# Measuring Impacts of Information Sharing on Perishable Product Supply Chain Performance

Natalia Iakymenko, Gunrid Kjellmark, Daryl Powell, Eivind Reke

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Natalia Iakymenko, Gunrid Kjellmark, Daryl Powell, Eivind Reke. Measuring Impacts of Information Sharing on Perishable Product Supply Chain Performance. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.526-533, 10.1007/978-3-031-16407-1_62. hal-05386133

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# Measuring Impacts of Information Sharing on Perishable Product Supply Chain Performance

Natalia Iakymenko<fnref n="1" />(✉), Gunrid Kjellmark<fnref n="2" />, Daryl Powell<fnref n="1" />, and Eivind Reke<fnref n="1" />

**Abstract.** Information sharing is important for the competitiveness of perishable product supply chains (PPSC). Current research, however, still disagrees if information sharing directly contributes to improved PPSC performance or if it is moderated by other factors. Additionally, few studies measure the impact of information sharing quantitatively. This study addresses these gaps by studying the impacts of information sharing on the performance of ready-mixed concrete supply chains. A single case study was carried out to address the research goal. The data from the case study were analyzed both quantitatively and qualitatively. The results have shown that poor information sharing in PPSC leads to excess waiting times at the ready-mixed concrete factory and at the construction sites. This, in turn, leads to frequent disputes between the two companies regarding the concrete quality.

**Keywords:** Perishable product supply chain · Information sharing · Ready-mixed concrete

## 1 Introduction

Information sharing is generally acknowledged as a contributor to organizational and supply chain competitiveness in perishable product industries because supply chain actors need timely and quality information to coordinate intra- and inter-organizational activities [1–3]. There are, however, still some disagreements as to the impact of information sharing on perishable product supply chains (PPSC) performance. Some studies show that although information sharing is essential, it does not necessarily directly lead to improved PPSC performance. Other studies discuss PPSC performance only qualitatively without measuring PPSC performance parameters [2, 3]. Such a perishable product as ready-mixed concrete is not discussed in the PPSC literature—the most studied products are food (vegetables, fruits, meat, etc.) and human blood. Ready-mixed concrete is an extreme example of a perishable product with a shelf life of up to 24 h. Demand for ready-mixed concrete is volatile due to frequent order changes from the contractors. This leads to re-plannings, delivery adjustments, and uncertain waiting times.

<footnote n="1">¹ SINTEF Manufacturing, S.P. Andersens vei 3, 7031 Trondheim, Norway
natalia.iakymenko@sintef.no</footnote>

<footnote n="2">² SINTEF Community, S.P. Andersens vei 3, 7031 Trondheim, Norway</footnote>
Hence, research investigating the impacts of information sharing in the concrete supply chain is needed. The objective of this study is to 1) measure the impacts of information sharing on concrete supply chain performance, 2) understand how information sharing affects the concrete supply chain performance.

This paper is structured as follows. First, we shortly present a theoretical background of information sharing in PPSC. This is followed by the research method and case description. We then present the results. The paper concludes with a discussion, conclusions, and suggestions for future research.

## 2 Theoretical Background

Perishable products are products whose quality deteriorates over time. They include vegetables, fruits, dairy products, human blood, etc. [4]. Ready-mixed concrete is a perishable product with a very short lifetime. After the concrete is mixed, there are between 1.5 to 24 h or so before it starts to lose its properties. Effective and efficient information sharing between a ready-mixed concrete supplier and a contractor is important to maintain on-time concrete deliveries, high product quality, and avoid waste.

Information sharing in PPSC can be defined as inter-organizational communication or the extent to which critical information is communicated among supply chain partners to improve the ultimate customer satisfaction [2, 6, 7]. Information sharing has been highlighted as an important means of reducing complexity and improving the performance of PPSC [1, 2, 5]. Ferguson and Ketzenberg [1] found that information sharing can reduce uncertainty in demand and supply, significantly improve service levels, decrease inventory levels, lower stockouts, increase product freshness, and greatly reduce product wastage. Shi et al. [8] showed that information sharing in PPSC helps in improving decision-making and minimizing costs across the supply chain.

However, the research is not in total agreement regarding the positive impact of information sharing on PPSC performance. For example, Peng et al. [9, 10] point out that the relationship between information sharing and PPSC performance is influenced by moderating factors that should be further investigated. The latest systematic literature review on PPSC done by Lusiantoro et al. [2] confirms that the relationships between information sharing and PPSC performance are currently unclear.

Given these findings, existing literature calls for more investigation into relationships between information sharing and PPSC performance.

## 3 Research Method

This study is carried out as a part of the research project aimed at developing solutions for improved on-site concrete casting in terms of time and money spent. This part of the study concentrates on the work of a ready-mixed concrete (RMC) supplier (Company A) and a contractor (Company B). It aims to measure the impacts of information sharing on the concrete deliveries. This requires an in-depth investigation of the fresh concrete deliveries and collection of historical data in the companies, which advocates for the selection of a case methodology [11, 12].
The study was done in two steps. First, quantitative data on concrete deliveries were collected and analysed based on the propositions from Company A. Next, the results of the analysis were discussed with the case companies to get deeper insight into the data analysis results, identify possible contingency factors affecting the results, and validate the results.

Quantitative data include information about each trip of the concrete truck to the construction sites: customer, construction site, concrete quantity, type of concrete, time the order is sent to production, production start and finish, a departure time from the RMC plant, arrival time to the construction site, unloading start and finish times, a departure from the construction site, and arrival to the factory. Company A collected these data with help of GPS, Transport Management System (TMS), tablets, and sensors that can identify when loading and unloading of concrete starts. All data is stored in Excel spreadsheets. To analyze the data, several propositions describing factors that possibly influence concrete deliveries were formulated by the researchers together with Company A. Data analyses were done to test these propositions.

The results of the analysis were then presented to Companies A and B at workshops. Several issues were discussed during the workshops. First, the companies validated the results by pointing out possible mistakes in the data. For example, one of the participants noticed that an unloading time of 1 min is not possible. Consequently, the researchers conducted additional data cleaning to weed out the data errors. Second, the companies provided possible interpretations of the results. Last, the companies discussed how the results of the analysis can be used for improved concrete deliveries in the future.

## 4 Case Description

The production of RMC starts by blending cement, aggregates such as gravel and sand, and water in a mixer. Ready concrete is then poured into trucks positioned under the mixer. Trucks cycle between the batch plant and construction site until the whole volume of ordered concrete is transported. As the order quantity of concrete is typically larger than the capacity of a single truck, several deliveries need to be scheduled to fulfill an order. The deliveries cannot overlap in time and the time between deliveries should be small.

The work between Companies A and B is challenging. Company B wants to be able to place their orders as close to the delivery time as possible. The timing of the needed delivery often remains uncertain because completion of prerequisite work (reinforcement, concrete forms, etc.) at the site is difficult to forecast reliably. Additionally, the volume of the deliveries is difficult to estimate because the actual site measurements can differ from the measurements based on drawings and plans. On the other hand, Company A tries to time its deliveries so that all projects are served on time. This is further complicated by the RMC properties. RMC is a perishable product and once the water has been added to the mix of dry materials, concrete has several hours until it starts to lose its properties and might jeopardize the strength and stability of the concrete. This leads to frequent disagreements about the timing and quality of the delivered concrete.
# 5 Results

Below we present the results of the data analysis and the interpretation of the results by the case companies. In total, eleven propositions were tested during the study, but in nine of these propositions, the results were either insignificant or deemed irrelevant for the information sharing. Hence, only two are presented here.

## 5.1 Time Intervals Between Concrete Deliveries in an Order

The proposition tested was that the time intervals between the last and second last deliveries ($t_n$ in Fig. 1) are different from the time intervals between other deliveries in an order. Company A noticed that time before departure of the last delivery in an order is often longer than the time intervals between previous deliveries in an order.

Figure 1 shows an example of an order which includes six deliveries.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Concrete deliveries in a single order and their denotations</figcaption></figure>

In total, 388 orders were analysed. The time between deliveries was calculated as the time between cars leaving Company A's plant. The results of the analysis show that in 268 out of 388 orders, $t_n$ was the longest of all intervals in an order, which corresponds to 69% of orders.

We also analysed how much longer is the waiting time before the last two deliveries (additional waiting time). For the orders where last or second last deliveries are the longest, we calculated the difference between the waiting time before the last delivery ($t_n$) and the average of the rest of the waiting times in the order ($\mu$ ($t_1, t_2 \dots t_{n-1}$)):

$$ Additional \ waiting \ time = t_n - \mu(t_1, t_2 \dots t_{n-1}) \quad (1) $$

Total additional waiting time for all orders is 166,5 h. This means 166,5 h are spent waiting for the last two deliveries to be shipped to the construction site. It is important to note that administrative time and orders with 3 and less deliveries are not included in our calculations, so waiting time could be higher.

We asked companies to interpret the results. Company A stated that information on the last deliveries are often not known in advance—Company B gives preliminary information that there will be delivery but does not give the precise quantity of the concrete to be delivered, or they might call and ask to change the delivery timing.
Company B stated that they need time to calculate exactly how much concrete they need in the last deliveries—initial calculation might differ from the actual built structure. The timing of the delivery might depend on the setting time of the concrete in previous deliveries.

## 5.2 Concrete Truck Waiting Times at Different Construction Sites

The proposition tested here was that the concrete trucks waiting times at the construction sites differ depending on the project. Company A noted that they often experience that their trucks spend more time at some construction sites, but they do not know the exact reason why.

The available data on concrete deliveries were divided by project/construction site and average truck waiting times for the different construction sites were calculated:

$$ \text{Average truck waiting time at a construction site} = \frac{\sum \text{Truck waiting time during each delivery}}{\text{Number of deliveries}} \quad (2) $$

Construction projects with more than 50 deliveries were taken into consideration. Table 1 shows the average waiting times the trucks spend at the different construction sites, standard deviation and number of deliveries.

<table><caption>**Table 1.** Average truck waiting time for different projects/construction sites</caption><thead><tr><th>Project #</th><th>Average waiting time before unloading at the construction site, min</th><th>Standard deviation, min</th><th>Number of deliveries</th></tr></thead><tbody><tr><td>1</td><td>2,23</td><td>3,89</td><td>64</td></tr><tr><td>2</td><td>5,11</td><td>13,65</td><td>95</td></tr><tr><td>3</td><td>3,79</td><td>6,24</td><td>219</td></tr><tr><td>4</td><td>2,03</td><td>76,73</td><td>348</td></tr><tr><td>5</td><td>4,62</td><td>9,53</td><td>97</td></tr><tr><td>6</td><td>2,65</td><td>6,98</td><td>51</td></tr><tr><td>7</td><td>4,05</td><td>8,57</td><td>165</td></tr><tr><td>8</td><td>3,7</td><td>6,54</td><td>159</td></tr><tr><td>9</td><td>4,34</td><td>6,95</td><td>77</td></tr><tr><td>10</td><td>6,54</td><td>12,45</td><td>142</td></tr><tr><td>11</td><td>2,74</td><td>4,24</td><td>53</td></tr><tr><td>12</td><td>4,83</td><td>6,58</td><td>76</td></tr><tr><td>13</td><td>3,61</td><td>6,59</td><td>414</td></tr><tr><td>14</td><td>4,57</td><td>6,88</td><td>61</td></tr></tbody></table>

(continued)
<table><caption>**Table 1.** (continued)</caption><thead><tr><td>Project #</td><td>Average waiting time before unloading at the construction site, min</td><td>Standard deviation, min</td><td>Number of deliveries</td></tr></thead><tbody><tr><td>15</td><td>1,77</td><td>3,39</td><td>53</td></tr><tr><td>16</td><td>6,94</td><td>16,7</td><td>66</td></tr><tr><td>17</td><td>3,54</td><td>7,25</td><td>54</td></tr><tr><td>18</td><td>0,98</td><td>2,61</td><td>51</td></tr><tr><td>19</td><td>4,35</td><td>6,01</td><td>179</td></tr><tr><td>20</td><td>4,73</td><td>8,59</td><td>170</td></tr><tr><td>21</td><td>6,05</td><td>8,48</td><td>61</td></tr><tr><td>22</td><td>5,92</td><td>9,83</td><td>190</td></tr><tr><td>23</td><td>6,35</td><td>13,05</td><td>479</td></tr><tr><td>24</td><td>4,8</td><td>8,14</td><td>172</td></tr></tbody></table>

The difference in waiting times for different construction sites varies considerably.

Figure 2 shows histogram of waiting time distribution for the analysed construction sites. As we can see, in most occurrences waiting time is between 2,88 and 4,78 min. Even bigger variance is in standard deviations, which can vary from 3 to 77 min. High standard deviations mean that some waiting times at separate construction sites differ considerably from the average at those sites.

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Distribution of trucks average waiting time at different construction sites</figcaption></figure>

We asked companies to interpret the results. Company B commented that truck waiting time might depend on what is being built (f.eks., several stories apartment building or infrastructure project), as well as the project team, its planning efficiency,
and communication between Company A and project teams. Both companies agreed that it would be valuable to conduct in-depth case studies in well-performing projects in order to learn from them. They also agreed that even though performance probably doesn't depend only on information sharing, it is still a big part of it.

# 6 Discussion and Conclusions

The results have shown negative impacts of poor information sharing on both RMC supplier (Company A) and contractor (Company B). The results have shown that poor information sharing in PPSC leads to excess waiting times at the ready-mixed concrete factory and at the construction sites. Company A is subjected to at least 166 h a year of time waiting to ship the last deliveries in an order. It is also subjected to highly variable waiting times at the construction site, which complicates their production and fleet planning. Due to the uncertainties in delivery precision, both companies are involved in the disputes regarding the product quality and responsibility for the deteriorated product. These findings are in line with previous research showing that information sharing influence PPSC performance. In previous studies, however, PPSC performance was measured in terms of inventory costs, stockouts and product wastage [1, 13]. We add to this research by measuring PPSC in terms of waiting times. For perishable product such as concrete with extremely short shelf life, waiting times are critical.

Finally, our research showed that truck waiting times at the construction sites vary considerably depending on the project/construction site and project team. This is in line with Peng et al. [9, 10] who argue that relationships between information sharing and PPSC performance are affected by moderating factors, and it is difficult to show the direct connection between information sharing and PPSC performance.

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