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Master’s Thesis
The influence of product information on a
firm’s inventory strategy shortly after the
product launch
Supply Chain Management
University of Groningen, Faculty of Economics and Business
Author: Sieuwke Elisa de Jong
Student Number: S2723727
Supervisor: Prof. Dr. K.J. Roodbergen
Co-assessor: Dr. Ir. S. Fazi
Date: 28-01-2019
Groningen
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Table of Contents
Abstract ... 3 1. Introduction ... 4 2. Theoretical Framework ... 6 2.1 Inventory Strategies ... 6 2.2 Product Information ... 7 3. Method... 12 4. Findings ... 18 4.1 Sales Data ... 184.2 Customer Satisfaction: Customer Reviews, Publicity and Return Numbers ... 19
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Abstract
Purpose: Due to the complexity of forecasting product launches, inventory shortages and
surpluses may occur shortly after the launch. This paper investigates which product information types, that become available shortly after the launch, firms use for inventory decisions. Due to the high financial impact of inventory, this is an important topic to investigate.
Methodology: The purpose of this paper is to extend theory about product launches, product
information and inventory strategies and to explore new information types that are used for inventory decisions shortly after the launch. Therefore, a multiple case study is conducted among retail companies that launch shopping products.
Findings: There are multiple important findings from this research. First, sales data is not much
shared within the supply chain, especially shortly after the launch. This is not in line with the literature, which states that the sharing of sales data could improve inventory decisions. Second, the historical sales data of reference products, return numbers and conversion rates are additional information types that multiple companies use for their inventory decisions shortly after the launch, but these have not been investigated in the inventory literature yet. Third, there are multiple companies who would like to use customer reviews and publicity for their inventory decisions, but they do not know how.
Contribution: This paper contributes by researching which information types companies use
for inventory decisions shortly after the launch. In addition, this paper also illustrates some gaps that exist between literature and practice.
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1. Introduction
Shortly after a new product has been launched, companies may find themselves with inventory shortages or surpluses, since demand was different from what was forecasted. Due to the high financial impact of these inventory shortages and surpluses, it is important that companies react quickly to new information about the success of the launched product (Kahn, 2014; Tan et al., 2017). This applies to for example shopping products, which are products that are occasionally bought, thoroughly compared on price, quality and style (Claessens, 2017) and are often more expensive than convenience products. Due to their higher value, it is important for companies to have a well-managed and thought out inventory strategy, so inventory and penalty costs are kept low. Furthermore, shopping products have to be frequently launched because of their short life-cycle (Claessens, 2017; Seifert et al., 2016). This gives many opportunities for data collection. Even though the first information about these products becomes available shortly after the launch, it is not clear when and which information types companies use for decisions about inventory strategies.
According to Sapra et al. (2010), an inventory strategy is the trade-off between minimizing inventory costs caused by inventory surpluses and minimizing penalty costs caused by inventory shortages. These surpluses and shortages may appear due to demand uncertainty caused by a lack of historical data (Rosenfield, 1989), which makes an inventory strategy a complex subject when launching a product. A product launch can be divided into two periods: the launch period and the sales period (Chintapalli and Hazra, 2015). In the launch period, demand is forecasted and marketing plans are made. In the sales period, actual types of product information become available, which gives insight into the performance, popularity and success of the product (Chintapalli and Hazra, 2015; Cui et al., 2011; Shepherd and Günter, 2006). It is important that this information is collected as soon as possible, so it can be used for decisions about inventory strategies (Cui et al., 2011). However, little is described in literature about the period shortly after the launch of the product, when the first product information becomes available. Therefore, the research question that is addressed in this research is:
Which product information types can companies use shortly after launching a shopping product, and how are inventory strategies adjusted based on this information?
5 companies that provide shopping products in the Netherlands. The following sub questions are developed to help answer the research question.
1. Which product information types are available in the academic literature that can be used for adjusting inventory strategies shortly after launching a product?
2. Which product information types do companies use for adjusting inventory strategies shortly after launching a product?
3. What are future opportunities for companies regarding product information types that are used for adjusting inventory strategies shortly after launching a product?
4. How are these product information types used for adjusting inventory strategies? Not much literature about inventory strategies and product information is available for the period shortly after the launch when not much information about the product is available. Many inventory strategies that focus on products with uncertain demand have been described (Christopher, 2000; Frohlich and Westbrook, 2001; Lee, 2002; Chen et al., 2012). However, these strategies only consider existing products. The studies of Bowersox et al. (1999) and Kou and Lee (2015) are two of the few studies that do focus on product launches and inventory strategies, but these papers only take the period before the launch into account. In addition, many papers have been written about product information, but none of them are in the context of this research. Bendoly et al. (2012) and Di Benedetto (1999) explain in their papers different types of product information that can be used when deciding on an inventory strategy, but they do not include product launches. Ernst et al. (2010) have a focus on product launches in their paper, but they only focus on information gathered before the launch. The research that comes a bit closer to this paper is the research of Cui et al. (2012). In their paper, they investigate how online reviews effect new product sales shortly after the launch by investigating over 300 products from Amazon. However, the focus in their paper is only on customer reviews. This paper expands this research by investigating other information types. In addition, this paper investigates how these information types effect decisions about inventory strategies instead of product sales.
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2. Theoretical Framework
In this chapter, the theoretical background of this paper is discussed. To show different decisions companies can make when they receive product information shortly after launching a shopping product, this chapter starts with an explanation of different inventory strategies. Afterwards, the chapter continues with a theoretical framework about product information types that can be used shortly after the launch to base inventory decisions on. This framework serves as an important base for the multiple case-study that is described in Chapter 3.
2.1 Inventory Strategies
Based on literature, different strategies are distinguished that can be used to adjust inventory. Below, three strategies are described that can be applied to inventory surpluses and shortages. These are product disposal, return policies and lean launch.
Product disposal is the quantity of products you remove from stock (Haijema, 2014) by either selling the products at a reduced sales price, giving them away or liquidating them (Çetinkaya and Parlar, 2010; Rosenfield, 1989; Toelle and Tersine, 1992). According to Rosenfield (1989), it is sometimes necessary to dispose products, because it can happen that the sale of inventory surpluses does not exceed expenses associated with it. Another strategy for dealing with inventory surpluses after launching a product is to include return policies in contracts. A return policy is a commitment from an upstream partner to accept the return of products from a downstream partner and allows companies to take more risks regarding inventory (Ai et al., 2012; Hsieh and Lu, 2010; Padmanabhan and Png, 1995). Even though literature is available about how to deal with inventory surpluses, none of the research focuses on the period shortly after the launch.
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2.2 Product Information
To investigate what product information companies use for their decisions about inventory strategies shortly after the launch, it is important to know what types of information product information contains. By comparing the papers of Shepherd and Günter (2006), Lee et al. (2011) and Fan et al. (2017), four types of product information can be distinguished; customer satisfaction, sales data, market data and advertisement. The reason for using these four information types in this paper is further explained below.
Inventory decisions for new products are dependent on the product’s performance (Cui et al., 2011). Information that measures a product’s performance concerns the information about a product’s effectiveness and efficiency (Shepherd and Günter, 2006). Effectiveness is the extent to which requirements of the customers are met and efficiency is about how economically a firm uses its resources to provide a pre-specified customer satisfaction level (Shepherd and Günter, 2006). Lee et al. (2011) extend this by stating that information about financial performance (market data and sales) and information about strategic performance (customer satisfaction) can measure the performance of a product launch. In addition, Fan et al. (2017) describe that there are two primary factors that influence customers decisions to purchase a product; the satisfaction of other customers who share their experience about the bought product and advertisement
Similarities in the definitions of Shepherd and Günter (2006), Lee et al. (2011) and Fan et al. (2017) can be observed. Strategic performance (Lee et al., 2011) and effectiveness (Shepherd and Günter, 2006) contains roughly the same information; information about customer satisfaction, which is a primary factor that influences the purchase decisions of other customers (Fan et al., 2017). Therefore, this is combined in this research in one information type; customer satisfaction. Financial performance (Lee et al., 2011) and efficiency (Shepherd and Günter, 2006) is about how well a product is performing. Therefore, these are combined in two information types: sales data and market data. Fan et al. (2017) mention advertisement as another primary factor that influences the success of a new product, which is therefore also used as an information type in this paper.
Customer Satisfaction: Customer Reviews and Publicity
8 subsequently to make decisions about the product. Fornell et al (2010) expand this by showing how data on customer satisfaction can help predict the future growth of a product. When new products do not meet expectations of customers, it is often expressed via customer reviews or publicity, with the risk of losing reputation as a consequence (Aula, 2010). Publicity is editorial space in media that helps to promote a product without identifying the message sponsor (Eisend and Küster, 2011). Social media is an important source where products can get publicity, since it is impossible for organisations to control conversations and information about their product (Aula, 2010). According to Burmester et al. (2015), customers prefer information coming from publicity over information coming from advertisement when making purchase decisions, since publicity is objective and unbiased information that is formed by a third party. Therefore publicity can have a major impact on sales (Burmester et al., 2015).
Another source of customer satisfaction that can have a major impact on sales is customer reviews, which is a way for customers to evaluate their newly bought products and speak their satisfaction about the product (Cui et al., 2012). It is one of the most frequently used ways to monitor and listen to customer voices (Kang and Park, 2014). Due to the increase in the use of internet, electronic word of mouth is now a big source for product reviews (Cui et al., 2012). Online reviews can have a great effect on new product sales (Cui et al., 2012; Fan et al., 2017; Floyd et al., 2014; Zhu and Zhang, 2010), due to the fact that customers trust these uninfluenced opinions better (Floyd et al., 2014).
9 All of the above shows that the way products are presented in customer reviews or publicity can significantly influence sales levels of launched products and can predict future sales. However, it is unknown if customer reviews and publicity are also used for making decisions about inventory strategies shortly after the launch.
Sales Data
Most forecasting models used for inventory decisions are based on an analysis of historical sales in combination with managerial judgement (Trapero et al., 2015). Sales data is the most straightforward performance measure of success for a product and therefore also the most straightforward data to base decisions on. One way to gather sales data is to look at the firm’s own sales numbers. However, this could give an inaccurate impression of the real situation shortly after the launch, since there could be many reasons for differences between the expected and actual sales data (Nagashima et al., 2015). Supply Chain Integration is a concept in the supply chain management literature that gained much attention (Chen and Lee, 2012; Flynn et al., 2010; Nagashima et al., 2015). Supply Chain Integration is the degree in which partners in the supply chain collaborate to improve flows of information, products, services and money to provide customers with maximum value (Flynn et al., 2010). Sharing data and collaborating with up- and downstream partners in the supply chain is becoming a more important way to gather information on sales (Chen and Lee, 2012) and could be valuable for different partners in the supply chain to get an impression of the real situation. The simplest form of collaboration is sharing information about inventories and sales levels. More intense forms of collaboration are the sharing of experiences, risks and profits (Nagashima et al., 2015). The bullwhip effect is a famous example when there is a lack of sales data sharing, which results in high inventory surpluses since the variance in orders to the suppliers is higher than the sales of the buyer (Chen and Lee, 2012). Even though the importance of sales data for companies is clearly stated, it is unknown how and what sales data companies use when making inventory decisions shortly after the launch of a shopping product. In addition, it is not clear when companies start using the first sales data to base inventory decisions on.
Market Data
10 stating that the performance of new products is positively moderated by market competitiveness in China. In addition, by conducting a survey in U.S.A., U.K. and the Netherlands, Debruyne et al. (2002) found that 60% of the launches provoke reactions by competitors after the launch. This is confirmed by Abbey et al. (2015). They state that prices of new products often must be adjusted after the product launch to keep up the demand level, since substitutes of the launched products enter the market. It is clear that market data is important when new products are launched. However, most of the available literature focuses on the product’s success. Therefore, it is not clear yet if market data is used for inventory decisions shortly after the launch.
Advertisement
Advertisement is a paid communication with an identified message sponsor (Eisend and Küster, 2011). Advertisement has a major influence on product sales (Fan et al., 2017), even though the effects do not hold as long as the effects of publicity (Burmester et al., 2015). Mcalister et al. (2016) confirm in their study that advertising always has an impact on sales. However, the impact is bigger for differentiating firms than for cost leaders. Dinner et al. (2011) found that advertising across multiple channels increases the effect on sales and they show that online advertising outperforms traditional advertising methods (Dinner et al., 2011). From the above can be concluded that when a firm puts much energy and money in advertising, sales should increase. The extent to which sales increase after advertising could therefore be an important type to see how a product is performing and subsequently how inventory strategies should be adjusted shortly after the launch.
Customer Reviews
Customer Reviews Volume
Aula, 2010; Burmester et al., 2015; Cheung and Thadani, 2012; Cui et al., 2012; Di Benedetto, 1999; Eisend and Küster, 2011; Fan et al., 2017; Floyd et al., 2014; Fornell et al., 2010; Lee et al., 2011; Ottum and Moore, 1997; Schneider and Gupta, 2016; Shepherd and Günter, 2006; Zhu and Zhang, 2010
Customer Reviews Valence
Customer Reviews Dispersion
Publicity Publicity Volume Aula, 2010; Burmester et al, 2015; Cheung and Thadani, 2012; Cui et al., 2012; Di Benedetto, 1999; Eisend and Küster, 2011; Fan et al., 2017; Fornell et al., 2010; Lee et al., 2011; Ottum and Moore, 1997; Schneider and Gupta, 2016; Shepherd and Günter, 2006
Publicity Valence Publicity Dispersion
Sales Data Sales Data Company Chen and Lee, 2012; Flynn et al., 2010; Lee et al., 2011; Nagashima et al., 2015; Shepherd and Günter, 2006; Trapero et al., 2015
Sales Data Supply Chain
Market Data Reaction of competitors
Abbey et al., 2015; Bendoly et al., 2012; Calantone et al., 2010; Debruyne et al., 2002; Khajavi et al., 2015; Lee et al., 2011; Shepherd and Günter, 2006; Yang and Li, 2011
Advertisement Advertisement Burmester et al., 2015; Dinner et al., 2011; Eisend and Küster, 2011; Fan et al., 2017; Mcalister et al. 2016
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3. Method
To answer the research question, a multiple case study is conducted. As mentioned in Chapter 2, many different information types and inventory strategies are described in comparable fields in the academic literature. Therefore, the goal of this paper is theory building. A case study is appropriate to answer the research question, because the aim of this paper is to also explore new information types (Karlsson, 2016). A multiple case study has been chosen over a single case-study for this topic, because of the exploratory purpose of this paper. Studying multiple product types and decisions about inventory strategies gives more generalizability and a more reliable and clearer base for future research. Furthermore, a multiple case-study also gives an increased external validity and a reduced observer bias (Karlsson, 2016).
Unit of Analysis
The unit of analysis during this study is the decision-making process of an inventory strategy, since it corresponds with the research question and it allows to investigate which information types firms use and what the effects are of these information types on decisions about inventory strategies.
Research Setting
As stated in the research question in Chapter 1, the focus of this research is on shopping products because of their short life-cycle. To answer the research question, companies that have to deal with many product launches are needed. Therefore, retail companies are approached. To collect data, individuals from the companies are interviewed at their office. This is valuable since it is more convenient for the interviewees and it lowers the bar to participate in this research. In addition, individuals have access to files or other data in their offices that can support their answers and they can quickly ask other available colleagues to confirm or elaborate on certain statements. Due to the location of the office of Case A, the interview with this company is conducted at an external location.
Case Selection
13 launch. Due to the time span of this research, individuals are asked to describe the historical events of the launch process and launch decisions.
To answer the research question, eight cases are selected. This number is chosen because the research method is a multiple case study and generalizability is one of the goals. In general, new cases should be investigated until no new information is obtained from the cases. However, eight is the maximum number of cases possible due to the time span of this research. This number still realizes some generalizability, while simultaneously some in-depth study per case is obtained in the available time span.
14 branches and launch different products, results between cases can differ. Based on the different cases, both theoretical and literal replication can be applied (Karlsson, 2016). Most companies from the cases operate in different markets, with different lead times, product life-cycles and competition (theoretical replication). However, Case 1, Case 4 and Case 6 only sell products online, which allows literal replication. The same yields for the other cases, which mainly sell products offline.
Case Product Authority
of decision-making Motivation for launch Industry Online or Offline Sort Company Case A Shopping product Within the company Intrinsically Computer Industry Online Reseller of Electronical Parts Case B Shopping product Within the company Intrinsically Tele-communica tions Online and Offline Reseller of Mobile Phones Case C Shopping product Within the company Intrinsically Clothes Industry Online Reseller of Clothes Case D Shopping product Within the company Intrinsically Retail Industry Online Reseller of Books, Toys and Electronics Case E Shopping product Within the company Intrinsically Retail Industry Mostly Offline Reseller of Health and Beauty Products Case F Shopping product Within the company Intrinsically Furniture Industry Mostly Offline Holding for multiple Department Stores Case G Shopping product Within the company Intrinsically Child Industry Online and Offline Seller of Pregnancy and Baby Products Case H Shopping product Within the company
Intrinsically Agri Retail Industry Mostly Offline Seller of Garden and Pet Products
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Data Collection
Companies are approached via the connections of IMCC. An email is sent with an explanation about the research. After the final case selection, semi-structured interviews are conducted, which is suitable because on the one hand some of the information types are already available, but on the other hand, exploratory research is still needed to investigate other information types. The interview questions are based on the information types displayed in Table 2.1. In addition, the data collection is also partly inductive because of the theory-building purpose of the research (Karlsson, 2016). Therefore, the first questions are very broad, so the influence of the interviewer on the answers of the interviewee is limited. The last part of the interviews consists out of questions about the weight of the effect of the different information types on inventory decisions. The interviewee can rank the different information types on a scale from 1 to 10. The interviews last for approximately one hour in combination with the interview of another researcher. The interviews are conducted by two interviewers, which increases the reliability. For the interviews, a protocol is available, which is displayed in Appendix A. This protocol increases reliability and internal validity. To assure the quality of the data, the interviews are recorded with permission. This decreases bias of the researchers. After the interviews are conducted, the interviews are transcribed and cross-checked by the participating companies to check whether everything is correctly interpreted. The next step is the coding of the transcripts, which leads to a within-case analysis and a cross-case analysis.
Quality Criteria
16 to different outcomes for all cases, which is why cases are also selected that operate in the same market. Reliability measures if the method to collect data is stated in such a way that everyone can repeat it (Yin, 2009). A protocol is used during the interviews, which results in a standard way of interviewing. In addition, two researchers are involved, which gives the possibility to share knowledge, to control each other and to explicitly state everything to understand each other.
Data Organization and Analysis
The first step in the data analysis is the within-case analysis. The collected data is analysed by structuring and sorting the data by means of coding. Coding enables the researcher to find different patterns in the data that should be studied (Karlsson, 2016). This process should lead to an overview of different product information types that companies use to base their inventory decisions on shortly after the launch. Every transcript is coded with the deductive codes in Table 2.1 and with inductive codes.
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Coding Tree
In Table 3.2, the coding tree is presented, which was developed after the described data analysis. The start of the data analysis was deductive, based on the codes in Table 2.1. However, based on the data, some deductive codes are removed and some inductive codes are added. In Table 3.2, only the second and third order codes are displayed to get an indication of the coding tree. All first order codes are presented in Appendix B. This coding tree is further described in Chapter 4.
2nd order codes 3d order codes
Sales Data
Sales Data Sales Data Sharing
Reference Product Sales Data Customer Reviews
Customer Satisfaction: Customer Reviews, Publicity, Return Numbers Customer Reviews Valence
Customer Reviews Volume Publicity Publicity Valence Publicity Dispersion Return Numbers Advertisement Advertisement Market Data Market Data Market Data Reactions
Market Data Prices
Conversion Rates Conversion Rates
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4. Findings
This chapter describes the findings from the data analysis that is described in Chapter 3. Different companies are interviewed about information types that they use for their decisions about inventory strategies shortly after the launch of a shopping product and about the action on inventory they take when they receive this information. With this data the coding tree in Table 3.2 is made, which is described below.
4.1 Sales Data
Sales data was for all eight companies the highest information type rated, which indicates that it is the most important information type in practice to base inventory decisions on shortly after the launch of a shopping product. All eight companies rated sales data with a 9 or a 10 as weight of the effect on inventory decisions shortly after the launch. In addition, Case G, Case F, Case C, Case D and Case H explicitly stated that it is the first information that becomes available shortly after the launch.
“[The first information] we receive on a daily basis is the sales data, which is also most important” (Case F)
Generally, when sales are much better than expected shortly after the launch, companies immediately react by ordering more products at the supplier the same day. When sales are less than expected, all companies mentioned that action on inventory is not immediately taken. So, the moment companies react to positive or negative sales data is different. When sales do not get better after a while, products are sent back to the supplier if possible, and otherwise discounted. Liquidating the products almost never happens.
“Especially adjusting upwards is important, otherwise you will have stockouts. Adjusting downwards is not really necessary, because then there is only a bit too much (..)” (Case E) “A rule of thumb is that we wait for three weeks before taking action (…). After three weeks, we learned from experience that we have a good indication what the product is going to do. If it is not good then, we will react” (Case F)
Sales Data Sharing
19 or expected sales numbers. A reason that Case F, Case G and Case H do not get this information can be that they mainly sell products from their own private label. Shortly after the launch, inventory decisions are only based on the companies’ own sales data. In addition, Case E mentioned that they sometimes miss information about the inventories of suppliers. They explained that the sales of the supplier can be higher than expected, which can cause a European inventory shortage shortly after the launch. Even though some suppliers started communicating this a bit, it often comes as a surprise, which is why they cannot prepare.
Sales Data Reference Product
The historical sales data of a reference product is an additional information type that companies use for inventory decisions shortly after the launch. All companies often search for a product that is closely related to the product that will be launched, so they can use that product’s data as reference. Case D and Case E also explicitly mentioned the use of this reference product for inventory decisions shortly after the launch. Current demand patterns of the new product can be compared to historical demand patterns of reference products, which can say something about the success of the product and the future sales of the product. Because there was no standard question about sales data of a reference product in the interview protocol, it could be that more companies use this data for decisions about inventory strategies.
“(…) The art is to follow the product to compare if it is also reality. From the first moment you have to follow the product and adjust if necessary.” (Case E)
4.2 Customer Satisfaction: Customer Reviews, Publicity and Return
Numbers
Information about customer satisfaction can be gathered in three ways shortly after the launch of a shopping product according to practice; via customer reviews, publicity and return numbers.
Customer Reviews and Publicity
20 “We do use Customer Reviews, but not for decisions about inventory” (Case G)
“[When the quality of the product is good, but the reviews are still negative] is information I do not use” (Case H)
The information that Case D, Case A and Case C mostly extract from customer reviews shortly after the launch is the valence. Case C for example stated that they will stop selling the product if there are too many negative reviews. Case D said that negative or positive reviews are considered when decisions about inventory strategies are made, but not if there are only a few. They also stated that volume of reviews has an influence on inventory decisions shortly after the launch, but only when it is seen back in the sales data. For Case C, if there are many or only a few reviews is not seen as information shortly after the launch.
“It could say something [if we have 1000 instead of 10 reviews about a launched product], but we do not use it for inventories.” (Case C)
“We do take [the number of reviews] into account when it is a popular product. We check if we see it back in the sales numbers” (Case D)
Only Case D mentioned dispersion of customer reviews in combination with publicity as information that influences their inventory decisions shortly after the launch. They stated that it can happen that reviews are written before or soon after the launch by experts who have much influence and a big fanbase. They pay more attention to these reviews.
“There are sometimes experts that test games before the launch and write a review about it. You can see that this sometimes has a big impact. This is also something that happens after the launch of a product.” (Case D)
Both E-commerce and non-E-commerce companies rather use publicity for their inventory decisions shortly after the launch than information coming from customer reviews. E-commerce companies rate the weight of the effect of publicity on inventory decisions high (from 7 to 10) and also some non-E-commerce companies like Case G, Case E and the Case F rated it with a 6 or higher. A reason for this difference could be that the impact of publicity is higher on sales in contrast to customer reviews, since the dispersion is higher. However, publicity may come as a surprise and quickly change sales levels, which makes it hard to adjust inventory.
21 “Some time ago, there was suddenly a Pokémon hype (…) We see something happening in the market from the news and then we must sit with the supplier (…) And then you are too late, because nobody saw it coming.” (Case A)
Case H, Case F and Case G stated that they think customer reviews and publicity would be valuable information to use for inventory decisions in the future, since especially the influence of customer reviews is growing. They just do not know how to use it. Also Case C mentioned that they would like to use customer reviews more for their inventory decisions shortly after the launch.
“I can imagine that it is valuable data. (…) Only more people are going to let everybody know what they think of a product. I have no idea how I can get that information in such a way that I can use it. (…) If I would get a nice report about it, I could maybe take it into account in my decisions.” (Case F).
Action on inventory is taken sooner when the publicity or reviews are positive than when they are negative, which can be compared to the action that companies take after receiving information from sales data. An explanation for this similarity can be that action on inventory is most of the time only taken after it is seen in sales data.
“A while ago, we have had an eyelash serum of our private label, which was used in a vlog of a famous person. The day after, the sales went through the roof because it was in three papers. We had to respond immediately that day.” (Case E)
“In principle, we wait until the release and a bit after the release [when publicity is negative] because sometimes it takes some time before people decide they want to read or play it and see for themselves” (Case D)
Return Numbers
The third way companies measure customer satisfaction shortly after the launch of a shopping product is by looking at return numbers. Case D, Case C, Case A and Case B all mentioned that they keep track of the return numbers and rated it on average with a 6.8 as weight of the effect on inventory decisions shortly after the launch.
“[We can see customer satisfaction] in return numbers. This can have multiple causes of course. (…) We keep track of the numbers to see how the product is performing.” (Case A)
22 after a few months, which makes it hard to anticipate on regarding inventory shortly after the launch. Even though many returns are normal in the fashion market, Case C still rated it with a 9. If too much clothes are returned, inventory is scaled down and products are eventually not sold anymore.
“[If too much clothes are returned] we will delete it from the site, because we are bothering customers with products that they do not want. And then it will go back to the supplier or a deal will be made with a buyer.” (Case C)
Furthermore, Case D stated that it can be hard to find out the cause of returns for some of their products, because there can be a million reasons why products are returned, which do not all have to do with the quality of the product. Because there was not a standard question about return numbers in the interview protocol, it could be that also other participating companies use this information type for their decisions about inventory strategies shortly after the launch.
4.3 Advertisement
Information from advertisement is considered most important after information gathered from sales data for inventory decisions shortly after the launch of a shopping product. On average it is rated with 8.1. The way it is exactly used is a bit different. Case F indicated for example that they use a promotion forecast and if sales deviate, they will act and Case H mentioned that they compare the sales after a promotion with the sales of a reference product.
“When we decide to promote a product, we always make a promotion forecast (…). I will purchase extra because we expect to sell. If this is not the case (…) I will try to do something with my orders. For example, extend or cancel them (…). So, we definitely review this.” (Case F)
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4.4 Market Data
Market data is rated by the companies on average a 6 as the weight of the effect on inventory decisions shortly after the launch. All companies ranked market data differently and no patterns between E-commerce and non-E-commerce companies could be observed. All companies mentioned that prices and reactions of competitors are monitored, but action is often only taken after it is seen in the sales data. Case F was the only company that mentioned that they would also adjust inventory strategies because of their image of being the cheapest seller of furniture. In accordance with publicity, reactions of competitors can be unpredictable and may come as a surprise, which makes it hard to predict the amount of inventory needed.
“For existing products where we are known from, but our competitors are also going to sell it, I will be reactive because I do not know what the competitor is going to do. It is not that I (…) lower my inventory by half.” (Case H)
“You do not know often [the reactions of competitors] beforehand. We try to predict it and if we know, we try to take it into account.” (Case D)
4.5 Conversion Rates
Case D also mentioned conversion rates as information that influences inventory decisions shortly after the launch of a shopping product and rated it a 9. A conversion rate is the percentage of the visitors on the webpage that buys the product.
“We work with conversion rates (…). If it is lower than normal, you know something is going on. You can see this also after the release. (…) If we see at that moment [that the conversion rates] are decreasing or it is not growing and it stays the same, then we try to find out why and try to take the right action.” (Case D)
Because conversion rates is not an information type that was included in the questions in the interview protocol, not every company was asked about this information type. Case G, Case E and Case H indicated that they do not use conversion rates for their inventory decisions, but they think it could be a valuable information type in the future.
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5. Discussion
After conducting the interviews, it turns out that there are many product information types that companies use for their decisions about inventory strategies shortly after launching a shopping product. In the literature study in Chapter 2, different information types are found, which are; customer satisfaction, sales data, market data and advertisement (Fan et al., 2017; Lee et al., 2011; Shepherd and Günter, 2006). These are displayed in Table 2.1. The information types found in practice are displayed in Table 3.2. It turns out that in practice, companies use some other information types for inventory decisions shortly after the launch than those that are described in literature. These differences are discussed in this chapter.
Sales Data sharing
Companies rated sales data as the most important information type to base inventory decisions on, but sharing sales data across the supply chain shortly after launching a shopping product is not as common as expected, which is not in agreement to the academic literature described in Chapter 2. Case A, Case C, Case D, Case E and Case B mentioned that suppliers sometimes give a prognosis or indication about sales before the launch, but companies must use their own sales data to make decisions shortly after the launch. Furthermore, Case E mentioned that it often happens that suppliers have to deal with a European inventory shortage shortly after the launch, which is not always communicated and therefore a part of information that is lacking when companies make inventory decisions shortly after the launch. This is not in line with the current academic literature, which states that sharing sales data across the supply chain can be an important tool for inventory decisions, so unnecessary stock in the supply chain can be avoided (Chen and Lee, 2012; Flynn et al., 2010).
25
Sales Data Reference Products
Historical sales data of reference products seems to be an important information type in addition to the current sales data when making decisions about inventory strategies shortly after the launch, but not much about the use of reference products as information type can be found in the inventory literature. All participating companies mentioned the historical sales data of a reference product as one of the most important information types. Even though it is mostly used before the launch, two companies mentioned that they use the sales patterns of reference products shortly after the launch to compare the current sales too, so they can indicate which sales pattern the product is going to follow. Because there was no standard question in the interview, it could be possible that more companies use this information type.
In contrast to practice, not much about this information type can be found when searching the literature again. Kahn (2014) mentions shortly the use of reference products in his paper by stating that data which is used for forecasting new products before the launch can be based on assumptions, for example that the sales will be 10% higher than a previous launch. He does not mention if this is also used shortly after the launch. In addition, there is also some literature that states that using reference products for decisions is not a good approach. For example, Geurts and Reinmuth (1980) describe that using historical data of reference products is a popular approach, but that it often leads to high forecast inaccuracies. In contrast to this literature, using historical sales data of reference products still seems to be a popular approach for inventory decisions in practice, before and shortly after the launch. In the future, using data of reference products for inventory decisions shortly after the launch could become more valuable and popular with the current opportunities of artificial intelligence. This technique is already used for forecasting instead of for inventory decisions, for example in the oil industry (Mostafa and El-Masry, 2016) and its popularity is only growing. The correct use of reference products as information for inventory decisions shortly after the launch would therefore be an interesting topic for future research.
Customer Reviews and Publicity
26 products. A reason for the difference could be that the impact of publicity is higher on sales in contrast to customer reviews because the dispersion is higher. Information that is mostly gathered from customer reviews and publicity is the valence; the ratio of positive and negative reviews. However, as described in the theoretical framework in Chapter 2, the volume and dispersion is also information that companies can gather from customer reviews and publicity (Cui et al., 2012). The dispersion of publicity is only used by Case D and even though some other companies state that the dispersion and volume of customer reviews and publicity could have an influence, it is barely used for inventory decisions shortly after the launch.
A reason companies mentioned why they do not use customer reviews and publicity is that they do not know how, even though some literature about using customer reviews and publicity for decisions is available. Three companies that do not take customer reviews into account yet mentioned that they think it would be valuable information to use, but they do not know how they can transform the information in such a way that they can use it for inventory decisions shortly after the launch. The same yields for publicity. Publicity is already used for inventory decisions, but the impact can be quite big, which is why three companies mentioned they would like a more reliable way of using this information for inventory decisions shortly after the launch.
27 smaller companies can transform information from customer reviews and publicity in such a way that they can use it for inventory decisions shortly after the launch.
Return Numbers
Return numbers is an additional information type used by companies to base inventory decisions on shortly after the launch, which is not mentioned in literature. From the interviews, it came to light that three companies use return numbers to measure customer satisfaction and to base inventory decisions on shortly after the launch, even though it was not a standard question in the interview protocol. In Chapter 2, customer reviews and publicity are identified as two ways to gather information about customer satisfaction (Cui et al., 2012; Eisend and Küster, 2011; Kang and Park, 2014). When looking further in literature for return numbers, it turns out that returns are already thoroughly investigated. However, this research focuses on how to manage the return of products and on sustainability, green supply chains and closed loop supply chains (Shaharudin et al., 2017). No research has been conducted on how product returns can be used for inventory decisions, even though almost half of the interviewed companies indicate it as an important information type for inventory decisions. From this research, there is proof that return numbers is an information type used for inventory decisions shortly after the launch. However, investigating how return numbers can be effectively and correctly used for inventory decisions is still an opportunity for the academic literature.
Advertisement, Market Data and Inventory adjustments
An information type that practice and literature agree on is advertisement. Advertisement is considered most important after sales data for inventory decisions in practice shortly after the launch. Case H and Case F stated that they compare their sales after the promotion to the sales of a reference product or to a promotion forecast. This is in line with the literature in Chapter 2 that states that advertising always has an impact on sales. The change in sales after advertisement is therefore an important information type for inventory decisions shortly after the launch.
28 et al., 2010; Debruyne et al., 2002; Khajavi et al., 2015). However, it seems that market data is not that useful for inventory decisions shortly after the launch, because there is often not enough time to adjust inventories when competitors react.
The actions that are taken shortly after the launch when the first information becomes available is different for shortages and surpluses, which is not fully in agreement with the literature described in Chapter 2. When sales are better than expected and there is a shortage, action is often immediately taken by ordering more at the supplier if possible, which is in accordance with the literature. However, when it is the other way around, and sales are less than expected shortly after the launch, all companies often wait a while to see what happens, which does not exactly match the literature. Rosenfield (1989) states that it can happen that the sale of these surpluses does not exceed expenses associated with the holding anymore. Therefore, disposing products could be valuable. A reason that this does not happen is that companies may still have hope that sales will increase, even though this might eventually negatively affect the profit of the product. A reason for the difference between literature and practice could be that the literature does not focus on the period shortly after the launch. Therefore, it can be concluded from this research that companies often do not react on inventory surpluses by disposing products shortly after the launch.
Conversion Rates
30
6. Conclusion
The aim of this research is to find out which information types companies can use for inventory decisions shortly after the launch of a shopping product. Subsequently, attention is paid to when and what kind of action companies take regarding inventory strategies when they receive the first information. It is important to study this topic because inventory surpluses or shortages may occur when launching a new product, since there is no historical data to make a forecast. It is important that companies react quickly to new product information that becomes available shortly after the launch to avoid inventory surpluses and shortages, especially since these inventory shortages and surpluses can have a dramatic financial impact on the firm. A multiple case study is conducted for this research among eight different companies that operate in different markets. All companies sell shopping products, recently launched a product, are intrinsically motivated to launch products and have the authority of launch decisions within the company. The interviews are analysed by coding the interviews, which gives a structured overview to conduct a within and cross-case analysis. The coding tree can be found in Table 3.2 or Appendix B.
From the research, different conclusions can be drawn. First, it is found that sales data is not much shared between the companies and their suppliers, especially shortly after the launch. However, in literature, many papers prove that the sharing of sales data improves performance and could improve inventory decisions. Nagashima et al. (2015) provide a possible explanation for this contradictory phenomenon by stating that sales data sharing is not that common yet because there are many conflicts of interest in the supply chain. However, the sharing of sales data could result in more reliable sales data to base inventory decisions on shortly after the launch of a shopping product and therefore it could be an opportunity for the academic literature and for organisations. Second, it turns out that the historical sales data of a reference product is an additional information type that multiple companies use for their inventory decisions shortly after the launch, but this has not been investigated in the inventory literature yet. However, with the current opportunities like artificial intelligence, the historical sales data of a reference product could be a very useful information type in the future.
31 focus on Amazon and the effect on inventory decisions is still neglected, which leaves a gap in the literature. Fourth, return numbers are used by multiple companies as information type for their inventory decisions shortly after the launch. However, in the operations literature, return numbers are only described in a sustainability and logistics context. Using return numbers as information type for inventory decisions is still lacking. Fifth, it turns out that conversion rates, which is the percentage of visitors on the website that actually buy the product, is an additional information type that multiple companies want to use for inventory decisions shortly after the launch. However, conversion rates have not been applied in the inventory literature yet. Due to the growing online retailing, conversion rates are becoming a more popular information type (Park, 2017), and therefore they can lead to many opportunities in practice and literature. Future Research and Limitations
32
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37
Appendices
A. Interview Protocol
Introductie:
Bedankt dat u wilt meewerken aan ons onderzoek. Wij onderzoeken de voorraad strategieën voor en na de lancering van een product. Het interview bestaat uit drie delen: de periode voor de productlancering, de periode na de productlancering en een ranking. Het interview duurt ongeveer 60 minuten. Zoals vermeld is de data compleet anoniem. Vindt u het goed dat wij het interview opnemen? Dit zal ten goede komen van het analyseren van onze data.
--- Algemene vragen voorafgaand aan het interview mailen ---
Algemene vragen
Naam
Functie
Werkervaring in deze functie
Productnaam
Jaar van productlancering
Rol in het voorraad beslissingsproces
1. Waar ligt de beslissingsbevoegdheid over de voorraad van dit product in uw organisatie?
2. Hoe zit het proces van voorraad besluitvorming eruit? 3. Wat is de doelgroep van het product?
4. Waarom is dit product gelanceerd naar de markt?
5. Wat is de huidige voorraad strategie die jullie hanteren voor dit product? Deel 2: Periode na de lancering
38 2. Hoelang duurt het voordat de eerste informatie beschikbaar is?
-- Wanneer er een type informatie wordt genoemd die nog niet bekend is --
3. Hoe wordt de voorraadstrategie aangepast op basis van …. informatie? Customer Reviews
4. Worden Customer reviews gebruikt als input/informatie voor het aanpassen van een voorraadstrategie?
a. Nee: Is er een reden dat deze informatie niet wordt verzameld? Denkt u dat het waardevol zou zijn om deze informatie te verzamelen?
b. Ja: Wat voor informatie verzamelt u uit Customer Reviews?
i. Aantal reviews
ii. De verdeling tussen positieve en negatieve reacties iii. De snelheid waarmee de reviews zich verspreiden
c. Hoe wordt de voorraadstrategie aangepast op basis van deze informatie? Publiciteit
5. Wordt Publiciteit gebruikt als input/informatie voor het aanpassen van een voorraadstrategie?
a. Nee: Is er een reden dat deze informatie niet wordt gebruikt? Denkt u dat het waardevol zou zijn om deze informatie te gebruiken?
b. Ja: Wat voor informatie verzamelt u uit Publiciteit?
i. De grootte van de publiciteit
ii. Verdeling tussen positieve en negatieve reacties iii. De snelheid waarmee de publiciteit zich verspreidt
c. Hoe wordt de voorraadstrategie aangepast op basis van deze informatie? Sales Data
6. Wordt Sales Data gebruikt als input/informatie voor het aanpassen van een voorraadstrategie?
a. Nee: Is er een reden dat deze informatie niet wordt gebruikt? Denkt u dat het waardevol zou zijn om deze informatie te gebruiken?
39 ii. Sales data van partners in de supply chain
c. Hoe wordt de voorraadstrategie aangepast op basis van deze informatie? Adverteren
7. Worden de investeringen in adverteren gebruikt als input/informatie voor het aanpassen van een voorraadstrategie?
a. Nee: Is er een reden dat deze informatie niet wordt gebruikt? Denkt u dat het waardevol zou zijn om deze informatie te gebruiken?
b. Ja: Hoe wordt de voorraadstrategie aangepast op basis van de informatie? Marktdata
8. Worden reacties uit de markt gebruikt als input/informatie voor het aanpassen van een voorraadstrategie?
a. Nee: Is er een reden dat deze informatie niet wordt gebruikt? Denkt u dat het waardevol zou zijn om deze informatie te gebruiken?
b. Ja: Welke informatie verzamelt u uit de markt? i. Huidige substituten in de markt
ii. Reacties en acties van concurrenten
c. Hoe wordt de voorraadstrategie aangepast op basis van deze informatie? 9. Kun u ook informatie bedenken die u graag zou willen gebruiken, maar die momenteel
niet gebruikt wordt bij het aanpassen van een voorraadstrategie kort na de product lancering?
a. Hoe zou u deze informatie dan willen gebruiken? Afsluiting
We willen u graag bedanken voor uw deelname aan dit interview en uw duidelijke antwoorden. Heeft u nog vragen en/of opmerkingen over het interview? Wij zullen in de komende tijd het transcript naar u toesturen, zodat u nog de mogelijkheid heeft om controleren of wij alles goed hebben geïnterpreteerd.
--- Bij tijd tekort, dit over de email sturen ---
Deel 3 Ranking
40
Information typen Gewicht (0-10)
Customer Reviews
Publiciteit
Sales Data
Adverteren
