Vendor Managed Inventory in Fresh Foods:
A Sustainable Concept?
Marta Tišler
S2940787
Master Thesis
MSc Technology and Operations Management
June 20th, 2016
Preface
“The significant problems we face
cannot be solved at the same level of thinking we were at when we created them.”
- Albert Einstein
Writing this thesis was the final part of my journey towards graduation. In combination with the whole Master’s in Technology and Operations Management, I can say that the last year was full of personal and professional developments. Looking back, I am happy and thankful that I was able to experience every little moment of it, the difficult ones as much as the wonderful ones. None of this would have been possible without the inspirational and supportive people in my family. My sister, Nina, who believes more in what I can achieve than I do myself; my dad, Žaromil, who has always been a role model with his incredible knowledge; and my mum, Tatjana, who has taught me to always look after myself (first). Thank you for providing an environment to grow up safely, but still crazy enough to be different - hvala vam, VVVM.
Next, I would like to thank Paul Buijs for presenting this research topic so passionately in the first place, but also and foremost for his supervision throughout the last semester. Thank you for providing help and constructive feedback that allowed me to improve. I would also like to express my gratitude to Roel Post, who has provided me with the essential data and information to conduct my research and thus supported me in writing my thesis with a lot of input and time. Also, I would like to thank Bram de Jonge, who has taken time to give feedback and new insights as well.
Abstract
Because of its perishability, fresh food has a high chance of being wasted along the supply chain. A way to increase product freshness and decrease food waste might be to improve collaboration between supply chain partners in a fresh food supply chain and thus, quicken the handling of fresh foods.
Vendor Managed Inventory (VMI) has proven to be an efficient supply chain concept for decreasing lead times and safety stocks for non-perishable products. However, it has not yet been studied in fresh food supply chains. Hence, its implications on freshness along the supply chain are unknown. This thesis investigates whether applying VMI in a fresh foods context could improve the freshness of products and consequently be an interesting supply chain concept to help reduce food waste.
A case-based research approach with quantitative and qualitative methods is used in order to compare the performance outcomes before and after VMI was implemented between a fresh foods supplier and its largest customer. The research shows that the implementation of VMI had a significantly negative effect on the freshness of the delivered products, resulting in a higher risk of food waste.
Content
Preface ... I Abstract ... II
1. Introduction ... 1
2. Theoretical Background, Theory ... 2
2.1. Vendor Managed Inventory ... 2
2.2. Fresh Foods... 5 3. Methodology ... 7 3.1. Data Collection ... 7 3.1.1. Quantitative ... 7 3.1.2. Qualitative ... 8 3.2. Data Analysis ... 9 3.2.1. Quantitative ... 9 3.2.2. Qualitative ... 10 4. Findings ... 11 4.1. Service Level ... 11 4.2. Lead Time ... 11
4.2.1. Same Shelf Lives ... 12
4.2.2. Different Shelf Lives ... 12
4.2.3. Demand ... 12
4.3. Different sites ... 13
4.4. Freshness ... 14
4.4.1. Same Shelf Lives ... 15
4.4.2. Different Shelf Lives ... 15
5. Discussion ... 16
6. Limitations and Further Research ... 18
7. Conclusion ... 19
1. Introduction
Perishable products such as fresh foods can cost a company a lot of money if not taken care of appropriately. As indicated by Mena et al. (2011) “between 25% - 50% of food produced is wasted along the supply chain” (p. 648). This has a negative impact on both the companies facing waste and the environment. With its perishability, the fresh foods sector has very different characteristics compared to non-perishable fast moving consumer goods and thus demands different supply chain configurations. The fact that so much waste occurs along the food supply chain indicates that supply chains are not yet sufficiently efficient and sustainable to ensure the highest possible product freshness and keep food waste to a minimum.
A possibility to improve efficiency and reduce waste along the food supply chain is reducing the bullwhip effect (Lee et al. 1997) by improving collaboration between suppliers and customers and decreasing inventory. Better collaboration would give the supplier a clearer overview about actual demand, to which he could adapt production and replenishment. Implementing Vendor Managed Inventory (VMI), a supply chain concept that transfers more responsibility to the supplier, while order placing is eliminated, could be a way to do so (Disney & Towill 2003). In general, the main idea of VMI is to share accurate information about demand and stock levels between a supplier and a customer to try to optimize consignments in terms of timing and quantity and to eliminate storage buffer locations from the supply chain completely (Holweg et al. 2005). This results in improved collaboration between suppliers and customers (Holweg et al. 2005) and aims for more efficiency in terms of service levels, lead times and reductions in costs (Angulo et al. 2004; Tyan & Wee 2003; Yao et al. 2012). Still, VMI configurations differ depending on industry, size of involved parties and products (Elvander et al. 2007; Holweg et al. 2005). It has already been successfully implemented by different companies, such as Barilla, HP, Campbell Soup Company (Waller et al. 1999) and IKEA (Henningsson & Lindén 2005), however, these companies mainly deal with non-perishable products. Implementing VMI into the fresh foods sector has hardly been studied before and thus, little is known about the effects of VMI in a fresh foods context.
introduction of VMI in a fresh food supply chain are compared to each other. Thus, the main research question is defined as:
How does the introduction of VMI influence the freshness of perishable products in food supply chains?
Answering the research question helps to determine whether it is beneficial to implement VMI in the fresh foods sector. This research adds to current literature on VMI by addressing a setting that has hardly been studied before, i.e. fresh foods, and indicating potential factors that influence food freshness. In addition, it gives managerial insight by evaluating the effect of VMI on food freshness along the supply chain and gives insight into the supplier’s performance and the resulting effects for retailers in a fresh food setting.
The remainder of the thesis is structured as follows. Section 2 addresses the literature review of VMI and fresh foods and summarizes how VMI could help improve the handling of fresh foods. In Section 3, the methodology used is introduced and elaborated in detail, after which the study’s findings are presented in Section 4. Section 5 discusses the findings and relates them to literature, followed by limitations in Section 6 and finally, a conclusion in Section 7.
2. Theoretical Background, Theory
2.1. Vendor Managed Inventory
Within VMI, information about stock levels is shared continuously giving the supplier a better overview of demand patterns (Kaipia & Tanskanen 2003; Vigtil 2007). Another way to give the supplier detailed insight into actual demand is sharing point of sale (POS) data. Taylor and Fearne (2009) state that using electronic point of sale (EPOS) data facilitates adapting production to demand, which results in a better overall performance, with lower waste and inventory. According to Vigtil (2007) the importance of POS data depends on demand uncertainty and the supplier’s ability to react to that. This means that with higher uncertainty and a lower response time to changes, POS data becomes more important to the supplier. However, EPOS data can be quite overwhelming in its amount or not aligned with the suppliers’ IT system, resulting in suppliers not using it, despite its availability (Taylor & Fearne 2009). Therefore, it is important to adjust the data to the supplier’s needs and IT systems and apply proper algorithms for its usage (Ettouzani et al. 2012; Taylor & Fearne 2009). Furthermore, sufficient capacity, thus having a certain production flexibility, and short lead times facilitate the supplier’s ability to use the shared data and react to demand changes quickly (Ettouzani et al. 2012).
By gaining so many insights into actual demand patterns, the supplier is also assigned with certain responsibilities. Alongside the transfer of decision making and inventory management, the supplier is also responsible for demand forecasting and retail management (Tyan & Wee 2003). Further, Vigtil (2007) stresses the importance of an “advanced shipping notice”, which is an upfront notice about delivery by the supplier to the retailer and/or the distribution centre (DC). This gives the DC and/or the retailer the possibility to prepare resources and make replenishment more efficient, especially if warehouse operation capacity is highly utilised. These responsibilities give the supplier enough freedom to adapt their production processes to actual demand and to choose for themselves when and in what quantity replenishments take place (Rusdiansyah & Tsao 2005).
need a higher inventory level in order to reach a service level of 95 per cent. Even though VMI is supposed to increase productivity, Tyan & Wee (2003) also note that the implementation of VMI could result in loss of control for the retailer and decrease productivity.
2.2. Fresh Foods
Fresh foods differ from non-perishable products on various aspects. The most important distinction is the perishable characteristic of fresh foods that results in a shorter lifetime. Thron et al. (2007) claim that because of the unique characteristics that perishable products have, such as limited shelf life and fragility, supply chain concepts that aim for a higher collaboration can only be transferred to this sector in a limited way. This is because products cannot be held in stock for a long time, or in a high amount, as this would lead to deterioration and thus waste. Also, perishable products possess a “customised product expiration schedule”, as the authors refer to it, which determines when the product should be discarded. Thus, suppliers need to deliver the product quickly enough before it reaches its expiry day.
Obviously, the freshness of products with short lifetimes, such as vegetables, fruit, fish, meat or pastry, decreases quickly. Therefore, they should be transported fast and put into storage only for a limited amount of time in order to ensure highest freshness. However, when facing delivery delays, retailers are often forced to increase their safety stock, which further leads to a higher amount expired products. This is an important trade-off that has to be dealt with in a fresh foods supply chain. Retailers can choose to either offer a high availability by higher inventory but risk food waste, or choose for a more frequent delivery system (Göbel et al. 2015; Thron et al. 2007). However, different products are characterized by different shelf lives and thus might require different handling. Categorizing the products based on their shelf lives might be a possibility to elaborate on that.
Because demand for fresh products is so variable and difficult to forecast, retailers often over-order to be able to meet customer demand. However, in food supply chains without collaboration, this independent ordering behaviour and the resulting bullwhip effect leads to an even higher risk of food waste at each of the stages (Du et al. 2009). Mena et al. (2011) found that food waste can occur at any stage of a supply chain, while the reasons differ from stage to stage and from one product group to another (Göbel et al. 2015). Kantor et al. (1997) state that possible reasons could be inadequate forecasting and poor handling, transportation and storage. In addition to that, globalisation has led to supply chains becoming more complex (Skjøtt-Larsen et al. 2007) with more stakeholdersinvolved along the whole supply chain. For a food supply chain, this means that more stakeholders are involved in the process from farm to fork making it difficult to assess where most freshness is lost.
Improving communication between supply chain partners would reduce demand uncertainty at each stage and thus result in a better ordering strategy and better planned consignments (Vigtil 2007) by taking into account partners down- and up-stream of the supply chain. This would lead to fewer demand fluctuations and a lower safety stock (Xu et al. 2000), which could decrease the risk of waste. Going one step further would be to make the supply chain shorter and faster by removing one decision making step within the supply chain, which mitigates the bullwhip effect (Disney & Towill 2003; Yang et al. 2003). According to Holweg et al. (2005), deleting one decision making step and thus aligning the process allows supply chains to eliminate and/or merge storage buffers along the chain and gives the possibility to have less safety stock. Also, information flow time delays could be removed by improved information sharing between supply chain partners in terms of demand data and forecasting (Disney & Towill 2003; Mena et al. 2011). This could be especially interesting for the fresh foods sector as it reduces transportation time and time spent in storage and would bring products to the end-customer faster and in a fresher condition.
3. Methodology
The purpose of this study is to investigate how the introduction of VMI in a fresh foods sector influences product freshness. The research follows a theory building approach (Eisenhardt 1989) in order to find the key variables and the relationship between those by using multiple research methods that can be applied within a case study (Voss et al. 2002). Case research offers the possibility to observe VMI in fresh foods in its natural setting and full context (Eisenhardt & Graebner 2007) and thus is a good opportunity to apply an exploratory research method (McCutcheon & Meredith 1993). Using multiple research methods serves to increase the research’s validity and to aim for triangulation (Voss et al. 2002). Moreover, elaborating a case is a suitable method for investigating how and why questions and therefore fits the underlying research question (Voss et al. 2002). The research follows a deductive approach in identifying this relationship (Eisenhardt & Graebner 2007), using data from a fresh foods supplier of a large retailer who has recently introduced VMI into their processes. The data provided is historical, labelling the case as retrospective (Voss et al. 2002). Abundant data on both situations, before and after the implementation of VMI, is available. This makes it possible to investigate performance measures and compare both situations to each other. Looking into many different products allows better grounding for theory building (Eisenhardt & Graebner 2007) and increases the research’s validity. Because of the availability of this data, the possibility to apply explorative research, and its unique characteristic, this case was selected.
3.1. Data Collection
3.1.1. Quantitative
data before the implementation of VMI was aggregated to weekly levels, whereas the data set after the implementation of VMI was available on a daily level, the latter was adapted and cumulated to weekly levels as well. This data cleaning procedure resulted in a period of fifteen weeks in 2014/15 (before VMI implementation) and 2015/16 (after VMI implementation) and 138 products, which could be compared to each other.
Because the perishability of the supplier’s products differ in amount of days, they were categorized based on shelf lives. This should help elaborate whether VMI performance depends on shelf life. The categories made are based on the categorization scheme of the retailer and include the following three:
Ultra-Short products, with a shelf life of up to seven days (106 products)
Short products, with a shelf life between eight and twelve days (19 products) and Long products, with a shelf life of greater than twelve days (13 products)
3.1.2. Qualitative
To gather qualitative information on the case, content analysis of available documents was conducted and one formal conversation with the flow manager of the large retailer was held. In addition to that, multiple informal conversations were held with a principle informant. A principle informant is defined as a person who has knowledge about the provided data that is researched (Voss et al. 2002). Within this study, this was a researcher, who was well informed about the processes of the supplier and the retailer. The conversations with him were used to discuss possible data errors, findings, potential reasoning and further steps.
3.2. Data Analysis
3.2.1. Quantitative
The goal of analysing quantitative data is to explore whether the advantages of VMI also hold for the fresh foods sector and to determine whether VMI is a collaborative approach that can improve product freshness along the supply chain, which could further result in less food waste. To be able to test whether and what kind of influence VMI has on product freshness, adequate performance measures were determined. Based on the data to hand, three performance measures with which VMI performance could be measured were determined, namely: service level, lead time and freshness.
Service level
Because VMI is supposed to increase service level (Angulo et al. 2004), this performance measure was selected to be examined. It measures the performance of the supplier based on how much demand he is able to meet. It is calculated by dividing the amount that was delivered by the demand for a certain period and serves as an indicator for the quantity of the service.
Lead time
Considering the short shelf life of perishable products, lead time is a crucial performance measure. Reduced lead times, one of the goals of VMI (Angulo et al. 2004), result in shorter transport and storage times for products, and could thus be highly advantageous for perishable products. The measure is calculated by subtracting the remaining shelf life at order pick at the retailer (the time when products are picked from the inventory to be delivered to stores) from the shelf life right after production and serves as an indicator for the efficiency of the service. Because of the provided data, this performance measure could only be calculated for 104 products in the Ultra-Short category.
Freshness
With the implementation of VMI, freshness was introduced as a new key performance indicator (KPI) at the supplier. It measures how much of the initial shelf life is still left at the retailer’s order pick. This is calculated by dividing the remaining shelf life at order pick by the initial shelf life right after production and serves as an indicator for the quality of the service. For this research, the assumption is made that if lead time decreases, product freshness increases.
from the shelf life right after production, those changes would influence the resulting lead time. Therefore, both lead time and freshness outcomes were examined separately; once for those products that did not experience a shift in shelf life since the introduction of VMI and once for those that did. This approach was chosen in order to distinguish outcomes that were influenced solely by the introduction of VMI from those that were affected by other factors as well.
In order to determine whether the differences between the outcomes are significant, paired t-tests were conducted. For each product the average outcome in both situations, before and after VMI implementation, was calculated. The paired t-test was then used to determine whether the changes in outcomes between both situations were significant or not. In case there was a significant difference on a confidence interval level of 95 per cent, the specific category was investigated in more detail in order to find possible explanations for the result.
3.2.2. Qualitative
The formal conversation with the expert revealed that some products had a different supply flow before the introduction of VMI than after, whereas the supply flow for other products stayed similar in both situations. This change derives from the fact that the retailer has four regional distribution centres (RDC). Before, some products were delivered directly to these RDCs, where order picking was done. There was also a main distribution centre to which other products were delivered before they were forwarded to the RDCs. However, since the introduction of VMI, all products are delivered to a new, shared distribution centre (SDC), to which order picking was transferred. For those products that went directly to RDCs in the previous situation, this change in flow adds another step in their supply. According to the expert, these products should show the strongest difference in lead times whereas products that have a similar flow (instead of going to the former main distribution centre, they are now going to the SDC) would show approximately the same lead times. Based on this insight, it was decided to deepen the analysis and create a subcategory within each existing category that takes into account these two different supply flows.
4. Findings
Below, the main findings are presented for each of the fresh food categories using performance measures defined in Section 3.
4.1. Service Level
Category Before VMI implementation After VMI implementation Significant Yes/No Ultra-Short 92,1% 96,6% Y Short 98,8% 97,9% N Long 92,6% 96,7% Y
Table 1 - Outcomes service level
The findings show that the average service level for all products in the Ultra-Short category increases by 4,5 percentage points after the introduction of VMI. This increase in service level is significant
(
t(106) = -3,6, p < .001)
. Also the Long category experiences a significant increase in service level by 4,1 percentage points(
t(13) = -2,3, p < .043)
. However, the Short category shows a decrease by 0,9 percentage points after the introduction of VMI, which is not significant(
t(19) = 1,8, p < .097)
.4.2. Lead Time
Table 2 gives an overview of how many products were affected by a change in production and/or ingredients.
Category Shelf life – shorter/longer
Ultra-Short – 40 products 20 shorter 20 longer
Short – 7 products 2 shorter
5 longer
Long – 3 products 1 shorter
2 longer
4.2.1. Same Shelf Lives
Category Before VMI implementation After VMI implementation Significant Yes/No Ultra-Short 1,5 1,8 Y Short 1,7 2,0 N Long 17,0 29,0 Y
Table 3 - Average lead time in days, same shelf lives
Analysing the lead times for those products with the same shelf life in both situations, before and after the introduction of VMI, shows that on average, lead time for all categories increased since the implementation of VMI. However, only for the Ultra-Short and Long categories the differences are significant
(
t(64)= -6,7, p < .001; t(10)= -2,6, p < .027; respectively)
. The increase in lead time for the Short category was not significant on a 95% confidence interval level.4.2.2. Different Shelf Lives
Category Before VMI implementation After VMI implementation Significant Yes/No Ultra-Short 1,4 1,8 Y Short 1,6 2,2 N Long 10,6 8,7 N
Table 4 - Average lead time in days, different shelf lives
The average lead time for products that experienced a change in their shelf lives increased for the Ultra-Short and the Short category, whereas it decreased for the Long category. However, only for the Ultra-Short category the increase was significant
(
t(40)= -4,6, p < .001)
.4.2.3. Demand
4.3. Different sites
According to the flow manager, the increase in lead time should mostly derive from the change in supply flows that some products underwent since the introduction of VMI. Because of the additional step in their supply flow, it was assumed that these products would show the most increase in lead time. However, data shows the exact opposite, as can be seen in Table 5.
Supply Flow Category Before VMI implementation After VMI implementation Significant Yes/No Average Difference Similar supply flow Ultra-Short 1,4 1,8 Y +1,4 Short 1,3 2,0 Y Long 17,8 27,1 N New supply flow Ultra-Short 1,9 1,8 N +0,3 Short 2,3 2,1 N Long 3,4 9,0 N
Table 5 - Average lead times per supply flow
4.4. Freshness
The change in shelf life after production influences the remaining shelf life of products, as does the lead time. Table 6 shows how lead time for certain products whose shelf life changed, changed too. To make a clear distinction between whether freshness is influenced by the introduction of VMI only or not, this section is divided into two parts as well. The first elaborates the findings for those products whose shelf lives did not change; the second investigates the influences on products for which shelf life did experience a change.
Category and
amount of products Shelf life shorter/longer Lead time shorter/longer
Ultra-Short – 40 products 20 shorter 7 shorter 13 longer 20 longer 20 longer Short – 7 products 2 shorter 1 shorter 1 longer 5 longer 1 shorter 4 longer Long – 3 products 1 shorter 1 shorter 2 longer 2 longer
4.4.1. Same Shelf Lives
Category Before VMI implementation After VMI implementation Significant Yes/No Ultra-Short 74,4% 69,7% Y Short 80,6% 77,6% Y Long 77,4% 60,5% Y
Table 7 - Freshness, same shelf lives
With lead time increasing, the product freshness is negatively influenced and thus decreased. All categories show a decrease in average freshness, which is significant (ultra-short: t(64)= 6,9, p < .001; short: t(12)= 2,3, p < .042; and long t(10)= 3,5, p < .007).
4.4.2. Different Shelf Lives
Category Before VMI implementation After VMI implementation Significant Yes/No Ultra-Short 79,1% 71,2% Y Short 82,0% 75,5% N Long 82,5% 78,0% N
Table 8 - Freshness, different shelf lives
The Ultra-Short category shows a decrease by 7,9 percentage points, which is significant
5. Discussion
By introducing VMI into the supply chain processes, the retailer aims for the following goals:
Higher service level Shorter transport times Higher freshness and Less safety stock
Because the provided data did not offer any information on safety stocks, it was not possible to look into this goal, however, the data did give insights into whether the remaining objectives were met. Lead time was used as a performance measure to look into how transport times are influenced by the introduction of VMI. In fact, the research findings show that the introduction of VMI did influence service level, lead time (approach to analyse transport times) and product freshness.
For two out of three product categories (the Ultra-Short and Long), the goal of a higher service level was achieved. Thus, after the introduction of VMI, relatively more demand was met, indicating that VMI is advantageous. This might indicate that the supplier succeeds in incorporating demand information into his processes to increase service level and thus supports the findings of Vigtil (2007) that collaboration improves consignments. Still, the Short category experienced a drop in service level. With the data to hand, it was not possible to relate this drop to a specific cause. It might derive from certain products whose service level fell substantially or whose production schedule is strongly influenced by the change in order picking times. Further research should be done to investigate why the Ultra-Short and Long categories increase in service level whereas the Short category does not.
freedom to decide about replenishments in terms of time and quantity, retailers lose control of the processes and risk a lower productivity.
The increasing lead time results in a drop in product freshness. This is supported by the research findings as the average product freshness decreases for each category. On the one hand, this can be related to the same reasons like lead time; the longer dock-to-stock time results in an increase in lead time which leads to products arriving less fresh. On the other hand, the rescheduling of order picking from 10pm to 6pm influences freshness as well. Because order picking is moved four hours earlier, it is difficult for the supplier to deliver products on time. Therefore, some products are produced too late to be picked and will only be delivered one day later, leading to lower freshness. This issue could be solved by adapting the production schedule to the new order picking times. This has not been done so far and shows that suppliers have difficulties in providing a certain flexibility in production (Aviv 2007) and establishing an appropriate production-distribution plan (Niknamfar 2015).
For products whose shelf life after production shortens, the rise in lead time has an even stronger negative influence on freshness, because it is negatively influenced on two levels. Firstly, the time those products can be consumed is shortened, and secondly, with a longer lead time, an essential part of the shelf life is spent on transportation. Therefore, products reach end consumers in a less fresh condition, increasing risk of food waste. Similarly, the shelf life can be extended, which is one way to decrease food waste along supply chains (Broekmeulen & van Donselaar 2016), however, if lead time increases, a longer shelf life does not lead to higher product freshness. Therefore, the effect on the freshness of products with longer shelf lives cannot be determined accurately. Nevertheless, longer lead times result in lower product freshness for customers and thus proper measures need to be taken to overcome this negative outcome.
fulfil demand. This might explain why these products hardly show any increase in lead time. However, further research is suggested to determine the exact reasons for this behaviour.
To summarise, implementing VMI in the fresh foods sector did not help to meet the retailer’s goals. Apart from increased service levels, the findings were rather negative, such as increased lead times, thus longer transport times, and lower product freshness. Furthermore, this study supports literature in showing that incorporating information into internal processes is difficult (Holweg et al. 2005). It needs to be noted that the introduction of VMI in this case came along with more fundamental changes, such as the earlier order picking, to which production has yet to adapt. The negative outcomes could be related to this misalignment and to the bad planning of truck arrivals.
Investigating the changes in appropriate performance measures since the introduction of VMI at a fresh foods supplier helped to determine the effects of VMI on product freshness along the supply chain. This approach offered first insights into which aspects and product characteristics need to be considered when implementing VMI in the fresh foods sector. Nevertheless, a more detailed analysis on product level in combination with an investigation of the production schedule might give more information about the processes and define those that offer most improvement opportunities to ensure fresher products. This could further help to distinguish between different influences, such as inventory pooling compensating for increased dock-to-stock time. These explanations could not have been elaborated in this research because it would have required more detailed data of multiple stakeholders involved in the farm to fork process.
6. Limitations and Further Research
7. Conclusion
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