Forecasting in Pharmaceutical Industry Using Artificial Intelligence: Current and Future Aspects
“Forecasting” The term broadly refers to the process of prediction as per the customer’s demand based on the huge historical sales data in the pharmaceutics industry. The aim of forecasting help to understand the market value and enable to predict the optimum level of customer demands. Thereby business management facilitates augmenting the future requirements from the previous sales quantity documents by considering both major and minor factors in a broad spectrum. This full-length Paper discusses the details of marketing, new product launch and specialized aspects such as orphans and bio-similar drugs. Artificial intelligence(AI) plays a strategic role to forecast the probable market requirements in advance for the industry and prepares to face future challenges. Forecasting could be multi-directional, application based on various approaches of the pharmaceutical industry such as Artificial neural network topology (ANN), Adaptive Network Based Fuzzy Inference System (ANFIS) which can be applied as a neuro fuzzy approach and proposed model approaches. This paper presents a detailed account on the key role of AI pertaining to the techniques that help the pharmaceutical industry supported by applications, illustrates, effectiveness and approach.
Keywords: Artificial intelligence, Artificial neural network topology, business management, forecasting techniques, pharmaceutical industry.
“Forecasting” is the process of using the pattern contained in huge historical past sales data to predict future values. Forecasts predict the future levels of sales, demand, inventories costs, imports, exports, and prices among others in the form of numeric. The aim of forecasting is to guide the management to plan requirements for the marketing effort, material, personnel, production and market shares of the competitive products and marketing conditions are assisted. Clear and well-prepared forecasts should be accurate enough to allow for better future planning and control could not be validated without the forecast. Demand forecasting is one of the main inputs when developing long-term strategic plans. It is a method of analyzing the past and current historical data to determine future values. Hence, forecasting is the making of predictions about the future performance based on past and current huge data. Forecasting is necessary because nowadays, health and treatment services are facing issues in the conditions of prosperity and democracy. Therefore the pharmaceutical industry can be assist as a strategic sector.
Forecasting are examined by the largest pharma market globally in the treatments for diseases such as rheumatoid arthritis and osteoarthritis, osteoporosis, carpal tunnel syndrome, tendonitis, rotator cuff tear etc. The segment accounted for 15% of the global total in 2017. Cardiovascular, oncology and anti-infective are drugs are the 2nd, 3rd and 4th largest markets.
Upto 2021, the fastest-growing segment of the global pharma market will be drugs for treating metabolic diseases, thyroid diseases and pituitary gland. This segment will grow at 10% a year going forward, the recent growth of 11.5% , but it will in 5th position for market size. The largest sub-segment of the global pharmaceutical industry are the anti-diabetic drugs worth over $85 billion in 2017; 2nd are the anti-virals and 3rd comes to the anti-hypertensives. Drugs for some of the less prevalent cancers like thyroid, skin, and ovarian cancer are the fastest-growing subsegments. The USFDA has allowed a less rigorous regulatory procedure and lower endpoint benchmark for cancer drugs, so increasing the rate of innovation.
It is one of the main processes of planning of forecasting in the pharmaceutical industry. It gives information about which products are purchased, when, where and in what quantities. By incomplete forecasting techniques, pharmaceutical manufacturers are affected. In a developed pharmaceutical market are seen on both valuable information data and market balanced power about products. In other sectors, obtaining the forecasting techniques’ by using terms & conditions, systematically sharing all available information and independently developing demanding scenarios from political terms and conditions with the greatest accuracy.
Fig2.1. Complex structure of pharmaceutical market
All pharmaceutical companies are in a close relationship with pharmacies (wholesalers & retailers), doctors, the pharmaceutical market, and patients. All pharmaceutical formulations should manufacture according to guidelines such as Food and Drugs Administration (FDA) and Goods Manufacturing Process (GMP). For the new challenges of the modern economy, pharmaceutical companies make a lot help to changes. For the new drug development manufacturing management and supply chain cause the effect. Pharmaceuticals companies will help the economy growth for many countries in the world. The population of the country and cure from a variety of medical conditions and benefits has helped the pharmaceutical industry an area of discussion.
For improvements of methodologies exist but in pharmacy straightforward, implicit and explicit assumptions of the direct human judgments and limited quantitative data. Two most common methods are the “Assumption Method” and “Morbidity Method”. It is based on the study such as historical data of past assumptions to predict future needs in human demand forecasting. In this study, a neuro-fuzzy approach was used for determining of next periods of the forecast.
This methods is mostly used in management science. Accurately predicting sales forecasting performance in useful in many contexts in pharmaceutical sector management. When pharmaceuticals marketing managers review historical sales data accurate predictions help them to distinguish between suitable and unsuitable forecasting for pharmaceuticals products. The failure to perform an accurate forecasting demand decision may result in an unsuitable forecasting demand being approached in the pharmaceutical industry. Since the quality of the pharmaceutical industry is mainly reflected in its research and training, the approaches to forecasting demand affect the quality level of the pharmaceutical market. Accurate predictions enable pharmaceutical marketing managers to improve forecasting sales performance by offering additional customer supports such as giving some extra offers on purchasing. Thus, accurate prediction of customers is one way to enhance quality and provide better sales services. As a result, the ability to predict customers’ purchasing performance is important for sales forecasting in the pharmaceutical industry.
Neuro-fuzzy and neural networks are set of theory, which help to soft computing techniques or tools of establishing intelligent systems. A fuzzy inference system (FIS) employing neuro-fuzzy if methods are containing adequate knowledge from human experts can deal with imprecise and vague problems. FIS widely used in the optimization of sales forecasting, control and system identification forecasting. Fuzzy systems do not usually learn and adjust themelves.
The artificial neural network (ANN) model is based on the biological nervous system,such as the brain working as information processing. They are loosely modeled after the neuronal structure of the mammalian cerebral cortex but on much smaller scales.
In other terms it is a simple mathematical model of the brain which is used to process the non-linear relationships between inputs and outputs in parallel like a human brain does every second. Artificial Neural Networks are used in a variety of tasks, a popular use is for classification. You can collects forecasting sales data for example of different years of sales data of pharmaceutical products and then train a neural network on the sales, then if you supply a new sales data of a pharmaceutical products it will give the new sales matches the models and then will output what sales of pharmaceutical products is.
Fig2.3.1. Artificial Neural Networks (ANN)
There are five main types of sales forecasting techniques for pharmaceuticals. These techniques are; Simple conjoint type models, Zipf’s law, Simple Elasticity Model, The Bass Model, and Simple Extrapolation.
This model is based on working of how innovative your products is relative to the competition on the product quality and performance. Extensive historical data research has shown that the very simplest conjoint models in forecasting sales in the pharmaceutical industry.
In 1940, an eccentric Harvard linguist was developed a law that is known as zipping’s law. According to that law it can predict unconnected things such as the size of the market in a city, the population of the city, number of hospitals, number of pharmacies and also pharma market shares that’s all things are helped in the development of pharmaceuticals marketing or sale.
If you any change your marketing investment i.e. 1%, how much will your markets share change? The answer gets this question is known as the marketing spend elasticity.
Did you know? The costs of pharmaceutical marketing behind brand drugs has a bigger impact on its sales forecasting than the costs of pharmaceutical marketing a generic class of medicines has on its sales.
This model predicts the speed of uptake for a new brand drugs or generic class drugs in pharmaceuticals marketing. It is based upon the fact that when new products are launched in a market. People are nervous about new products they tend to react what other people are doing. But when they will accept the product without thinking of how other people get on first.
It is based on the fact when no new competitors are about to launch, no major clinical trial results data to be reported and there is no more accurate way to forecast than to just based on the current and future aspects. It is also based on established pharmaceutical products in a stable market. It is simple extrapolation method for forecasting of sales.
Forecasting are influences many other functional areas within an organization. These linkages may be unidirectional or bidirectional. The links reflect the varied uses to which a forecast can be applied – such as revenue planning, production planning, resource allocation, project prioritization, partnering decisions, compensation plans, lobbying efforts, and so forth. These varied uses, and the effect of forecasting on many functional areas in an organization, reflect the first major challenge of forecasting – meeting the needs of varied and diverse stakeholders.
The link between forecasting sales revenue and unit volumes is an obvious one; however, the form of the forecast required may differ between these two. The unit volume forecast needs to include detail above that are required for forecasting demand – including information on sampling data, safety stock, and the distribution of products amongst the various packaging forms. These links should be bidirectional. In other words, the forecaster must understand the needs of the recipients of the forecast in order to select the best methods for generating the forecast.
In the most role of forecasting demand is to help in the development of business management in the pharmaceutical industry.
Forecasting should predict the number of product sales for each period of time in the pharmaceutical company. The case study given in the paper is based on a real-life example within the pharmaceutical field. The Paper presents review details on current and future aspects of forecasting in the pharmaceutical industry. And also discussed various approaches of the pharmaceutical industry such as Artificial neural network topology (ANN), Adaptive Network Based Fuzzy Inference System (ANFIS) applied as a neuro fuzzy approach, and proposed model approaches of forecasting in the pharmaceutical industry. Also, we discussed various factors affecting the parameters of forecasting in the pharmaceutical industry.
Review of Literature This section explores selected journal articles in relation to the research objectives outlined in the introductory part of the research paper. The articles are critically reviewed to determine the key components and factors in forecasting, the forecasting techniques and models discussed, proposed and applied and the challenges of implementation in warehousing forecasting demand management. Furthermore, the research approach and methodology used by the studies will be evaluated to assess the assimilation of information and knowledge development. Forecasting...
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