Mathematical model to mitigate planning fallacy and to determine realistic delivery time

Yamini, S.; Marathe, Rahul R.

doi:10.1016/j.iimb.2018.05.003

Cited by 4 publications

(4 citation statements)

References 61 publications

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“…For instance, the decision to innovate a product or to rely on the support of ecosystem partners like VCs [25]. Interestingly, the dominant number of works in the aforementioned literature tends to work top-down [26]. The basic assumption made by researchers in this work assumes causal models for new venture success or failure.…”

Section: Assumptionsmentioning

confidence: 99%

Challenges in Data Driven Decision Making in the Early Stages of a Startup

2023

JCTCSR

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Entrepreneurship is usually considered a chaotic process paved with uncertainties. Without a method to the madness, entrepreneurship can become detrimental to the well-being of the entrepreneur. The early stage of a start-up refers to all important activities that help identify a potential business from an idea. This discovery process is characterized by stress and uncertainty which makes data-driven decision-making particularly challenging. Unlike mature phases (post-launch and product-market fit phase) where data is available in plenty, early phase decision-making depends on either limited data or generating one’s own data. Uncertainty gives rise to biases in decision-making. The two most negatively impacting biases are confirmation bias and self-serving bias. In combination, they affect the way an entrepreneur in the early phase interprets both data and the consequences of his/her decision. Through a qualitative interview-based study involving 30 entrepreneurs in the early phase and 10 entrepreneurs in the mature stage, this study focuses on recognizing the presence of these biases and proposes a habit-based process for grooming early-stage entrepreneurs. The scientific principles underlying the proposed framework have been detailed out and pragmatic solutions for improving early-phase decision-making have been derived.

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Section: Assumptionsmentioning

confidence: 99%

Challenges in Data Driven Decision Making in the Early Stages of a Startup

2023

JCTCSR

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“…Furthermore, it satisfies the “individual rationality” constraint of the retailer, i.e. this constraint requires the agents to prefer to participate in the contract than not to (Laffont and Martimort, 2009; Yamini and Marathe, 2018). The problem is solved in the backward direction by meeting the incentive compatibility and individual rationality constraints of the retailer.…”

Section: An Overview Of the Settingmentioning

confidence: 99%

Effect of manufacturer's flexible returns policy in a duopolistic competition

Yamini

Gajanand

2022

BIJ

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PurposeFlexible return policies are offered by the manufacturers to encourage the retailers announcing a lenient returns scheme to their customers.Design/methodology/approachThis study considers the distribution of durable products in a supply chain where the demand is sensitive to sales effort and retail price. Using a game theoretic framework, the paper presents an assessment of the strategic effect of flexible returns policy announced by the manufacturer under retail competition and highlights its implications on profitability.FindingsComparative analysis of monopolistic and duopolistic competition provides a better understanding about the repercussions and related facts on offering a flexible returns policy in these environments. It is profitable for the manufacturer to offer a flexible returns policy when there is retail competition than under monopolistic condition.Practical implicationsPractitioners view returns policy offered as an insurance given to the buyers and they infer it to be a better mechanism for doing business. Lenient returns policy promotes the sales by increasing the trust on the retailer and boosts up the perception of quality about the product by lowering the perceived risk for customers.Originality/valueEffective product return strategies such as being lenient in terms of time, money, effort, scope and exchange can result in increased revenues, lower cost and improved profitability to the manufacturer and retailer, at the same time offering an enhanced level of customer service.

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“…They collected a large sample of procurement data from 16 building construction projects, and cluster analysis and regression analysis were used. Furthermore, Yamini and Marathe [29] considered determining a realistic delivery time in the supply chain by reducing bias.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

2021

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This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center.

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Mathematical model to mitigate planning fallacy and to determine realistic delivery time

Cited by 4 publications

References 61 publications

Challenges in Data Driven Decision Making in the Early Stages of a Startup

Challenges in Data Driven Decision Making in the Early Stages of a Startup

Effect of manufacturer's flexible returns policy in a duopolistic competition

Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain

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