CASE STUDY

Demand Forecasting Helps a Healthcare Logistics Company Enhance their Demand Forecasting Ability by 80% – A Supply Chain Analytics Case Study by Quantzig

Aug 22, 2018

Demand Forecasting Methods

Demand forecasting is a systematic measure of the future demand for a service or a product. These methods are basically divided into two, namely, survey-based methods and statistical methods.

  • Survey-Based Methods: This is one of the most common and widely used demand forecasting methods. It encompasses the future plans and intentions of customers. Survey-based methods rely on customer surveys to identify the demand for an organization’s offerings as well as to anticipate its future demand.
  • Statistical Methods: Statistical forecasting methods are complex when compared with survey-based methods. Unlike other methods used to forecast demand, the statistical methods rely on the information obtained from industry forecasts along with cross-sectional and historical data obtained from several sources.

Quantzig offers demand forecasting solutions that enable the supply chain, sales, product, manufacturing, and finance teams to collaborate to create accurate demand and capacity plans in minutes instead of days. Request a free proposal to know more.

The Need for Implementing Demand Forecasting Techniques

Virtually every industry today is challenged by the dynamic nature of customer expectations, regulatory laws, market dynamics, and the ongoing advancements in technology – all of which are compelling factors for players across industries. Also, due to such factors companies are being forced to re-examine their demand management and demand forecasting techniques.

However, a major challenge in demand forecasting revolves around the identification of the right demand forecasting techniques to suit their needs. There are several demand forecasting techniques and there is no standard technique that empowers an organization to identify future risks. Hence, it is essential to keep abreast of the different demand forecasting techniques within a particular industry.

Engagement Overview

The client operating in the healthcare logistics sector wanted to leverage our expertise to identify the accurate demand forecasting methods for enhancing business efficiency. They were looking at devising effective analytics-based strategies to enhance efficiency and calculate optimum inventory levels. This engagement not only helped them achieve their set goals but also played a key role in identifying critical parameters for future success.

About the Client

A global healthcare logistics service provider. The client is a market leader in the logistics industry, specializing in the development, packaging, and distribution of unique healthcare products to meet the needs of healthcare companies.

Client’s Challenge

The healthcare logistics service provider was facing several challenges due to logistics disruptions at multiple inflection points. To tackle this challenge, the client wanted to evaluate the forecasting methods adopted by their peers and implement the best forecasting techniques to avoid adverse impacts on their bottom line.

Business Impact

The solutions offered by Quantzig enabled the healthcare logistics service provider to achieve a remarkable 80% improvement in their demand forecasting ability. The demand forecasting methods were devised using qualitative and quantitative information. Also, the adopted methods generated forecasts translated into greater visibility across business functions, improvements in profit margins, and enhanced efficiency and helped them identify balance points and inventory optimization techniques.

Get in touch with our analytics experts to gain in-depth insights into our healthcare analytics solutions.

Demand Forecasting Insights

The complexities associated with demand forecasting methods have bedeviled businesses for decades and the healthcare logistics sector is no exception. Today, businesses have access to vast amounts of data making the entire process of implementing demand forecasting methods much easier and accurate. Though such techniques have enabled businesses to improve profitability, it still remains a high-stakes guessing game based on software applications and analytics, both of which depend on simplistic business rules.

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