are not negatively correlated with the revenues from selling the good (across stores and weeks) and with the number of stores that sell the good.

Mechanisms of Collaboration in the Hotel Supply Chain: Two-Stage Ordering Contract and Option Contract

This paper considers a hotel supply chain consisting of a hotel and an online travel agent (OTA) for the distribution of a limited number of rooms through both offline and online channels. Under the merchant cooperation assumption, to overcome the disadvantages of the decentralized decision model and demand uncertainty, two collaboration mechanisms, namely a two-stage ordering contract and an option contract, are introduced to increase the profit in the hotel supply chain. This paper investigates the optimal decisions of the hotel and the OTA under different contract models. Moreover, the authors analyze the effect of demand uncertainty and the interaction of demand variability and the hotel’s capacity on the profits of the hotel and the OTA under different models. The results show that both the two-stage ordering contract and the option contract can increase the profits of the entire supply chain and the hotel; however, the profit of the OTA can be increased through the two-stage ordering contract and option contract only when the hotel’s capacity is relatively small and the demand variability is big; otherwise, the two collaboration mechanisms cannot increase the OTA’s profit.

A Study of The Effect of Demand Uncertainty for Low-Carbon Products Using a Newsvendor Model.

This paper studies the effect of uncertain demand on a low-carbon product by using a newsvendor model. With two different kinds of market scales, we examine a game whereby a manufacturer produces and delivers a single new low-carbon product to a single retailer. The retailer observes the demand information and gives an order before the selling season. We find in the game that if the retailer shares truthful (or in contrast unreal or even does not share) forecast information with the manufacturer, the manufacturer will give a low (high) wholesale price through the sequence of events. In addition, as a policy-maker, the government posts a subsidy by selling the low-carbon product per unit. The manufacturer creates a new contract with a rebate for the retailer. We also take the consumer aversion coefficient and truth coefficient as qualitative variables into our model to study the order, pricing, and expected profit for the members of supply chain. The research shows that uncertain demand causes a the major effect on the new low-carbon product. Thereby, we suggest the retailer should share more truthful information with the manufacturer.

Effect SCM Capacity Factor of Small and Medium-Sized Supplier on Operational Performance: Focused on Moderating Effect of Demand Uncertainty

Effect SCM Capacity Factor of Small and Medium-Sized Supplier on Operational Performance: Focused on Moderating Effect of Demand Uncertainty

Evaluation of the Effect of Product Demand Uncertainty on Manufacturing System Selection

The use of advanced manufacturing systems is widespread; however, manufacturers frequently face difficult decisions when it comes to selecting the most appropriate system. Uncertainty regarding product demand makes this process more difficult, as many factors are influencing simultaneously. This paper focuses on analyzing the demand uncertainty on the performance of modular drilling manufacturing systems versus other alternatives and evaluating the uncertainty's impacts on the final decision. To do so, a model is suggested and the effect of demand uncertainty on the output is investigated. Three automotive components of varying complexity are used to examine the approach for making reliable decisions.

Production and procurement strategies for seasonal product supply chain under yield uncertainty with commitment-option contracts

This paper develops a game-theoretic decision-making model of production and procurement with commitment-option contracts in a decentralized supply chain consisting of one manufacturer and one retailer. The manufacturer distributes one kind of seasonal product via the retailer to the end market, whose demand is random, and the manufacturer's production yield is stochastic. We study the retailer's optimal ordering policy and the manufacturer's optimal production strategy under two different option contracts (that is, commitment-unidirectional call option contract and commitment-bidirectional option contract). The effects of demand uncertainty, production yield stochastic volatility, instant procurement price variability, and the price parameters’ changes of option contracts on the optimal operational strategies and performances of both participants are also analyzed. These results will be helpful for the manufacturers and retailers in the supply chain with seasonal products to make production scheduling strategy and procurement policy feasibly and scientifically.

Reviving demand-pull perspectives: The effect of demand uncertainty and stagnancy on R&D strategy

This paper looks at the effects of demand uncertainty and stagnancy on firms’ decisions to engage in R&D activities and the amount of financial effort devoted to these. The paper provides a number of contributions to the innovation literature: first, it adds to the revived debate on demand-pull perspectives in innovation studies by examining demand-related (lack of) incentives to invest in innovation. Second, it complements the literature on barriers to innovation by focusing on demand-related obstacles rather than the more frequently explored financial barriers. Third, it analyses whether experiencing demand barriers is a sector-specific feature. Firms active in high- or low-tech manufacturing or in knowledge-intensive or low-tech services might be more or less dependent on demand conditions when deciding to perform R&D. We find that uncertain demand and lack of demand are perceived as two quite distinct barriers. While the perception of a lack of demand has a marked negative impact not only on the amount of investment in R&D but also the likelihood of firms to engage in R&D activities, demand uncertainty seems, on the contrary, to represent an incentive to spend more in R&D, although only in low-tech sectors. We interpret this evidence in terms of the specific phase of the innovation cycle in which decisions to invest in R&D are taken. Sectoral affiliation seems to be playing a role only for demand uncertainty, supporting the conjecture that positive expectations on the presence of adequate market demand are a necessary condition to invest in R&D.

Optimization and Simulation of Collaborative Networks for Sustainable Production and Transportation

Complex and delocalized manufacturing industries require high levels of integration between production and transportation in order to effectively implement lean and agile operations. There are, however, limitations in research and applications simultaneously embodying further sustainability dimensions. This paper presents a methodological framework based on optimization and simulation to integrate aggregate optimized plans for production and multimodal transportation with detailed dynamic distribution plans affected by demand uncertainty. The objective function of the optimization model considers supply, production, transportation, and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission costs, as well as collaboration over the multimodal network. Bill-of-materials and capacity constraints are included. A feedback between simulation and optimization is used to plan requirements for materials and components. Computational experiments are based on realistic instances. Results demonstrate that the framework can be effectively used to analyze cost-CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission tradeoffs, effects of demand uncertainty, and collaborative distribution strategies on economic and environmental performance of the supply chain.