=Paper=
{{Paper
|id=Vol-2348/short01
|storemode=property
|title=Value of Smart Data for Supply Chain Decisions in a Data Rich, Uncertain World
|pdfUrl=https://ceur-ws.org/Vol-2348/short01.pdf
|volume=Vol-2348
|authors=Ganesh Sankaran,Martin Knahl,Guido Siestrup,Ismini Vasileiou
|dblpUrl=https://dblp.org/rec/conf/cerc/SankaranKSV19
}}
==Value of Smart Data for Supply Chain Decisions in a Data Rich, Uncertain World==
https://ceur-ws.org/Vol-2348/short01.pdf
Big Data, Warehousing and Data Analytics
Value of Smart Data for Supply Chain Decisions in a
Data Rich, Uncertain World
Ganesh Sankaran1,2, Martin Knahl1, Guido Siestrup1, Ismini Vasileiou2
1
Hochschule Furtwangen University, Robert-Gerwig-Platz 1, 78120 Furtwangen, Germany
2 University of Plymouth, Drake Circus, Plymouth, Devon PL4 8AA, United Kingdom
Abstract. Data-driven decisions are becoming increasingly relevant for supply
chains as traditional paradigms are being replaced with concepts and approaches
more suited for the advent of big data. However, the prevailing consensus is that
companies are struggling to cope with an overabundance of data, which presents
the following pertinent question: how to efficiently analyze data applying filters
of relevance and insightfulness to make effective decisions? There is currently a
lack of research focus on providing quantitative tools to do such analyses. This
paper, besides offering thoughts on decision-making uncertainty in a digital sup-
ply chain context, describes an approach to address the research gap. The ap-
proach (which involves developing a quantitative model) is further elucidated by
utilizing an example in the agricultural supply chain that illustrates how value of
data can be quantified by measuring the performance impact of insights delivered
using uncertainty reduction as the leverage.
Keywords: Supply Chain Management, Digital Technologies, Big Data Ana-
lytics, Uncertainty, Data Driven Decision Making, Value of Data.
1 Decision Making Under Uncertainty in Digital Supply Chains
In designing and analyzing supply chain processes, the theoretical frame of “hierarchy
of decisions” has often been used [1]. This view, that segments processes according to
scope and significance, into strategic, tactical and operational, acknowledges the piv-
otal role of decision making in Supply Chain Management (SCM).
The decision-making process in SCM, as also in the general sense, is crucially about
choosing a course of action by assessing alternatives and settling on one “that is most
likely to result in attaining the objective” [2]. In this way, the efficacy of the process
hinges heavily on the ability to parse and understand uncertainty in the states of the
world associated with the alternatives. This notion of uncertainty is at the heart of the
Organizational Information Processing Theory (OIPT) that posits uncertainty as the
disparity between information processing need and corresponding capacity, and links
it to process and organizational performance [3].
In the current environment characterized by supply chains readily embracing digital
technologies and transforming themselves into Digital Supply Chains (DSC), decision
making under uncertainty presents an apparent contradiction: the abundance of data
afforded by digital technologies would lead one to expect DSCs to be exploiting this
49
Big Data, Warehousing and Data Analytics
2
opportunity to achieve parity in the OIPT sense (between information processing need
and attendant capacity) and drive improved performance resulting in a higher utilization
of data. However, various studies show that most digital data that is captured is not
utilized [4] and less than 1% of unstructured data is analyzed at all [5]. Research in the
areas of digital transformation and Value of Information (VOI) offer up some clues to
clarify this contradiction.
Digital transformation is about innovating new business models and ways of value
creation and capture, focusing on the dual outcomes of customer engagement and inte-
grated digitized solutions. It is perhaps better understood by contrasting with a related
term – digitization, which on the other hand, is a narrower technology-centric view [6].
Not surprisingly, supply chains that focus on transformation perform significantly bet-
ter than peers [6]. On the other hand, a lack of transformation focus leads to unmet
expectations and such companies are apt to complain, as have six out of 10 respondents
in this survey of 3000 executives, of having more data than they can use effectively [7].
A related line of research inquiry concerns VOI in a big data context. Research into
Information Systems (IS) following IS economics tradition highlight the lack of tools
to quantify data and the need to address the challenge of “finding a way to quantify the
value of information that considers both insightfulness and risks” [8]. The two lines of
inquiry are linked, and the convergence is in the fact that supply chains that are trans-
formation focused are more likely to want to justify investments and therefore also want
to quantify value of data - and this is where this research aims to contribute.
2 Approaches for Measuring Business Value of Data
2.1 State-of-the-Art
Using a resource-based view, which holds that heterogeneity of organizational re-
sources is a source of value (as it differentiates a firm from competition), Melville et al.
[9] argue for consideration of competition and environmental factors to measure value
of data as they are seen to impact value. Higher the level of competition or industry
concentration, higher is the marginal product and, conversely, lack of competition cre-
ates slack resources leading to lower productivity [10]. Environmental factors or exter-
nal focus, on the other hand, is seen to enhance performance as timely and accurate
information regarding a firm’s external environment are preconditions for agility [11].
Besides several empirical studies that adopt a general view on the impact of data on
value and emphasize the link between data-driven decision making and firm output and
productivity (see [12, 13] for representative examples), there are also several studies on
particular problem instances. Ketzenberg et al. [14] assessed VOI in the presence of
uncertainty around demand, return, and product recovery delivering a key insight that
greater the uncertainty, greater is the VOI. Dunke and Nickel [15] incorporated for-
ward-looking information in supply chain planning and proposed an optimization
model that utilizes preview of future information with help of lookahead devices (e.g.
sensors) to transform an uncertain future into a certain one.
50
Big Data, Warehousing and Data Analytics
3
2.2 Need for Further Research
The discussion above points to a wealth of empirical studies and models for specific
problems. However, a general-purpose quantitative model with a normative character
(elaborated in 2.3) is lacking. In a review of 117 articles on the topic of research con-
tributions in this area, Viet et al. [16] had found that, in a supply chain decisions’ con-
text, there is disproportionate attention being paid to inventory whilst other areas have
received insufficient attention. They also report that the impact of new and innovative
data sources (e.g. sensor data) remains under-explored. In laying out a research agenda
for future information systems research, Abbasi et al. [8] call for research on the “value
of various data sources and channels in terms of quality of insights, enabling new ca-
pabilities, and quantifiable business value.”
2.3 Model Conceptualization
Before describing the proposed model, it is instructive to go over key model attributes
that were considered as prerequisites: (1) Quantitative: the overarching question calls
for the ability to measure the incremental value of insights from digital data. This ne-
cessitates a quantitative-based model that yields a numerical solution. (2) Predictive:
The model must emulate a decision-making process where the performance potential
of data-driven insights can be studied. This requires the model to embody predictive or
simulative capability. (3) Relevant: Zadeh’s principle of incompatibility holds that
complexity makes relevance and precision impossible to obtain simultaneously [17].
Therefore, the model needs to be built on a framework that lends itself to strike the right
balance. From a performance measurement perspective, it needs to be inclusive (one of
the key characteristics of a good performance measurement framework [18]) and not
predisposed to any specific supply chain strategy. For instance, both cost (primary fo-
cus for efficient supply chains) and agility (primary focus for responsive supply chains)
measures need to be supported. (4) Usable: as the key question being addressed most
interests supply chain managers, the model should, despite its quantitative rigor, include
a graphical component for the decision-making process to be analyzed visually as well.
The proposed model is grounded in the Approximate Dynamic Programming (ADP)
methodology [19] (also called reinforcement learning). It is an active field of research
that has a long history owing to its evolution from work done in optimal control theory
and stochastic approximation (dynamic programming and Markov decision processes).
ADP’s choice as the model’s underpinning is due to its suitability vis-à-vis prerequi-
sites set forth earlier and its effectiveness in addressing the class of problems typical of
the supply chain problem domain. One way to justify this claim is by noting the sub-
components of ADP and highlighting structural similarities between ADP and Supply
Chain (SC) problems. ADP problem formulation consists of policy, reward, value and
model environment. The solution involves an appropriate choice of policy, which is a
set of endogenous controllable variables (e.g. reorder point in SC) in the face of uncer-
tainty expressed by the model environment (exogenous information, for e.g., customer
demand in SC) to maximize cumulative rewards or value (e.g. global perspective in
SC). The approximate nature of ADP allows problems involving large state-spaces
51
Big Data, Warehousing and Data Analytics
4
(typical of SC) to be solved by using an approximation architecture. The approximation
architecture or the learning element allows better policies to be adopted as the system
learns to interpret the uncertain environment better and develops a more accurate pic-
ture of the (delayed) consequence of actions on value. For the proposed model, this last
aspect is crucial to modelling the recalibration of uncertainty due to infusion of digital
data. [20]
The model incorporates formalisms to represent key elements of uncertainty and
digital data. For this research, uncertainty is viewed as an empirical quantity [21] that
can be modelled as a probability distribution. Furthermore, a Bayesian view of proba-
bility is adopted (other view being frequentist) [21], which is suitable in this problem-
context of decision-making where beliefs about states of the world are conditioned on
all available information. Quantification of uncertainty is a relatively untapped aspect
in stochastic optimization literature [22] but will be an essential component in the
model as it impacts policy selection and consequently its predictive ability. In the case
of digital data, a semantic model (for example, based on W3C SSN ontology [23]) is
adopted that provides similar modelling rigor. Finally, for model visualization, System
Dynamics (SD) approach is the primary candidate [24]. SD provides an intuitive rep-
resentation of causal relationships between variables and their impact on performance.
2.4 Illustration of Model Aspects: Example in the Agricultural Supply Chain
The example pertains to the production and sales of seeds that starts with the production
stage (that involves sowing, growing, harvesting, treatment and packaging) and culmi-
nates in the sales of seeds to farmers. The problem of estimating yield is the focus of
the example and it helps elicit the salient model features.
Once sales projections are made, production is planned assuming a certain yield (us-
ing factors like crop physiology). However, this is at best a noisy or imprecise estimate
and the reality at harvest time tends to vary widely from projections. One key implica-
tion is the planning of treatment and packaging capacity, which is often a bottleneck. If
the capacity planned is insufficient, it leads to lost sales and higher than required ca-
pacity leads to poor utilization and impinges on profits. However, advances in digital
technologies provide the ability to use sensors and the like, which act as lookahead
mechanisms and can provide advance insights during the lengthy sow-grow-harvest
cycle, which can help revise noisy prior estimates with updated, sharper posterior esti-
mates. The dynamics of interaction are presented in Fig. 1. As can be seen from the
illustration, relevant sensor data (e.g. weather, water content) that are predictors of yield
when captured can be utilized to revise estimates and perform contingency planning in
the form of organizing additional subcontracting capacity or shaping demand (promo-
tions) to better match demand and supply. In this way, the proposed model emulates
decision making with and without insights from digital data to evaluate the impact on
metrics (e.g. backorders, capacity utilization). The key objective is to make the model
suitable for assessing investments (for instance by facilitating small-scale experiments)
by focusing on the potential for better decision making under uncertainty whereby re-
turn on investment can be calculated as a function of incremental value due to insights.
52
Big Data, Warehousing and Data Analytics
5
Fig. 1. An example of crop-seeds manufacturing and sales described in the text is illustrated. The
increase of a certain measure causes an increase (+) or decrease (-) of the connected measure.
3 Conclusion
An implication of wide adoption of digital technologies by supply chains is the increase
in decision-making complexity and uncertainty, which translates to a greater burden on
information processing needs and capabilities. This strain is apparent in various studies
that show that digital data is heavily under-utilized.
This paper proposed a quantitative-based model that assesses data in terms of its
insightfulness, thereby enabling supply chains to address the problem of under-utiliza-
tion and seeks to provide a means to evaluating digital data based on its moderating
influence on uncertainty and its impact on process performance metrics.
The focus of the next stage of research is resolving design decisions pertaining to
model conceptualization, which is followed by model development. The third and final
stage will be model solving that is supplemented with a case-oriented proof-of-concept.
References
1. Silver EA, Pyke DF, Peterson R (1998) Inventory management and production planning and
scheduling, 3. ed. Wiley, New York, NY
2. Simon HA (1977) The new science of management decision, Rev. ed. Prentice-Hall, Eng-
lewood Cliffs, N.J.
3. Galbraith JR (1974) Organization Design: An Information Processing View. Interfaces
4(3): 28–36
4. Manyika J, Chui M, Bisson P et al. (2015) Unlocking the potential of the Internet of Things.
https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-
of-things-the-value-of-digitizing-the-physical-world. Accessed 21 Jan 2019
5. DalleMule L, Davenport TH (2017) What’s Your Data Strategy?
https://hbr.org/2017/05/whats-your-data-strategy. Accessed 12 November 2018
53
Big Data, Warehousing and Data Analytics
6
6. Ross JW, Sebastian IM, Beath CM et al. (2017) Designing Digital Organizations - Summary
of Survey Findings. https://media-publications.bcg.com/MIT-CISR-Designing-Digital-
Survey.PDF. Accessed 12 Nov 2018
7. LaValle S, Lesser E, Shockley R et al. (2011) Big Data, Analytics and the Path From Insights
to Value. https://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-
to-value/. Accessed 28 Jun 2018
8. Abbasi A, Sarker S, Chiang RHL (2016) Big Data Research in Information Systems: Toward
an Inclusive Research Agenda. Journal of the Association for Information Systems 17(2)
9. Melville, Kraemer, Gurbaxani (2004) Review: Information Technology and Organizational
Performance: An Integrative Model of IT Business Value. MIS Quarterly 28(2): 283
10. Melville N, Gurbaxani V, Kraemer K (2007) The productivity impact of information tech-
nology across competitive regimes: The role of industry concentration and dynamism. De-
cision Support Systems 43:229–242
11. Tambe P, Hitt LM, Brynjolfsson E (2012) The Extroverted Firm: How External Information
Practices Affect Innovation and Productivity. Management Science 58:843–859
12. Brynjolfsson E, Hitt LM, Kim HH (2011) Strength in Numbers: How Does Data-Driven
Decisionmaking Affect Firm Performance? SSRN Journal
13. Yu W, Chavez R, Jacobs MA et al. (2018) Data-driven supply chain capabilities and perfor-
mance: A resource-based view. Transportation Research Part E: Logistics and Transporta-
tion Review 114: 371–385
14. Ketzenberg ME, Laan E, Teunter RH (2006) Value of Information in Closed Loop Supply
Chains. Production and Operations Management 15:393–406
15. Dunke F, Heckmann I, Nickel S, Saldanha-da-Gama F (2018) Time traps in supply chains:
Is optimal still good enough? European Journal of Operational Research 264:813–829
16. Viet NQ, Behdani B, Bloemhof J (2018) The value of information in supply chain decisions:
A review of the literature and research agenda. Computers & Industrial Engineering 120:68–
82
17. Lovell B (1995) A Taxonomy of Types of Uncertainty. Dissertations and Theses
18. Beamon BM (1999) Measuring supply chain performance. Int Jrnl of Op & Prod Mnagemnt
19(3): 275–292
19. Powell WB (2014) Clearing the Jungle of Stochastic Optimization. In: Newman AM, Leung
J (eds) Tutorials in operations research: Bridging data and decisions. INFORMS, Hanover,
Md, pp 109–137
20. Sutton RS, Barto AG (1998) Reinforcement learning: An introduction / Richard S. Sutton
and Andrew G. Barto. Adaptive computation and machine learning. MIT Press, Cambridge,
Mass., London
21. Morgan MG, Henrion M, Small MJ (1990) Uncertainty: A guide to dealing with uncertainty
in quantitative risk and policy analysis / M. Granger Morgan and Max Henrion with a chap-
ter by Mitchell Small
22. Powell WB (2017) A Unified Framework for Stochastic Optimization. https://cas-
tlelab.princeton.edu/wp-content/uploads/2017/09/Powell-UnifiedFrameworkStochasticOp-
timization_July222017.pdf. Accessed 30 Jan 2019
23. Ganzha M, Paprzycki M, Pawlowski W et al. (2017) Streaming semantic translations. In:
2017 21st International Conference on System Theory, Control and Computing (ICSTCC):
Proceeeings : October 19 - 21, 2017, Sinaia, Romania. IEEE, [Piscataway, New Jersey],
pp 1–8
24. Duggan J (2006) A Comparison of Petri Net and System Dynamics Approaches for Model-
ling Dynamic Feedback Systems
54