Literature Review: Forecasting Techniques in Supply Chain

This report presents a comprehensive literature review focused on supply chain forecasting. It begins by emphasizing the growing importance of supply chain management in today's business environment and the critical role of forecasting in enhancing supply chain performance. The review explores the significance of forecasting accuracy and delves into the impact of structured quantitative and qualitative forecasting techniques. It examines various methods and models, including trend analysis, seasonal adjustments, and collaborative approaches, as well as the importance of data and judgmental inputs. The analysis covers the benefits of improved forecasting, such as reduced uncertainty, enhanced planning, and increased efficiency, while also addressing the challenges associated with implementation, such as the need for substantial investment in resources and the potential for bias. The review synthesizes the views of multiple researchers and provides a foundation for understanding the complexities of forecasting in the context of supply chain management.

Chapter 2: Literature review
Literature Reviews Chapter

Chapter 2: Literature review
Dear Writer,
I have made few changes in the title and inserted my comments in red, In general the work is
almost done but just required some changes. You need to add a summary statement at the end of
each title not to be too long; it could be one statement just to reflect the connection with our
conceptual framework. Under the title of adopting quantitative and qualitative technique; you
need to add the views of a combination of both quantitative and qualitative technique could
yield better accuracy to be in line with the original research proposal. Finally, we need to add a
summary for the whole literature reviews which shouldn’t be long, just to summarize the pre-
developed conceptual theory/framework.
Here below the feedback of my supervisor on the conceptual framework;
“ The conceptual framework Develops a conceptual framework from which the
primary/secondary research instrument emerge
For example,
“According to the traditional attitude theory; consumer behaviour is predicted from consumer
attitude when consumers buy the brand, which they like the most. An attitude may be defined as
“acquire behavioural disposition” (Smith & Swinyard, 1983). However Adelaar et al. (2003)
explained that behaviour is produced by emotional response…….”
* Make a ‘link’ between existing literature and your own research and identify the knowledge
gap the research is to address.
* All the facts and relationships identified by reviewing literature need to be analyzed to derive
relevant variables/constructs for the research. The relationships identified, moderators or
mediators of such relationships need to be presented graphically in a meaningful manner known
as the conceptual framework. Such variables/constructs reflected in the conceptual framework
need to be aptly supported with literature.
* Conceptual framework is a critical element in the research from which the primary/secondary

Chapter 2: Literature review
research instrument emerges. Therefore, ensure that this is being derived logically with
sufficient literature support as this is among the must to do things in a research project.
To summarize, I believe we need to reflect the importance of the forecast accuracy in enhancing
the supply chain performance which could be achieved/improved by adopting a combined
qualitative and quantitative method under the viewed benefits and challenges.
Thanks,
Introduction
In the recent years, the importance of the supply chain in the performance of the business
organizations has drastically increased. There are several reasons such as the economic
globalization, technology development, growing consumer power and the global focus on the
sustainability. The supply chain forecasting and its accuracy has become essential in increasing
the performance of the supply chain. With the growing importance of the supply chain in
increasing the performance of the supply chain, the interest of the researchers has also grown in
this area. The present literature review will discuss the views pertinent to different authors who
have discussed role of forecasting in enhancing supply chain performance, the importance of the
forecasting accuracy, the impact of adopting structured quantitative and qualitative forecast
techniques in forecast accuracy and finally review benefits and challenges associated with
forecasting in manufacturing environment.
Supply Chain

Chapter 2: Literature review
The supply chain refers to the activities, processes and relationships which are present in the
manufacturing process and includes the material sourcing, product manufacturing and storing
through the process of logistics and manufacturing, and finally delivering the manufactured
products to the end consumer. In the manufacturing process, the supply chain is not a linear set
of activities; however, it comprises a complex set of processes, activities or relationships which
are essential in the manufacturing process (Rai, PAtnayakuni & Seth, 2006).
Forecasting
The forecasting is an act which predicts the business activities of the demand of a particular
commodity in the near future. The prediction is conducted based in the information available at
the present time. The supply chain is dependent on relationships which are developed during the
manufacturing of a specific product or service. The forecasting process works as a guidance for
the future business activities (Hyndman & Athanaspopolos, 2014). The forecasting can be
conducted by analyzing the previous years or the historic data. It is called quantitative method of
forecasting as it uses the previous year data or statistics to predict the changes in future. Other
than that, there are qualitative methods of forecasting too in which the experts use their
knowledge to predict the future trends. In order to attain accurate forecasting, a combination of
both the methods will be used. It can be used to plan the activities and establishing a link
between the upstream and the downstream activities.
Role of forecasting in Enhancing Supply Chain Performance
In the perspective of Gunasekaran, Patel, & McGaughey (2004), forecasting and the
product development lifecycle are important part of the supply chain. Forecasting is the method

Chapter 2: Literature review
of meeting the customer’s needs and demands in a timely fashion which impacts the supply
chain performance measures as they are all linked to the perceived customer value of the
product. Rotemberg & Saloner (1989) have discussed that the forecasting methods warrant that
that there is constant monitoring by the management and there is improvement in the
performance measures. Accurate forecasting prediction requires that there are cross-functional
teams, rapid prototyping and engineering approaches. According to the Hsu & Chen (2003),
there are several alternative methods which are used in the forecasting process; however, to
maintain the forecasting accuracy feedback of the previous activities must be used to modify the
forecasting instrument. Gunasekaran, Patel & McGaughey, (2004) has stated that the accuracy of
the forecasting methods can be improved by benchmarking them with the other methods. Other
than that, by integrating different production schedules, an organization can increase the demand
forecasting for different links in the supply chain. In the perspective of Taylor (2003) is also
important to increase the accuracy of the supply chain forecast as the accuracy is directly linked
with the performance of the supply chain. In the views of Chen, Drezner, Ryan & Simchi-Levi,
(2000) forecasting methods can also remove the uncertainties in the supply chain. The
benchmarking technique integration with other forecasting methods can give a better
understanding and accuracy.
According to McCarthy and Golicic (2001), strategic competitive advantage can be gained by the
business organizations if the forecasting techniques are integrated with the supply chain
performance. According to Taylor & Buizza (2003); it is important to create collaborative
relationships with the trade partners and other tiers in the supply chain to improve the forecast
accuracy. According to Lee, Padmanabhan & Whang, (1997), forecasting is a pivotal business

Chapter 2: Literature review
function which can improve the performance of the organization by disrupting the activities
related to planning, order and replenishing of the products.
The collaborative forecasting has the potential to increase the performance of the firms. The
literature of Lockamy & McCormack (2004) has discussed the importance of collaborative
forecasting by integrating customers’ planning into the manufacturing process and developing
supply chain metrics to increase the supply chain performance. Cachon & Lariviere (2001) has
highlighted the importance of a tool named, CPRF in the forecasting method. It combines
forecasting and collaboration between different members of the supply chain. CPRF
(Collaborative Planning, Forecasting and Replenishment) enhances the performance of supply
chain by supporting and assisting joint practices between different sections of supply chain. In
the views of Aburto & Weber (2007) forecasting tools can increase the efficiency, increase the
sales, reduce the assets, working capital and decrease the inventory associated with the supply
chain. However, Cachon & Fisher (2000) have stated that this forecasting method demands
reliance with other supply chain partners and requires timely and detailed information with the
trading partners. In the views of Lee & Billington (1992) forecasting tools can improve the
performance of the supply chain; however, it requires substantial investment in human and
technological resources. Recently, the alternative approaches can increase the responsiveness and
product availability of assurance of the organization.
It has been discussed by Devaraj, Krajewaki, & Wei (2007) that in the forecasting procedure,
several different processes are required. The companies must audit their internal forecasting
process before collaborating with different trading partners and better to work jointly on
demands planning. Bacchetti & Saccani (2012) have discussed that there are four components of

Chapter 2: Literature review
the trading partners, such as management, systems, techniques and the performance
measurement. In the forecasting process, the involvement of the senior management is important.
The training is important in boundary-spanning personnel forecasting. In the forecasting process,
the market intelligence is obtained. Stadtler (2005) have discussed market intelligence is
obtained from different sources, primary of which are the salesperson, purchasing managers and
the buyers. In the perception of Makridakis & Wheelwright (1977) marketing mix activities and
the perception of the customers, suppliers are important to understand the shaping of demand in
the near future. The information sharing between the trading partners can reduce the demand and
supply uncertainty in the future. The forecasting gives information regarding the future demand,
supply or the price of the manufactured products. Therefore, it is essential in the management of
the supply chain.
Forecasting Accuracy
In the perception of Fildes, Goodwin, Lawrence and Nikolopoulos (2009), forecasting
accuracy is very important in the planning process of supply-chain companies. In the supply
chain companies, the forecasting demand involves the computerized forecasting system which
can produce initial forecast and these forecasts can adjust the demand planning of the company.
It increases the accuracy of the forecasting system. The accuracy of the statistical forecasting
system can be enhanced when the experts adjust the forecast according to their judgment and
takes into consideration special events and changes in the statistical model. Stadtler (2005) have
discussed the judgmental adjustments can improve the accuracy of the forecast in the
manufacturing firms; however, it may introduce the bias in the forecasting. In the views of
Nenni, Giustiniano & Pirolo, (2013) forecasters make unnecessary adjustments in the absence of

Chapter 2: Literature review
reliable information which may hinders the accuracy of the forecast. In the views of Acar,
Yavuz, Gardner (2012) forecast adjustments made by the experts can yield better results. The
large judgmental adjustments in improving the accuracy of the forecast. Ali, Mohammad &
Boylan, John (2010) have discussed that there are several reasons for the efficacy of the large
adjustments such as large adjustments are applied when there is reliable information. In the
perception of Aviv (2001) small adjustments are usually less effective as the information on
which these adjustments are carried is considered as unreliable. According to Baumann
(2010/2011), human decision making is as such that they ignore the good advice and the
computer mediated advice and have excessive trust on their personal judgment. Boylan (2010)
has discussed that the many times, the users make adjustments to the predictions which decreases
the accuracy of the forecasting.
In the perception of Cachon & Lairiviere, (2001) forecasting accuracy is important in the supply
chain management and other organizational functions such as scheduling, resource planning and
the marketing depends on the accuracy of the organization forecast. According to Chen & Wolfe
(2011), the forecast accuracy is an important part in the delivery of the supply chain. Datta &
Christopher (2011) have discussed that the forecasting tools must capture the hard data as well as
the judgmental data to achieve accurate results. It is important to maintain accuracy in the
forecasting predictions as the organization will have to make orders to the suppliers or
manufacture the products according to the results of the forecasts.

Chapter 2: Literature review
Adoption of Structured Quantitative or Qualitative Forecast Techniques in forecast
Accuracy
In the perception of Derrouiche, Neubert, & Bouras (2008) quantitative forecasting methods are
widely adopted to support the company’s operations in the supply chain activities. According to
Durango-Cohen & Yano (2011), there are several techniques used for the quantitative
forecasting such as trend analysis, seasonal adjustments, decomposition, graphical methods,
econometric modelling and life cycle modeling. Ebrahim-Khanjari, Hopp & Iravani, (2012) have
discussed that the trend analysis is the method of forecasting the data when there is definite
upward or downward pattern for the forecast. In the perception of Ellinger, Shin, Northington &
Adams, (2012), uses several models for the forecasting such as exponential smoothing,
regression and the triple smoothing. According to Fildes & Kingsman (2011), seasonal
adjustment refers to the model in which the variation in demand in different seasons can be
identified. The adjustments are made in the baseline forecast so that the impact of the seasonal
demand can be identified. Fildes & Goodwin (2007) have discussed that the decomposition in
another method of forecasting in which the data is separated into three different sections, namely,
trend, seasonal and the cyclic data. Fildes, Goodwin, Lawrence & Nikolopoulos (2009) have
stated trend refers to the horizontal upward or downward movement with time. According to
Fildes & Hastings, (1994), trend can be a recurring demand pattern with some or no repetition.
The random is another set of data which comprises of the data in which no pattern can be
identified. Fildes, Goodwin & Lawrence (2006) have stated that forecast method can project the
patterns and can combine them to generate some relevant information. Forslund & Jonsson
(2007) that the quantitative forecasting method can be used to represent an objective picture of

Chapter 2: Literature review
the actual sales. The quantitative forecasting relies on the statistics and the sales or the demand
patterns in the previous years. Franses & Legerstee (2011) have stated that the quantitative
forecasting methods helps the business managers to focus on the recent data and the company
can spot trends which provide accurate sales and market forecast. In the perception of Ho &
Ireland (2012), there are several benefits of employing quantitative forecast methods in the sales
or the demand forecast. Huang, Hsieh & Farn (2011) have discussed that it can also temper
unwanted enthusiasm or falsified numbers provided by the employees. It can show the realistic
numbers and establish a reality check for the organization. It can also be used to generate or find
patterns for making more accurate projections with the help of number. In the perception of
Jonsson & Gustavsson (2008) quantitative forecasting methods are also beneficial in attracting
external stakeholders within the organization. The external stakeholders rely on accurate
numbers more than the enthusiasm of the people. The potential investors will also feel
comfortable with the forecast process.
According to Klatch (2007), qualitative forecasting methods is another reliable method of
forecasting for the demand and the sales. The qualitative forecasting methods are based on the
judgment and the opinion of the managers and the executives of the business organizations.
There are several methods which are used in qualitative forecasting methods, namely, executive
opinion, Delphi technique, Sales force polling and the consume surveys. Lau, Ho, & Zhao,
(2013) have discussed that the choice of the forecasting impacts on the product life cycle and the
decision-making of the organization.
LeBlanc, Hill, Harder & Greenwell (2009) have stated quantitative models are only applicable if
there is little to no systematic change in the environment. When the patterns or relationships

Chapter 2: Literature review
between different factors change, there is little to no systematic change in the environment. Liao
& Chang (2010) have stated that the objective models are of little use if there is a changing
relationship between different entities. However, the qualitative approach can be applied in these
cases. The qualitative approach is the approach which is based on the human judgment.
According to Mishra, Raghunathan & Yue (2009), the judgmental forecasting base the
forecasting on the existing trends and they are also possess a number of shortcomings. However,
the advantage of these forecasting methods is that they can identify the systematic changes more
quickly and can interpret the impact of these changes in a better manner. Morlidge (2014) has
discussed that judgmental forecasting tools are useful in short-term forecasting methods and can
supplement or support the projections which is established with any of the quantitative method.
According to Nikolopoulos & Fildes (2013) executive opinions refers to the forecasting approach
in which the executives from sales, production, finance or administration can generate an
accurate forecast about the future sales. The qualitative forecasting method can is feasible when
there is lack of feasible historic data (Require rephrasing). In the perception of Olhager (2013),
the Delphi method is a structured communication technique which establishes a forecasting
method involving interaction between different forecasting approaches and relying on a panel of
experts. The Delphi method is dependent on the principle that forecasting from a structured
group of individuals is more efficient than forecasting from unstructured group.
It can be summarized that a combination of both qualitative and quantitative forecasting methods
can be used to enhance the accuracy of the forecasting. Both of the methods are complementary
and can be used in combination to enhance the accuracy of the forecasting process.
Benefits and challenges associated with Forecasting in Manufacturing Environment

Chapter 2: Literature review
Oliva & Watson, (2009) have discussed that there are several benefits of forecasting in
the supply chain of manufacturing organizations. In the forecasting process in the manufacturing
companies, there are three types of forecasting, namely, demand forecasting, supply forecasting
and the price forecasting. Parks (2012), the demand forecasting, the companies search investigate
the demand of an object by the industry and the end users. In the supply forecasting, the
companies collects the data about the current producers and the suppliers. In the perception of
Ali, Mohammad & Boylan, John (2010), the supply demands are evaluated according to the
technological and the political trends which might affect the supply of the organization. The
manufacturing companies manufacture a product which is sold to the end users. Therefore,
determining the price of the manufactured products is also essential.
In the perspective of Aviv (2001), price forecast should provide a prediction of the short and the
long term prices of the products. There are several benefits of the forecasting in the
manufacturing industries such as increase in the customer satisfaction, reducing the stock-out in
the inventories and scheduling the production of the organization in a better and productive
manner. It has been discussed in the literature of Baumann (2010/2011) the manufacturing
industries, it is important to keep the customers satisfied, it is important to provide them, the
product or the services that they want. The forecasting in the business helps in the prediction of
demand so that the customer demands can be fulfilled in the shortest lead time. Another benefit
of the demand forecasting is reduction in the inventory stock-out. It has been discussed by
Cachon & Lairiviere (2001) manufacturing organizations, the companies work with different
suppliers and have a long lead time. If a business organization is buying from the companies
with the longer lead time, then demand forecast is important so that the suppliers can arrange raw