Information sharing in extended supply chains: why does it have to be so difficult?

Figures & data

Figure 1. Illustration of multi-tier versus dyadic information sharing.

Table 1. Contextual factors that influence information sharing in supply chains and related literature sources.

Contextual factors	Motivation	Source(s)
Demand uncertainty (High–low)	High demand uncertainty encourages the use of high levels of information sharing between the participating firms to create an effective response and avoid building large buffers and safety stocks.	Kim, Umanath, and Kim (2005), Grover and Saeed (2007), Yigitbasioglu (2010), Kembro and Selviaridis (2015), Li et al. (2019)
Environmental uncertainty (High–low)	High environmental uncertainty forces the supply chain members to share information to a larger extent to attenuate unpredictability and reduce the risk associated with the uncertainty.	Li and Lin (2006), Frazier et al. (2009), Yigitbasioglu (2010), Wong, Lai, and Cheng (2011), Hung, Lin, and Ho (2014)
Product complexity (High–low)	Highly complex product descriptions require more information transfer through the supply channel, while simple products have less need for information transfer.	Kim, Umanath, and Kim (2005), Grover and Saeed (2007), Welker, van der Vaart, and van Donk (2008), Dubey et al. (2020)
Position of decoupling points (ETO, MTO, MTS)	Complex business conditions, such as ETO and MTO, require greater sharing of external information since demand levels, product configurations, routings, etc. can be highly variable. In MTS operations, the finished goods inventory decouples the internal operations from customer demand, which reduces the need for external information sharing.	Welker, van der Vaart, and van Donk (2008), Van Donk and van Doorne (2016)
Demand variety (High–low)	A diverse product range and market presence that leads to high levels of demand variety encourages greater sharing of external information, while information sharing is not required for low demand variety.	Welker, van der Vaart, and van Donk (2008)
Supply chain complexity	The value of information sharing is higher in structurally complex supply chains, where multiple nodes, tiers, connections, etc. need to be managed.	Caridi et al. (2010)

Unit of analysis:.

- Extended supply chain: Caridi et al. (2010), Kembro and Selviaridis (2015), Van Donk and van Doorne (2016).

- Dyad: All other sources.

Decoupling points: ETO: engineer to order; MTO: make to order; MTS: make to stock

Table 2. Overview of the Delphi process.

Round	Purpose	Year	Data collection method	Output
1	Exploring the subject	2014	Open-ended questions in web-based questionnaire: 29 panel experts	Identified 14 contextual factors
2	Reaching an understanding	2015	Qualitative evaluations of the 14 factors in web-based questionnaire: 29 panel experts	12 factors moved forward to next round Identified the importance–feasibility dimensions
3	Assessing opinions	2018	Quantitative assessments of and comments on importance and feasibility for two contexts associated with each of the 12 factors: 20 panel experts	Identified the tensions between importance and feasibility Identified the importance–feasibility paradox
4	Explaining the paradox	2020–2021	In-depth interviews with 12 panel experts	Confirmed and explained the paradox
5	Assessing persistence over time	2021–2022	In-depth interviews with 17 experts: 10 panel experts and 7 new purposefully selected supply chain executives	Confirmed the persistence over time (before and during the COVID-19 pandemic) Provided more nuances to the paradox

Table 3. The twelve factors and their contexts identified in the Delphi study.

Aspect	Factor	Contexts
Product and market	Demand variability	Low–high
	Demand uncertainty*	Low–high
	Product life cycle stage	Intro, growth, maturity, decline
	Product complexity*	Low–high
	Product life span	Short–long
	Time-to-market	Short–long
Supply chain	Supply chain complexity*	Low–high
	Number of companies in the supply chain	Few–many
	Supply chain strategy	Lean, agile
	Lead times	Short–long
	Supply chain flexibility	Low–high
	Customer order decoupling point*	MTS, MTO, ETO

*Identified in previous research; see Table 1.

Table 4. The influence of contextual factors on the importance and feasibility of MTIS (7-point scale: 1 = ‘low’; 7 = ‘high’).

Factor	Context	Importance Mean (std. dev.)	Feasibility Mean (std. dev.)	Parametric t-test		Non-parametric Wilcoxon signed rank test
				t-Value	p Value	p Value
Demand variability	High	6.53^a (0.72)	3.82^j (1.02)	10.098	0.000***	0.000***
	Low	3.18^a (0.81)	5.00^j (1.12)	4.980	0.000***	0.002**
Demand uncertainty	High	6.29^b (0.85)	3.65^x (0.93)	9.791	0.000***	0.000***
	Low	2.94^b (1.20)	4.71^x (1.49)	3.273	0.005**	0.009**
Product life cycle stage	Introduction	6.18^c (1.13)	3.82^y (1.33)	7.099	0.000***	0.000***
	Maturity	4.29^c (1.10)	4.94^y (1.30)	1.611	0.127	0.125
Product complexity	High	6.18^d (0.81)	3.53^k (1.18)	6.746	0.000***	0.001**
	Low	3.59^d (1.37)	4.82^k (0.95)	2.697	0.016*	0.023*
Product life span	Short	5.59 (1.33)	3.47^l (1.46)	3.911	0.001**	0.005**
	Long	5.06 (1.25)	5.12^l (1.32)	0.108	0.915	0.782
Time to market	Short	6.24^e (0.75)	4.12^z (1.32)	5.840	0.000***	0.001**
	Long	4.53^e (1.46)	5.06^z (1.34)	0.940	0.361	0.360
Supply chain complexity	High	6.25^f (0.78)	2.94^g (1.57)	7.451	0.000***	0.001**
	Low	4.06^f (1.57)	5.56^g (1.26)	2.777	0.014*	0.019*
Number of firms in the SC	Many	6.12^m (0.70)	3.12^h (1.69)	7.141	0.000***	0.001**
	Few	5.12^m (1.36)	5.53^h (1.18)	0.835	0.416	0.247
Supply chain strategy	Agile	5.88 (1.31)	4.13 (1.20)	3.955	0.001**	0.004**
	Lean	5.69 (1.30)	4.44 (1.32)	2.712	0.016*	0.022*
Lead times	Long	5.88 (1.32)	4.29 (1.36)	3.002	0.008*	0.011*
	Short	4.94 (1.84)	4.31 (0.95)	1.213	0.244	0.227
Supply chain flexibility	High	5.25 (1.69)	3.88 (1.26)	2.112	0.052	0.063
	Low	4.88 (1.50)	4.63 (1.15)	0.436	0.669	0.658
Decoupling point	MTO	5.88 (1.15)	4.94 (1.48)	2.216	0.043*	0.059
	MTS	5.44 (1.36)	4.38 (1.71)	2.012	0.063	0.071

*, **, ***Significant difference at p < 0.05, p < 0.01 and p < 0.001, respectively (two-tailed test; comparing importance versus feasibility for a contextual variable and setting).

a–h, j–m, x–z: Significant difference at p < 0.001, p < 0.01 and p < 0.05, respectively (two-tailed test; comparing settings with respect to importance and feasibility, respectively).

Figure 2. Importance and feasibility of 16 contextual factors (8 factors × 2 contextual settings) that influence MTIS and regression line (scale: 1 = low; 7 = high).

Table 5. Key perspectives on the persistence of the paradox and supportive quotes.

Key perspectives	Representative quotes
The MTIS paradox exists.	‘I fully agree with the paradox; it puts words on what I have experienced in many projects over the years’. ‘The paradox holds. The more value, the more complexity; that is my experience’. ‘The more complex it is, the harder it is to implement, and the more risk is involved in such information sharing. For simpler flows it is easier to implement, but the value is low’. ‘For predicting demand of a very stable product, I don’t need MTIS. I probably don’t even need a calculator for that’.
MTIS pilots have been conducted, but they do not work.	‘It started ok, the producers went upstream in the supply chain. We talked critical business flows, processes, and standards, but suddenly, it was handed over to information systems and technology (IS/IT) and then became a technical integration—an XML project. Unfortunately, it died out, exactly for that reason’. ‘We started a supply chain in FMCG, with production in Switzerland, via a distribution center and out to store. We launched a new product, had no idea how much it would sell, so all in all a very complex situation. … Despite making significant efforts to establish partnerships and common goals, the initiative died out’. ‘We had bitter experience of this in the telecom sector, sharing information with companies that later became competitors’.
The respondents belong to the ‘unicorn’ camp or the ‘it must work, but somewhere else’ camp.	‘We have all been indoctrinated that collaboration is something to strive for and that it will solve a bunch of problems’. ‘I believe that it must work, somewhere else’.

Table 6. Four key difficulties for organizations to pursue MTIS and supportive quotes.

Key difficulties	Representative quotes from Delphi study panellists
Real-life extended supply chains are complex.	‘The complexity in MTIS increases with the number of actors and their different priorities; it is exponential!’ ‘With high supply chain complexity and number of actors, then a lot is required to make it work. We believe it will be easier to share with digitalization, but in practice that is not the case. That is a paradox. Everything is digitalized, so it should be easy. But it isn’t easy. You must use the same definitions everywhere, same item numbers, etc.’. ‘One must understand how this impacts each node. I must get the information into my own systems. The conclusion for MTIS was that: ok, this is great, but it doesn’t work’.
Firms focus on managing internal processes.	‘We focus 90% internally; we look at global supply structures, but the majority of these are still internal with multiple factories and distribution set-ups’. ‘Why are most companies focusing internally, investing in Industry 4.0. Why are these achieved, but not the supply chain projects? The answer is easy with insights into the paradox’. ‘Change, uncertainty, and complexity result in companies closing themselves and focus more internally to figure out how to manage the new circumstances—rather than looking externally at the rest of the supply chain. This is the paradox: when complexity increases, then the potential value increases, but at the same time we close ourselves and turn our attention internally, which makes it impossible to achieve MTIS’.
MTIS involves a multitude of risks.	‘It is about trust and the ability to protect yourself if the trust was wrong. Everyone knows that they can win by sharing information, but you could also lose by sharing. The risk is that you develop your own competition, so you want to protect your intellectual property. Today, managers are more sensitive to what type of data and information one shares externally, and then we are just talking about one tier in the supply chain’. ‘What is the willingness to accept risk entering a supply chain project, and then it fails? Is anyone willing to make that kind of investment in a risky project? It is more likely to go for smaller projects, but then you never reach the goal with MTIS. In other words, there is an element of calculating risk versus value in the chain’. ‘Risk sharing in the supply chain is central. Because often you have asymmetric risk, someone has more or less to win’.
MTIS is more than just a technical solution.	‘To share transaction data is easy, but when you try to optimize processes across multiple tiers; that is an exponentially increasing difficulty, which also covers the relationship aspect’. ‘The existing simple information that one could just retrieve is not what creates value because it is not that piece of information that we need to chase the bigger profits. To reach those, we need to share a different type of information, which isn’t transaction based’. ‘You need more than data; it requires something that helps understand and interpret the data. You need knowledge about the processes that generate the data and the processes that are influenced by these numbers’.

Table

Factor	Context	1. Importance							2. Feasibility
		Low -				- High			Low -				- High
		1	2	3	4	5	6	7	1	2	3	4	5	6	7
Demand variability	High	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Low	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Demand uncertainty	High	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Low	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Product life cycle stage	Intro	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Growth	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Maturity	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Decline	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Product complexity	High	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Low	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Product life span	Long	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Short	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Time-to-market	Long	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Short	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Supply chain complexity	High	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Low	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
No. of companies in the SC	Many	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Few	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Supply chain strategy	Lean	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Agile	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Lead times	Long	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Short	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Supply chain flexibility	High	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Medium	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	Low	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
Customer order decoupling point	MTS	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	MTO	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐
	ETO	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐	☐

Table A3.1. Overview of research quality criteria.

Criterion	How the criterion is addressed in this study
Credibility: Degree of match between the respondent constructions and researchers’ representation of the constructions	Prolonged engagement with Delphi panel experts (8years, including multiple times and triangulation of methods for data collection) to identify recurring and consistent patterns. Involvement of multiple researchers (investigator triangulation) with different backgrounds and expertise to reduce researcher bias and to enable reflexivity. Member checking by allowing the panellists multiple opportunities to consider other respondents’ answers, confirm their input, and add new insights. Terminology and focus of the study (MTIS) clearly explained to the panel experts at each step to avoid misunderstandings.
Transferability: Extent to which the study is able to make general claims about the world	Increased representativeness of the informants by following Delphi guidelines to select and describe a panel of highly experienced and knowledgeable experts (as described in the text).
Dependability: Stability of data over time	Setting up research protocols (in Word, Excel) to keep track of events, insights, analysis, and decisions over time. Repeating data collection of the same phenomenon over a long period of time. Using multiple data-gathering methods in the Delphi study to combine strengths of exploratory questionnaire, survey with seven-point Likert scales, and in-depth interviews.
Confirmability: The findings represent the results of the inquiry and not the researchers’ biases	Enabling auditing by documenting all findings and interpretations that can be traced throughout the research studies. All data considered by at least two researchers to minimize researcher bias.

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