Big data analytics and demand forecasting in supply chains: a conceptual analysis

• Pages: 739-766
• Date: 14 May 2018
• DOI: https://doi.org/10.1108/IJLM-04-2017-0088
• Published date: 14 May 2018
• Author: Erik Hofmann,Emanuel Rutschmann
• Subject Matter: Management science & operations,Logistics

Big data analytics and demand

forecasting in supply chains:

a conceptual analysis

Erik Hofmann

Institute of Supply Chain Management, University of St Gallen, St Gallen,

Switzerland, and

Emanuel Rutschmann

Department of Analytics, Deloitte Consulting AG, Zürich, Switzerland

Abstract

Purpose –Demand forecasting is a challenging task that could benefit from additional relevant data

and processes. The purpose of this paper is to examine how big data analytics (BDA) enhances

forecasts’accuracy.

Design/methodology/approach –A conceptual structure based on the design-science paradigm is applied

to create categoriesfor BDA. Existing approaches from the scientific literatureare synthesized with industry

knowledge through experience and intuition. Accordingly, a reference frame is developed using three steps:

description of conceptual elements utilizing justificatory knowledge, specification of principles to explain the

interplaybetween elements, and creationof a matching by conducting investigationswithin the retail industry.

Findings –The developed framework could serve as a guide for meaningful BDA initiatives in the supply

chain. The paper illustrates that integration of different data sources in demand forecasting is feasible but

requires data scientists to perform the job, an appropriate technological foundation, and technology

investments.

Originality/value –So far, no scientific work has analyzed the relation of forecasting methods to BDA;

previous works have described technologies, types of analytics, and forecasting methods separately. This

paper, in contrast, combines insights and provides advice on how enterprises can employ BDA in their

operational, tactical, or strategic demand plans.

Keywords Europe, Analytics, Big data, Demand-influencing factor, Forecasting methods,

Retail supply chains, Design science research

Paper type Conceptual paper

1. Introduction

Retailers know a lot about end consumers, perhaps more than we know ourselves. In 2012,

for example, US retailer Target sent coupons for baby clothes to a high school girl.

Her father was not amused by this type of advertisement, decided to visit a local Target

store and requested to see the person in charge. The manager apologized for the

inappropriate advertisement then a few days later over the phone. However, the father,

seemingly embarrassed, revealed during this phone conversation that he had talked to

his daughter: “It turns out there’s been some activities in my house I haven’t been

completely aware of. She’s due in August”(Duhigg, 2012).

As market expectations, competition, and volatility arerising, retailers are exploring data

analytics to address new challenges and opportunities. Data analytics techniques not only

provide single companies with greater accuracy, clarity, and insight but also lead to more

contextual “intelligence”shared across all supply chains regardless of industry or sector.

1.1 Initial situation

The example of US retailer Target illustrates that retailers possess a wealth of information

about the market and their customers. This data is a valuable resource as it enables

companies to strengthen their intermediary position between manufacturers and consumers

(Zhan et al.,2016). If retailers employ analytics,data becomes a factor thatcreates added value.

The International Journal of

Logistics Management

Vol. 29 No. 2, 2018

pp. 739-766

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-04-2017-0088

Received 10 April 2017

Revised 1 December 2017

Accepted 5 December 2017

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

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Big data

analytics and

demand

forecasting

How informationis used to generate a competitive advantageis therefore strongly dependent

on the method, scope, and purpose of data processing (Hazen et al., 2014). For instance,

Amazon, a leading onlineretailer, successfully patentedwhat it calls “anticipatory shipping,”

a method in whichdeliveries are initiatedprior to the actual customerorder placement in order

to cut delivery time. Amazon will box and ship products to areas where it anticipates orders

will be placed. The packages may remain at a local hub or be sent to another hub near an

active customer that is about to order (Bensinger, 2014). Another example is Otto, a German

online and catalog retailer. The company underwent a cultural change and now employs

data-driven decision making with big data and business intelligence (BI). It uses predictive

analytics to reduce the return rates of fashion items (Clarke, 2013).

Enterprises apply different methods to overcome the challenges of forecasting product

demand (Hazen et al., 2016). Some methods are qualitative (e.g. market research or expert

estimations), while others are quantitative (e.g. time-series forecasts). Problems regarding

decision support are based on the use of specific methods and, in turn, are dependent upon

specific input data.

In order to provide practical insights of big data in this study, an approach to its

integration in demand forecasting is necessary. Unfortunately, the current literature is either

too vague, driven by the wishful thinking of practitioners and consultants, or too technical

and specific. Many scientific contributions focus on quantitative reasoning or mathematical

approaches (Chen et al., 2015; Silva and Reilly, 2014), which require expertise and prior

domain knowledge. There is still a lack of comprehensive frameworks enabling convenient

access to big data in academia and in practice (Hazen et al., 2016; Wang et al., 2016;

Akter and Wamba, 2016; Wamba et al., 2015).

1.2 Methodology

The paper at hand addresses the following research question:

RQ1. How can big data analytics (BDA) improve demand forecasting?

In the following, Whetten’s (1989) three key research questions are elaborated upon to stress

the relevance of this research and to describe the methodology chosen in this paper.

Why? First, there is no systematic overview of the available BDA techniques and their

relation to forecasting methods. Moreover, there is no generic conceptual framework to

illustrate how data and information relate to the decision problem and situation from a

systems point of view (Tan et al., 2015). Second, the literature seldom depicts the process of

generating insight from various types of data. Thus, until now, there has been no

comprehensivedescription of the mechanisms and applicationsthat relate to the supplychain.

Third, there is nomatch between types of BDA and forecasting methods although BDA uses

extensive data to support decision making in an accurate and generic manner. It is unclear if

BDA is able to substitute or complement existing forecasting methods. Fourth, demand

forecasts do not only affect short-term decisions, they also support medium- and long-term

decisions, such as routing or site location problems. Thus, combination of BDA and certain

forecasting methods makes it necessary to consider different time horizons.

To investigate the potential for big data to be applied to the supply chain (Wang et al.,

2016), we focus on the retail industry. As the intersection between manufacturers and (end)

consumers, the downstream part of the supply chain is traditionally characterized by a level

of uncertainty and the direct effects of demand planning on overall turnover (Richard and

Morgan, 2012). Retailers need to make decisions such as whether to introduce new products

(Zhan et al., 2016) and whether to open new shop locations (Fernandes et al., 2015).

The benefits of more accurate consumer demand predictions in relation to these decisions

are apparent. For example, enhanced predictions would improve replenishment forecasts,

directly affecting retailers’profitability (Chase, 2014). Inventory is an important driver of

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