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The emergence of a pharmaceutical drug as a late entrant in a homogeneous category is a relevant issue for strategy implementation in the pharmaceutical industry. This paper aims to suggest a methodology for making pre-launch forecasts with a complete lack of information for a late entrant.

The diffusion process of the emerging entrant is estimated using the diffusion dynamics of pre-existing drugs, after an appropriate assessment of the drug’s entrance point. The authors’ methodology is applied to study the late introduction of a pharmaceutical drug in Italy within the category of ranitidine. Historical data of seven already active drugs in the category are used to assess and estimate ex ante the dynamics of a late entrant (Ulkobrin).

The results of applying the procedure to the ranitidine market reveal a high degree of accuracy between the ex post observed values of the late entrant and its ex ante mean predicted trajectory. Moreover, the assessed launch date corresponds to the actual date.

The category has to be homogeneous to ensure a high degree of similarity among the existing drugs and the late entrant. For this reason, radical innovations cannot be forecast with this methodology.

The proposed approach contributes to the still challenging research field of pre-launch forecasting by estimating the dynamic features of a homogeneous category and exploiting them for forecasting purposes.

Drawing on information processing view (IPV) theory, the objective of this study is to explore big data analytics (BDA) in pharmaceutical supply chain (PSC) for better business intelligence. Supply chain operations reference (SCOR) model is used to identify and discuss the likely benefits of BDA adoption in five processes: plan, source, make, deliver and return.

Semi-structured interviews with managers in a triad comprising pharmaceutical manufacturers, wholesalers/distributors and public hospital pharmacies were undertaken. NVivo software was used for thematic data analysis.

The findings revealed that BDA capability would be more practical and helpful in planning, delivery and return processes within PSC. Sourcing and making processes are perceived to be less beneficial.

The study informs managers about the strategic role of BDA capabilities in SCOR processes for improved business intelligence.

Adoption of BDA in SCOR processes within PSC is a step towards resolving the challenges of drug shortages, counterfeiting and inventory optimisation through timely decision. Despite its innumerable benefits of BDA, Australian PSC is far behind in BDA investment. The study advances the IPV theory by illustrating and strengthening the fact that data sharing and analytics can generate real-time business intelligence helping in better health care support through BDA-enabled PSC.

Demand foresting significantly impacts supply chain (SC) design and recovery planning. The more accurate the demand forecast, the better the recovery plan and the more resilient the SC. Given the paucity of research about machine learning (ML) applications and the pharmaceutical industry’s need for disruptive techniques, this study aims to investigate the applicability and effect of ML algorithms on demand forecasting. More specifically, the study identifies machine learning algorithms applicable to demand forecasting and assess the forecasting accuracy of using ML in the pharmaceutical SC.

This research used a single-case explanatory methodology. The exploratory approach examined the study’s objective and the acquisition of information technology impact. In this research, three experimental designs were carried out to test training data partitioning, apply ML algorithms and test different ranges of exclusion factors. The Konstanz Information Miner platform was used in this research.

Based on the analysis, this study could show that the most accurate training data partition was 80%, with random forest and simple tree outperforming other algorithms regarding demand forecasting accuracy. The improvement in demand forecasting accuracy ranged from 10% to 41%.

This study provides practical and theoretical insights into the importance of applying disruptive techniques such as ML to improve the resilience of the pharmaceutical supply design in such a disruptive time.

The finding of this research contributes to the limited knowledge about ML applications in demand forecasting. This is manifested in the knowledge advancement about the different ML algorithms applicable in demand forecasting and their effectiveness. Besides, the study at hand offers guidance for future research in expanding and analyzing the applicability and effectiveness of ML algorithms in the different sectors of the SC.

This study examines the role of intellectual capital disclosure (ICD) on earnings forecasting by analysts in the pharmaceutical industry in emerging countries, particularly in Indonesia, Malaysia and Thailand. This study specifically examines the role of each component of the ICD on analysts' forecasts, which consists of errors of forecasted earnings, the standard deviation of forecasted earnings and analyst recommendations.

Panel data analysis is conducted using a sample of 17 companies from pharmaceuticals industries in Indonesia, Malaysia, Thailand – Growth Triangle (IMT-GT), which are listed in the Indonesia Stock Exchange (IDX), Malaysia Stock Exchange (MYX) and Stock Exchange of Thailand (SET) from 2010 to 2017. Secondary data is obtained from Bloomberg and Annual report, where they are being analyzed to measure the ICD and gather the control variables.

The results indicate that the three components of ICD, namely human capital disclosure (HCD), structural capital disclosure (SCD) and relational capital disclosure (RCD), insignificantly influence average analysts' consensus recommendation and analysts' earnings forecast dispersion. However, the findings show a significant negative influence of relational capital disclosure (RCD) on analysts' earnings forecast error. In contrast, HCD and SCD have an insignificant impact.

Transparency in disclosing activities related to external parties is essential for the pharmaceutical industry. It is found that relational capital disclosure is the only ICD indicator that can strengthen analysts' profit predictions. Transparency about company activities in maintaining customer satisfaction and activities related to strategic alliances with other organizations are two critical things that can accommodate the accuracy of earnings forecasting from analysts in pharmaceutical companies.

This study contributes to ICD-related research by discussing the financial analyst's response to this voluntary disclosure in the pharmaceutical industry, particularly in Indonesia, Malaysia and Thailand. The selected observation period is seven years, starting one year after the global financial crisis. The results showed that the disclosure of IC is not an exciting thing for financial analysts. In forecasting current earnings, financial analysts are more interested in errors than the previous year's estimates.

This case looks at an important business task: forecasting a new product. The case can be used to teach finance, marketing (new product introduction), and healthcare strategy. The product is one of Amgen's most important new products: denosumab. On the surface, the case is fairly easy; students simply have to do some simple mathematical calculations. However, the challenges of forecasting quickly become apparent; every forecast depends on some critical assumptions, and the answer can vary dramatically.

Highlight the importance of forecasting as a business task. Give students the opportunity to create a forecast, using logical assumptions to generate an answer. Illustrate how dramatically forecasts can vary. Demonstrate why sensitivity analysis and customer understanding are both critical when trying to forecast a new product launch.

Integrated supply chain in pharmaceutical industry requires organized planning and modeling of each strategic element of pharmaceutical supply chain (PSC). The aim is to coordinate each activity of PSC and design a robust strategy to make the system hassle-free. Each activity of industry is interdependent and follows certain co-relations with each other. The paper focuses on the four most significant identified issues in PSC and analyses the weightages of these issues and their sub issues with respect to cost incurred and time taken to manage whole chain of supply.

Fuzzy analytical hierarchy process methodology has been applied to rank the issues which consume maximum time and/or costs.

The derived result shows that warehouse design and management (WDM) consumes more than one third of the total time and around half of the total cost. Other than WDM, process of supplier selection for procurement is second most time and cost consuming issue. The derived results are discussed and provided to the field experts of the area. The analysis will be useful for decision makers to economize PSC.

The study is limited to PSC; hence, result may vary with other practical situations.

This generated scope for further research on how to minimize the weightage of WDM for cost and time in PSC industry so that decision-makers can optimize the economic system of PSC.

The research is based on the field survey of India’s largest generic medicine distributing company in the government sector; hence, analysis has been performed on real situation.

This case is appropriate for use in undergraduate and MBA courses.

This case can be used in courses in business ethics, leading teams and organizations or business strategy. The focus of the case aligns well with discussions of managing up, navigating changes in top leadership and conflicts between executive vision and future company growth. Instructors that choose to emphasize the ethical approach could assign this case to explore tradeoffs between loyalty to current and future bosses.

Associate Director of Forecasting Cindy March faces a multi-faceted dilemma as biotech firm Veracity’s acquisition date by pharmaceutical giant Makhola approaches. After a new competitor enters the market, March expects Veracity drug Sangren’s future revenue to drop to $600m in 2019, but the outgoing Veracity CEO refuses to accept a forecast of less than $700m. March suspects that the CEO is intent on handing over a financially successful company and is overly optimistic about Sangren’s ability to maintain market share. In two weeks, March is due to present a 2019 Sangren forecast to incoming Makhola leadership, who she anticipates becoming her direct boss after the acquisition. Should March present the inflated forecasts and accept the poor reflection on her professional abilities or should she refuse to present numbers she does not believe in?

By analyzing and discussing the case, students should be able to:Evaluate the potential business and ethical conflicts arising from decision-making based on both data and intuition. Synthesize an appropriate strategy for navigating tradeoffs between current and future leadership.Analyze the gender dynamics of male-dominated executive leadership structures and strategies for female employees to combat gender biases.

The Behavioral Science Guys, 2015. One Simple Skill to Curb Unconscious Gender Bias. YouTube. https://www.youtube.com/watch?v=SEHi4yauhu8&ab_channel=VitalSmartsVideoTeaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

CSS 6: Human resources.

To highlight how vendor managed inventory (VMI) can be extended both upstream and downstream in the supply network to co‐ordinate the material and information flows among a number of different suppliers, manufacturing and distribution plants (“extended VMI”).

The research is based on data and information gathered during an in‐depth case study within the supply network co‐ordinated by GlaxoSmithKline, one of the world's leading research‐based pharmaceutical and healthcare companies.

Defines the peculiarities and the requisites of the extended VMI as to: the information flows supporting the relationships among the supply network members; the information systems, supporting the data collection, management, diffusion and elaboration; the performance monitoring system, highlighting the benefits for each supply network member as well as avoiding opportunistic behaviours.

The research intends to offer an original contribution to the stream of research on VMI, explaining the peculiarities and the requisites of the extended VMI. Future research should seek to consider the extended VMI in light of some supply chain management (SCM) practices which have emerged in recent years, such as collaborative planning, forecasting and replenishment. Moreover, a second research opportunity lies in investigating the mixes of SCM initiatives – among which is also the extended VMI – needing launch in a perspective of optimisation of the whole supply network.

The case reported here and the research findings should offer guidance for managers facing the decision‐making process concerning the implementation of the VMI both upstream and downstream in the supply network.

Most authors tend to consider VMI at the dyadic level, namely as an approach for managing materials and information flows between one or more customers and their immediate suppliers. Instead, this research adopts a supply network perspective, seeking to explain how VMI can be extended both upstream and downstream and considering the supply network as a whole rather than as a series of dyads.

Managing risks is becoming a highly focused activity in the health service sector. In particular, due to the complex nature of processes in the pharmaceutical industry, several risks have been associated to its supply chains. The purpose of this paper is to identify and analyze the risks occurring in the supply chains of the pharmaceutical industry and propose a decision model, based on the Analytical Hierarchy Process (AHP) method, for evaluating risks in pharmaceutical supply chains (PSCs).

The proposed model was developed based on the Delphi method and AHP techniques. The Delphi method helped to select the relevant risks associated to PSCs. A total of 16 sub risks within four main risks were identified through an extensive review of the literature and by conducting a further investigation with experts from five pharmaceutical companies in Bangladesh. AHP contributed to the analysis of the risks and determination of their priorities.

The results of the study indicated that supply-related risks such as fluctuation in imports arrival, lack of information sharing, key supplier failure and non-availability of materials should be prioritized over operational, financial and demand-related risks.

This work is one of the initial contributions in the literature that focused on identifying and evaluating PSC risks in the context of Bangladesh. This research work can assist practitioners and industrial managers in the pharmaceutical industry in taking proactive action to minimize its supply chain risks. To the end, the authors performed a sensitivity analysis test, which gives an understanding of the stability of ranking of risks.