On the quest for supply chain transparency through Blockchain: Lessons learned from two serialized data projects

The ability to look into the supply chain has long enticed SCM scholars and practitioners. The possibilities created by such visibility are endless—from risk reduction and continuity planning to inventory management and cost reduction, nothing is off the table when end‐to‐end visibility is a possibility. Because of such enticements, there is usually much buzz in the industry every time a new technology that promises visibility and transparency is brought forward. Yet, years later, stories sometimes emerge that said technologies either failed to deliver or were not everything they were made out to be. Blockchain is yet another emerging technology in this space. Some consultants promise that it will be the final answer to the transparency and visibility woes that companies currently face. Yet, there is little empirical investigation regarding how the technology may benefit adopters, what the bottlenecks may be, and to what extent it may be able to deliver on these promises, without massive system‐wide upgrades of extant hardware and computing prowess. The current study takes a step in this direction by investigating a blockchain‐driven proof of concept across an industry consortium to identify promises, possibilities, and challenges of blockchain.     

Wavelet Analysis of the Bombay Stock Exchange Index

The fluctuations in the rate of returns of the Bombay stock exchange are analyzed through wavelet transform. The fluctuations, in various time scales, naturally separated by the wavelets, are subjected to statistical analysis. The localization and multiresolution properties of the wavelets enable one to identify collective behaviour in the stock market and the extent of their influence at various time scales. The Gaussian nature of the rate of returns at certain scales and the periodic nature of the same, at other scales, are clearly brought out by this analysis. The utility of this approach for modeling purpose is also elucidated.

     

OPTIMAL FORECAST COMBINATION UNDER REGIME SWITCHING*

This article proposes a new forecast combination method that lets the combination weights be driven by regime switching in a latent state variable. An empirical application that combines forecasts from survey data and time series models finds that the proposed regime switching combination scheme performs well for a variety of macroeconomic variables. Monte Carlo simulations shed light on the type of data‐generating processes for which the proposed combination method can be expected to perform better than a range of alternative combination schemes. Finally, we show how time variations in the combination weights arise when the target variable and the predictors share a common factor structure driven by a hidden Markov process.     

THE RELATIONSHIP BETWEEN THE BLACK MARKET AND OFFICIAL EXCHANGE RATES: AN EXAMINATION OF LONG-RUN DYNAMICS IN INDIA

This study is the first to use Johansen's cointegration approach for India in the analysis of the long‐term dynamics between the black and official exchange rates for the period 1953–1993. The study also estimates the long‐run elasticity of the official rate with respect to the black market rate. As monthly data over 40 years are used, and a more robust methodology is employed, the results are likely to be more reliable as compared with the earlier work on India. The results of our study suggest that while there is a long‐term relationship between the two rates, the direction of causality is from the black rate to the official exchange rate. This is plausible in the Indian context where policy has generally lagged behind events in the black market. The hypothesis of a constant black market premium is rejected, implying that there is a mismatch between the percentage change in the official exchange rate and the percentage change in the black market rate.     

Applying Burke’s Dramatic Pentad to scenarios

Describing the evolution of possible futures in the narrative form is widely practised. However, while stories are intuitively recognized, the ability to compose a comprehensible story is often difficult. Ambiguities or inconsistencies within a scenario-as-story are not simply aesthetic faults, but are failings which can weaken the use of the technique in a learning or decision-making process. This paper provides a working understanding of narrative as a particular kind of knowledge and proposes an adaptation of Kenneth Burke’s Dramatic Pentad as a way to explicitly capture this kind of knowledge within a scenario construction process.     

Price determinants of bred heifers: Do reputations matter?

Replacement brood cows are among the most significant investments for cow-calf operations, thus crucial to profitability. Many cow-calf producers find it cost effective to purchase replacements from a reliable replacement heifer seller, though by doing so they increase risk of reproductive inefficiency due to unknown characteristics of the heifers. When important information about a product is missing to buyers, a seller can build a reputation over time that acts as signal for quality. Previous work has explored reputation effects in feeder cattle markets, but to our knowledge we are the first to explore reputation effects in bred replacement cattle markets. Using data from an annual replacement heifer sale, we analyze the values of heifer characteristics and test for premiums from reputation development. After controlling for reproductive practices, breed, and other characteristics, we find reputation does not play the role that Shapiro theorized. In this sale, the lot order is strategically chosen and may indicate bred heifer quality to buyers, replacing the need for reputation as a signal. This study highlights the importance of quality signals and regional preferences in bred replacement cattle marketing and lays the empirical groundwork for future studies to test Shapiro's theory.     

Knowledge Management in Supply Chains: The Role of Explicit and Tacit Knowledge

We theorize, building on the knowledge‐based view and the theoretical distinction between explicit and tacit knowledge, that knowledge management capability across the supply chain manifests itself in explicit and tacit knowledge, which in turn effectuates supply chain performance. The model is tested with survey data from 195 small‐ and medium‐sized enterprises reporting on their primary supply chain. The results indicate that the supply chain's knowledge management capability manifests itself in both explicit and tacit knowledge, with the latter being influenced more strongly. Moreover, it was found that while both explicit and tacit knowledge influence supply chain performance, the latter exerts a significantly greater impact than the former. Exploratory post hoc analyses add robustness to these findings and investigate mechanisms inherent to the transformation of tacit into explicit knowledge. Overall, this research contributes to academic theory development in logistics and supply chain management by the dichotomization of knowledge types and the demonstration of their differential magnitude of effects, and to managerial practice by providing important guidance for logistics managers structuring their knowledge management efforts across supply chains.