The inventory decision making process of new shopping products

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The inventory decision making process

of new shopping products

Master Thesis, MSc Supply Chain Management

University of Groningen, Faculty of Economics and Business

January 28, 2019 Marieke de Boer Studentnumber: S3266907 E-mail: m.e.de.boer.2@student.rug.nl Supervisor Professor Dr. K.J. Roodbergen Co-assessor Dr. Ir. S. Fazi

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ABSTRACT

Purpose One of the most important goals in inventory management is to maintain reduced inventories and minimize supply chain costs while meeting customer demands at the highest level. This is complex for new shopping products since they do not feature historical demand to forecast future demand and use this as input for the inventory decision making process. This study seeks to understand what factors decision makers take into account during the inventory decision making process of new shopping products and how important these factors are.

Methodology This study contains a multiple case study. The inventory decision making process of eight organisations is investigated by semi-structured interviews. The output generated by these cases are comparable and make cross-case analysis possible.

Findings In the inventory decision making process of new shopping products it is important to take into account sales data of comparable products, knowledge of advertisement and publicity of the new product, data from internet search terms and data arising from customer loyalty. As well as, holding inventory to ensure product availability and the product life cycle of the new product. Factors that create new insights to the operations literature are the importance of the number of physical stores, flexible return policies of the supplier, experience and knowledge of employees and the input from different disciplines in the inventory decision making of new shopping products.

Contributions This study adds new insights to the operations literature in the importance of factors in the inventory decision making process of new shopping products.

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Launching new products is of great importance for the success of a firm: 49% of the sales of successful firms are generated by new products (Benedetto, 1999). In order not to lose sales of new products, it is of significant value that there is enough inventory in stock to fulfil future demand. To forecast future demand, firms often rely on historical demand of products (Nagashima et al., 2015). Since new products do not feature historical demand, forecasting demand for new products is usually based on decisions of experienced managers (Ching-Chin et al., 2010). This study seeks to understand what factors decision makers take into account during the inventory decision making process of new products and how important these factors are.

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5 “What factors are taken into account during the inventory decision making process before

launching a new shopping product and how important are these factors?”

To answer the research question, sub questions are developed:

1. What factors are acknowledged in literature that influence the inventory decision making process before product launch?

2. What factors are taken into account in practice that influence the inventory decision making process before product launch?

3. How important are the factors that influence the inventory decision making process before product launch?

4. How do the factors, known in literature and taken into account in practice, relate to each other?

In order to answer these questions, this study performs a multiple case study. Eight cases are selected for semi-structured interviews and cross-case analysis afterwards.

The contributions of this study are threefold. A comparable study by Hultink et al. (2000) investigated how launch decisions bring a consumer product with success to the market. They found that successful products are launched to broaden the assortment of more innovative products and that advertisement is important in a successful launch. Even though these factors are important, Hultink et al. (2000) focus on launch decisions and did not focus specifically on inventory related decisions or shopping products, which is an opportunity for this study. Another study by Seifert et al. (2015) investigated human judgement in inventory decision making. They found that human judgement influences forecast accuracy positively. Their study is in a phase where historical demand data is available, which is unknown with new products and leads to a gap investigated in this study. Consequently, the practical relevance of this study is to improve the inventory decision making process.

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2. THEORY

One of the most important goals in inventory management is to maintain reduced inventories and minimize supply chain costs while meeting customer demands at the highest level (Lapide, 2004). To realize this, it is important to understand the connection and difference between demand forecast and the decision on the initial inventory level of a new product, which are both part of the inventory decision making process. Where in most literature historical demand is used to produce a demand forecast (Nagashima et al., 2015), this is not possible with new products. Consequently, other factors are used as input for the demand forecast, wherein it is of significant value that the input of operations and marketing department is combined (Lapide, 2004). Based on the demand forecast and multiple other factors, the initial inventory level of a new product is determined. This chapter reviews factors from an operations and marketing perspective that are important for the demand forecast and the decision on the initial inventory level in the inventory decision making process. From an operations perspective the factors are able to indicate and influence the demand in the market, where from a marketing perspective the factors are able to stimulate the demand in the market. Both are important to understand the inventory decision making process of new shopping products. Besides, this chapter also covers a direct relationship between the environmental impact and inventory decisions. Figure 2.1 visualizes and Table 2.1 summarizes the factors that are taken into account into the inventory decision making. This study seeks to find out if these factors are taken into account within the context of a new shopping product before its launch.

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7 Perspective Inventory decision

factors

Why According to

Operations Level of holding and backlogged inventory Influences future demand (§2.1) San-José et al., 2009; Balakrishnan et al., 2004; Craig et al., 2016; Nagashima et al., 2015; Bijvank and Vis, 2011 Product life cycle Indicates future

demand (§2.2)

Acimovic et al., 2017; Restuccia et al., 2015; Seifert et al., 2016 Sales data of comparable

products

Indicates future demand (§2.2)

Lee et al., 2012; Yu et al., 2011

Internet search terms Indicates future demand (§2.2)

Fuchs et al., 2010; Kulkarni et al., 2012; Rahman, 2017 Marketing Customer loyalty Stimulates future

demand (§2.3)

Craig et al., 2016; Ernst et al., 2011; Fuchs et al., 2010; Hoyer et al., 2010; Rahman, 2017 Advertisement and publicity Stimulates future demand (§2.3) Baum et al., 2018; Kulkarni et al., 2012; Pauwels et al., 2004; Rahman, 2017 Environmental impact Increasing

sustainability importance (§2.4) Cooper and Kleinschmidt, 1987; Fichtinger et al., 2015; Hua et al., 2011

TABLE 2.1 Factors that have a(n) (in)direct relationship with the inventory decision making process

2.1 Factors that influence future demand

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8 Level of holding and backlogged inventory

When a product is launched to the market there are three options: demand can be satisfied from current stock, the customer waits until demand is satisfied, or demand is lost (San-José et al., 2009). Unless demand is predictable, the trade-off for firms exists between holding inventory in stock or keeping inventory backlogged. In some cases stocking large quantities stimulates demand, which makes it important to have a sufficient amount of inventory (Balakrishnan et al., 2004). Also, stock-outs decrease customer loyalty and as a result customers switch retailers or substitute for a similar item (Craig et al., 2016; Bijvank and Vis, 2011). In this case, holding inventory buffers against product unavailability when forecast accuracy cannot be guaranteed (Nagashima et al., 2015). In other cases demand is driven by low product availability since customers find items more desirable and abundant inventory may signal an unpopular product (Craig et al., 2016). This way the balance between inventory in stock and inventory in backlog influences future demand.

2.2 Factors that indicate future demand

To reduce demand uncertainty, it is important to understand the factors that indicate future demand of new products from an operations perspective. One of these factors is the product life cycle of a new product. This can be used as the main source of demand dynamics: every stage represents another amount of demand (Seifert et al., 2016). Also, sales data of comparable products is a factor to indicate future demand of a new product (Lee et al., 2012; Yu et al., 2011). The last factor that indicates future demand is internet search terms, since customers are with the increasing e-commerce more likely to search on the internet for new product information (Kulkarni et al., 2012).

Product life cycle

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9 Sales data of comparable products

Firms use sales data of comparable products as input for their demand forecasting (Lee et al., 2012; Yu et al., 2011). New products before their product launch do not feature historical demand data, which makes sales data of comparable products an important alternative predictor of future demand (Lee et al., 2012; Yu et al., 2011). Two models applied to the fashion and food industry haven proven that using real sales data of comparable products predicts future demand of the new product (Lee et al., 2012; Yu et al., 2011). In the fashion industry, the Intelligent Fast Sales Forecasting Model (IFSFM) is able to achieve a fast in time and good forecast accuracy for new product sales (Yu et al., 2011). IFSFM uses sales data of comparable fashion products to generate the right parameters for the forecasting model (Yu et al., 2011). Important inputs for these comparable products are colour, size and price of the fashion products to generate sales amount as output (Yu et al., 2011). In the food industry, the Enhanced Cluster and Forecast Model (ECFM) is applied to increase forecast accuracy and is proven to be more efficient than other models (Lee et al., 2012). Real sales data of comparable products is also used as input factor of the ECFM to generate sales amount as output (Lee et al., 2012). The results of the ECFM show the same trend in the data with sales data of comparable products and with real data value of the new product (Lee et al., 2012). The sales data of comparable products indicates in this context future demand of a new product before its launch.

Internet search terms

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10 2.3 Factors that stimulate future demand

From a marketing perspective, future demand can be stimulated by different factors. Two important demand stimulators are the increase of customer loyalty in the firm (Hoyer et al., 2010) and using advertisement and publicity for the new product (Pauwels et al., 2004).

Customer loyalty

Customer loyalty is an important factor to stimulate product sales, since decrease in customer loyalty results in customers switching retailers (Craig et al., 2016). Also, there exists a direct relationship between co-creation and customer loyalty (Hoyer et al., 2010). The increase of customer loyalty is explained by the closer fit between the products and consumer interests (Hoyer et al., 2010). When customers are actively integrated and empowered to make decisions, it results in a stronger demand for the underlying products since customers feel a more psychological ownership of the product (Fuchs et al., 2010; Hoyer et al., 2010). Determining consumer interests in this way helps inventory decision makers in determining the right inventory levels before product launch (Rahman, 2017). Also, customer relationship management has a high effect on new product sales and a positive effect on new product performance (Ernst et al., 2011). These studies reflect how a close relationship with the customer stimulates demand of products. This study investigates if this relationship is also important with the inventory decision making of new shopping products before their launch. Advertisement and publicity

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11 accurate procurement decision of products before their launch can be made based on consumer interests (Rahman, 2017). These studies embrace more the marketing champagnes than how they are applicable before new product introductions. This study investigates if these factors are also important for the inventory decision making process of new shopping products before their launch.

Additionally to factors from an operations and marketing perspective, environmental factors are also important to understand what makes a new product a success (Cooper and Kleinschmidt, 1987). Besides, the success of new products can be affected by their environmental impact (Fichtinger et al., 2015).

2.4 Environmental impact on inventory decisions

An arising inventory decision factor in literature, but still neglected by many papers, is the impact of inventory on the environment (Fichtinger et al., 2015). The impact on the environment is important for the inventory decisions of new products, since there exists an almost linear relationship between energy consumption and order quantity (Fichtinger et al., 2015). The retailers order decisions are influenced by emissions related to production (Hua et al., 2011). Ordering smaller quantities is a characteristic of supply chain agility and results in lower warehouse emissions (Fichtinger et al., 2015). Besides, inventory costs can be reduced when inventory decisions are focused on the environmental impact (Fichtinger et al., 2015). For example, offshoring leads to higher warehouse emissions because of greater storage space required, higher safety stock levels and greater energy use (Fichtinger et al., 2015). In an optimal situation, managers make decisions where they need to optimize cost and the environment against a given service level (Fichtinger et al., 2015).

All factors discussed in this chapter have a direct or indirect relationship with the inventory decision making process. It is not precisely clear from literature if these factors are taken into account in the inventory decision making process of new shopping products and how important these factors are. A multiple case study is performed to fill this gap.

3. METHODOLOGY

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12 to search for a valid result (Karlsson, 2016: 175). This section explains why a multiple case study suits best and how it is performed.

Research design

In order to answer the research question, inventory decision makers of shopping products are approached. Early exploratory research is necessary since the factors reviewed in Chapter 2 are not explored in a context of new shopping products. This study seeks if the factors are applicable to the situation with new shopping products before the launch. Early exploratory research is a strength of multiple case study and therefore the first reason why a case study is performed (Karlsson, 2016: 167). To find which factors are important to organisations in practice for the inventory decision making process, this process needs to be observed in its natural setting. This is necessary because decisions can change over time, rely highly on human variability and can therefore differ between organisations. The unit of analysis of this research is the inventory decision making process, which enables to compare the decisions of different organisations after the results are generated. Observing the inventory decision making process in its natural setting is another strength of case study and therefore the second reason why a multiple case study is performed (Karlsson, 2016: 167). Thereafter, the inventory decision makers need to assess the factors on their importance, which is only possible by asking open interview questions as: “What factors are important? How important are these factors? Why are these factors important?”. A multiple case study method allows these questions best (Karlsson, 2016: 167).

Case selection

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13 relevant to decide on the scope of this study and the case selection criteria. From the experience of the software developing organisation it turned out that the decision making authority is located in some organisations at the supplier side. Since the unit of analysis in this study is the inventory decision making process and a case study is performed to observe and explore this process in its natural setting, the authority of the decision making must be located at the investigated firm. Therefore organisations with no authority in the decision making are excluded from this study. Another insight from the preliminary interview was the importance of intrinsic motivation of an organisation to launch a new product. According to them, an organisation who launches a new product to the market under external pressure does not consider the same factors as organisations who launch a product because of intrinsic motivation. The cases studied in this research need to be comparable, which makes product launches by intrinsic motivation another case criteria. The last case criteria, a diversity of online and offline organisations, is based on Chapter 2. Chapter 2 shows the importance of ‘internet search terms’ in online organisations which makes it interesting to investigate how online and offline organisations relate to each other in the inventory decision making. The case selection criteria are visible in Table 3.1.

Organisation/ Case selection criteria

A B C D E F G H

The product is a shopping product

Yes Yes Yes Yes Yes Yes Yes Yes

The product is recently launched

The decision making authority is within the company

The product launch is because of intrinsic motivation

Diversity of online and offline

Online Online and offline

Online Online Mostly offline Mostly offline Online and offline Mostly offline

TABLE 3.1 Case selection criteria

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14 this is an early exploratory research and not all outcomes can be predicted by literature. The output generated by the cases is comparable and makes cross-case analysis possible.

Taken the case selection criteria and the literature in Chapter 2 into account it is interesting to study organisations that possess over their own inventory of shopping products in a combination of offline and online oriented organisations. Eight cases are selected to realize generalizability of the results. Eight cases gives this study enough room to gain in-depth results for analysing the inventory decision making process. More cases would harm the in-depthness of the interviews and thus the results because of a time constraint. The final case description is visible in Table 3.2.

Case Sort company Industry Online/offline

A Reseller of electronic parts Computer industry Online B Reseller of mobile phones and contracts Telecommunications

industry

Online and offline

C Reseller of clothes Clothes industry Online D Reseller of books, toys and electronics Retail industry Online E Reseller of pharmaceutical products Retail industry Mostly offline F Reseller of garden and pet products Furniture industry Mostly offline G Reseller of pregnancy and baby products Child industry Online and

offline

H Online department store Agri Retail industry Mostly offline TABLE 3.2 Case descriptions

Operationalization of the concepts and variables

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15 according to the interviewee on a scale of 1 to 10. A 10 means ‘most important factor in the inventory decision making process of new shopping products’ and 1 means ‘least important factor in the inventory decision making process of new shopping products’.

Data collection

This study has an exploratory design, which means that is uncertain if all factors are covered by literature. Therefore semi-structured interviews suits best, because the interview scheme leaves space open for the interviewee to complement the theory in this study. This means the data collection process is a combination of deductive and inductive processes (Karlsson, 2016: 83). The interview starts inductive where the interviewee can come up with new factors that can complement theory and thus the conceptual framework. The second part is deductive in the sense of using the conceptual model as frame for testing if the factors according to theory are taken into account in a context of new shopping products. These semi-structured interviews are with an authorized inventory decision maker. The interview scheme (Appendix A) is designed based on the theory and is used to balance the input from the interviewer and interviewee. Every interview was recorded and performed with two interviewers to enhance the output of the questions and subjects. Anonymity of the results is guaranteed when this is preferred by organisations. This is important since organisations are more likely to participate in the research and it prevents this study from socially desirable answers of the interviewee. After the interviews were transcribed they were sent to the interviewees to be cross checked on interpretations and improve validity. This not only increases the validity, but also the reliability, since the interviewee confirms its own answers at a later moment.

Validity and reliability

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16 beforehand of the interview with the interviewee, as well as explaining the subjects as clear as possible during the interview. This way the meaning of the subjects represents what this study is investigating. The last way to increase the internal validity is by asking different questions that measure the same outcomes. Another quality criteria for this study is reliability. To increase the reliability the same interview protocol is used for every interview, which also enables cross-case analysis after data collection.

Data analysis Part 1: Coding

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17 Data analysis Part 2: Assessment

When all the factors taken into account by the inventory decision makers are clear, they are assessed on their importance. This process is deductive and inductive, since the assessment of factors finds place after the data is gathered. Highlighting the difference in importance of the factors is done by a forced ranking scale from 1 (least important) to 10 (most important). This way the importance of the factors by the decision maker is measured, as well as the relations between the factors (Karlsson, 2016: 120). The table and assessment design is shown in Appendix C. In the end a list of factors sorted on their importance by decision makers is developed.

4. FINDINGS

This chapter shows the factors that the interviewees take into account into the inventory decision making process of new shopping products before the launch. Within the findings, it is important to remember the connection and difference between demand forecast and the decision on the initial inventory level of a new product, which are both part of the inventory decision making process and explained in the beginning of Chapter 2. The findings are structured based on the third level codes of the final coding tree (Appendix B). The final coding tree also includes factors that were not included in the start coding tree, since they came up during the interview process. These are visible in the coding tree under the third level code ‘new factors’ and shown in Section 4.5. All factors are assessed on their importance in Section 4.6 and visualized in Table 4.1.

4.1 Factors that influence future demand

This section explains the importance of the level of holding inventory for all cases.

Level of holding and backlogged inventory

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18 products, because 6 would be too less”. Also Organisation B measures availability by taking lost sales into account: “When a customer visits our shops and wishes to leave the shop with a phone we do not have in stock, it is a lost sale and bad availability. Despite the fact that we can deliver it the next day through our omni-channel system”. Organisation B highlights here the difference between forecasting based on sales and forecasting based on demand. To ensure product availability, almost all organisations incorporate a service level into their ABC classification. However the organisations highlight that the service level can only be applied after a product has been to the market for a couple months. Incorporating a service level is therefore not applicable to new shopping products. To ensure product availability without a strict service level organisations tend to have more holding inventory of the new shopping products than they expect they would need.

4.2 Factors that indicate future demand

This section explains if and why the product life cycle, sales data of comparable products and internet search terms are taken into account by the organisations.

Product life cycle

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19 Sales data of comparable products

All organisations take sales data of comparable shopping products into account during the demand forecast and the initial inventory level decision making before the launch. Only Organisation H adds to this the percentage of lost sales of a comparable shopping product to decide on the initial inventory level of a new shopping product. Organisation E includes besides sales data also the level of promotions of a comparable shopping product and searches then for a comparable sales period to forecast demand and decide on the initial inventory level of the new shopping product.

Internet search terms

Organisation D is the one organisation that takes internet search terms into account during demand forecast and inventory decision making before the launch: “We call them views: how often people watch this product”. Three of the eight organisations measure the internet search terms of a shopping product, but do not use them as input for their initial inventory level decision making: “We do not include it in our inventories, but we do measure it” (Organisation A) and “The results are not inventory related” (Organisation B). The other four organisations give reasons why they do not measure the internet search terms: “No, we do not use it. I would like to use it, however our website is not so big yet, it is comparable to one or two shops” (Organisation H), “No, I do not know exactly, we do not use it, that is not my department” (Organisation G) and “I do not think so, it is not a daily process” (Organisation F). Related to internet search terms is the possibility for a pre-order. Organisation B and Organisation D have a system which allows customers to make pre-orders on a new shopping product before it is launched. The number of pre-orders is an important input for demand forecast and initial inventory decisions: “Depending on the level of customers that pre-ordered a new product on our website, we adjust the level of inventory we pre-ordered” (Organisation B).

4.3 Factors that stimulate future demand

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20 Customer loyalty

Organisation B is the one organisation that uses customer loyalty as input for their demand forecast and initial inventory levels of new shopping products: “We have new customers and contracted customers, with the contracted customer we look at the time of contract and when they have to assign a new contract”. Other organisations do not take sales data of specific customers into account when deciding on the inventory level of new shopping products. They mention that they do not mind what customers previously bought.

Advertisement and publicity

All organisation confirm that they collaborate with marketing about the promotions of a new shopping product. Organisation E, H and F compare the promotion planned for the new shopping product with a promotion of a previous shopping product to create a demand forecast and decide on the initial inventory level of the new product: “We look at the sales of a comparable product when it was promoted the same way” (Organisation H) and “The introduction promotion of the new article is compared with a promotion of a comparable product which leads to a comparable period of sales” (Organisation E). No organisation highlights the importance of tryvertising campaigns through social media.

4.4 Environmental impact on inventory decisions

All organisations are aware of the importance of sustainability, however this is not transferred into their inventory management. Four organisations mention specifically that they order full pallets or collies from an efficiency point of view instead of a sustainability point of view.

4.5 New factors

The factors in this section were not included in the start coding tree, nonetheless they came up during the interview process. The factors are added to the coding tree under the third level code ‘new factors’.

Physical shops

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21 new article we look at where it is situated in the shop and in how many shops” (Organisation H) and “We look at the number of shops and if this is increased compared to the last time we introduced a comparable product. The location of the product in our shops is determinative for the inventory level” (Organisation E).

Return policies of the supplier

The results show that return policies of the supplier with a new shopping product are important for the inventory decision making. Yet there exists a difference between organisations that purchase their shopping products at a supplier or when organisations co-create the shopping product with the supplier. Three organisations mention that the return policies of the supplier indicate how cautious they purchase their new shopping products before the launch: “We would increase our inventory when we have good return policies, when we do not have these good policies we would be more careful with our inventory” (Organisation A) and “Especially with fashion, we have very flexible suppliers which we take more risks with, and when the supplier is not that flexible we purchase more safely” (Organisation C). Also Organisation D makes use of the return policies in the inventory decision making of shopping products before the launch: “Most often we have the possibility to return product to the supplier, this way the inventory risk decreases. In this case we would order more inventory to be sure that we can serve the customer”. Three organisations who co-create the new shopping products with the supplier, do not possess over this luxury: “I wish we could do that, we do not have that option because we most often design our own products” (Organisation F), “When the supplier designed the product especially for us, we have to purchase it from him” (Organisation G) and “We do not have return policies, this is because most products are customized for us” (Organisation B). Two organisations do not bother that much about return policies, because they sell their new shopping products either way: “We can take some more risks, because even when we order way too much we would sell the products within six weeks” (Organisation C). Also Organisation E takes some more risks: “That does not apply to our company. We only have good return policies when we sell products that are irregular for us, like train tickets”.

Experience and knowledge of employees

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22 the purchasers we come to a consensus of our initiative inventory” (Organisation C) and “At last, it is gut feeling if we adjust the number of our initiative inventory or not” (Organisation E). Organisation H also applies it in the demand forecast: “The category manager gives a forecast, which is based on knowledge, experience and the feeling the category manager has with the new product” and Organisation F does too: “Sometimes it is based on feeling”. Organisation H even deviates the ordering advice sometimes given by the forecasting software because of the experience of employees. The other four organisations did not mention using the experience and knowledge of employees.

Input from different disciplines

Three organisations combine the input of different disciplines as purchasing and marketing to form a demand forecast and use as input for the inventory decision making process of new shopping products: “We depend on our internal stakeholders as sales and marketing, and our managers who perform the negotiating and make price arrangements with the suppliers” (Organisation B). Other organisations combine more the disciplines related to operations management: “The employee within the Buying and Sales department collaborates closely with Supply Chain Management, which is responsible for Operations, in different contexts” (Organisation C). Also Organisation F highlights this: “The first part of the inventory decision making is in collaboration with product management and supply chain, however eventually product management is responsible”. Two organisations do not (yet) apply this collaboration in their inventory decision making. Organisation G highlights: “We do not collaborate with marketing about our inventory decisions, that is our responsibility”. Organisation A mentions their wish for collaboration between these disciplines: “We are in a transition phase with our departments right now, we would like to achieve a collaboration between the category managers, marketing and sales when we introduce a new product”. Three organisations did not mention that the input from different disciplines is important to them in the inventory decision making of new shopping products.

Other factors

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23 the ordering advice from the forecasting software” (Organisation H). Second, organisations take into account product cannibalisation when they decide on the inventory of the new shopping products before the launch: “When we introduce a new shopping product, we look at product cannibalisation” (Organisation H) and “When we introduce a new shopping product it can be substitute or a complement product” (Organisation E). Both products have a different consequence for inventory decision making. Third, the number of channels an organisation is going to sell their new shopping product through has an influence on the inventory decision making: “Online is our biggest shop, if we sell products through multiple channels we would increase our initial inventories” (Organisation G). The last important factor mentioned by two organisations is combination products: “We take into account what new shopping products are more likely to be sold together, if we introduce one product, we would also increase the inventory of the other product” (Organisation A) and “We align the inventories of new shopping products that are most likely to be sold together” (Organisation G).

4.6 Importance of factors

All factors that are taken into account during the inventory decision making of new shopping products are ranked by the organisations on a scale from 1 to 10, meaning ‘least important’ to ‘most important’. The results of the ranking are visible in Table 4.1. First the factors are shown from the start coding tree. Next, the new factors that came up during the interview and were not included in the start coding tree are visualized. New factors Part 1 are mentioned by multiple organisations and are applicable to multiple organisations. New factors Part 2 are factors mentioned by one organisation. The table shows that within the start factors, sales data of comparable products and advertisement and publicity are most important. Sustainability is least taken into account during the inventory decision making of new shopping products. Within new factors Part 1 physical shops is most important to three organisations. Next, weather and season is assessed as most important by two organisations as well as return policies of the supplier. As last, the experience and knowledge of employees is weighted as very important by two organisations.

Factor\Case

A B C D E F G H

Total

Start factors

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PLC 6 7 3 10 2 8 8 5 49 out of 80

Sales data comparable products 7 8 8 10 10 9 9 7 68 out of 80

Internet search terms 7 4 7 10 1 6 5 1 41 out of 80

Customer loyalty 5 5 3 10 4 4 6 1 38 out of 80

Advertisement and publicity 6 6 10 9 10 7 9 8 65 out of 80

Environmental impact 9 3 0 7 3 6 7 1 36 out of 80

New factors Part 1

Return policies of the supplier 10 6 10 8 1 35 out of 50 Experience and knowledge of

employees

8 8 5 21 out of 30

Physical shops 10 10 10 30 out of 30

Season/weather 10 9 19 out of 20

New factors Part 2

Product cannibalisation 3 3 out of 10

Sales channels 9 9 out of 10

Lead time 10 10 out of 10

Introduction price 3 3 out of 10

Master data check 7 7 out of 10

Pre-orders 10 10 out of 10

Product specifications 5 5 out of 10

Internal sales targets 7 7 out of 10

Inventory of ‘competitor’ 8 8 out of 10

TABLE 4.1 Ranking of factors

5. DISCUSSION

This chapter discusses the findings and contributions of this study to the operations literature compared with the literature of Chapter 2 and new literature. As is stated before, it is important to remember the connection and difference between demand forecasting and the decision on the initial inventory level of a new shopping product, which are both part of the inventory decision making process. Taken this into account, table 5.1 summarizes what is explained in this chapter. It shows how the factors from Chapter 2 are used for forecasting demand and used as direct input for the initial inventory level of new shopping products, compared between what literatures states and what this study found.

Factors\Used for

Literature This study

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25 comparable products

Advertisement and publicity

X X X

Product life cycle X X X

Internet search terms X X X X

Customer loyalty X X X X

Environmental impact

X

TABLE 5.1 Comparison of factors applied in literature and this study

Factors applied by all organisations

According to all organisations it is important to take the level of holding inventory into account during the inventory decision making process of new shopping products to ensure product availability. It is also ranked by almost all organisations as ‘very important’. Holding inventory is in literature a buffer against product unavailability when forecast accuracy cannot be guaranteed (Nagashima et al., 2015). Literature did not specify this relationship for a specific product group, this study shows new insights in using the level of holding inventory as buffer in a context of new shopping products.

Also sales data of comparable products is assessed as ‘very and most important’ by all organisations. Existing literature shows that sales data of comparable products is used to forecast demand in the fashion and food industry (Lee et al., 2012; Yu et al., 2011). Current literature only investigated this in the demand forecast of new products in the fashion and food industry, this study highlights how it can be applied in the inventory decision making process of more industries. The organisations in this study show that besides using historical demand of a comparable product to forecast future demand of the new product, organisations also look at the level of product unavailability of the comparable product. This is used as input to decide on the initial inventory level of the new product.

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26 shopping products before the launch. This study shows how important advertisement and publicity is to forecast demand and decide on the initial inventory level in a context of new shopping products. The results confirm how important determining consumer interest is as predictor of future demand and inventory decisions (Rahman, 2017).

Factors applied by a few organisations

The findings in Section 4.2 show that only three organisations apply the product life cycle in the inventory decision making of new shopping products. This is also visible in the ranking where the assessment of the product life cycle differs from ‘least important’ to ‘most important’. Where literature explains how the product life cycle predicts future demand and how organisations base their procurements decisions on it (Restuccia et al., 2015; Acimovic et al., 2017), three organisations in this study do not use the product life cycle to forecast demand. They use the product life cycle to decide on the initial inventory level of new shopping products. Other organisations do not use the product life cycle because their products are too expensive or they cannot make it insightful for an individual product. Further investigation in literature shows that these reasons are refuted by explaining that demand information from the product life cycle can be applied to all products with a short product life cycle (Seifert et al., 2016). In addition, for a better financial performance it is important that organisations focus on individual products (Nath et al., 2010).

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27 instead of the relationship between internet search terms and inventory decision making. Despite this, Kulkarni et al. (2012) highly recommend these organisations to take the internet search terms into account in the demand forecasting since it shows in the pre-launch phase predictable sales patterns for the future.

The last factor only applied by Organisation B is customer loyalty. Literature states that customer loyalty results in a stronger demand (Fuchs et al., 2010; Hoyer et al., 2010) and helps determining the right inventory level before the product launch (Rahman, 2017). Where current literature only acknowledges these relationship with nonspecific products, this study shows new insights in how customer loyalty is applied in the demand forecast and initial inventory decision making of new shopping products. Taking the case selection criteria into account it is a logic outcome that the other seven organisations do not use customer loyalty in their inventory decision making since they do not work with contracts or co-create the product with the customer, as was stated in Section 2.3.

No environmental impact in the inventory decision making

The findings in Section 4.4 show that the organisations in this study do not take the environmental impact into account during the inventory decision making of new shopping products. This was unexpected since literature shows the positive relationship between reduced inventory costs when inventory decisions are focused on the environmental impact (Fichtinger et al., 2015). The trade-off described by Fichtinger et al. (2015) between costs and the environment during the inventory decision making came up by four organisations, however this study shows how organisations still make their decision from an efficiency point of view. During the interviews it was notable that some organisations did assess environmental impact with a high number to protect their image, as is shown in Section 4.6. The findings were not expected since environmental and social indicators are increasingly taken into account in the organisational decision making (Adams and Frost, 2008). However another look in literature gives a possible explanation: there is a lack of conceptual frameworks and theoretical grounds in applying environmental management in supply chains (Wong et al., 2015).

New insights for operations literature

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28 in Chapter 2. This section reviews how these factors create new insights in the operations literature.

(1) Physical shops: Section 4.5 shows that organisations take the number, size and location of the physical shops into account during the inventory decision making. Argawal and Smith (2013) state that taking these variabilities into account results into an improved retailers’ profit. This study extends this literature by applying it in the context of a new shopping product before the launch. Taken the case selection into account it is a logic continuation that only the offline organisations came up with this factor and assessed it as ‘most important’.

(2) Return policies of the supplier: Section 4.5 shows that three organisations take more risks in the inventory of new shopping products when they have flexible return policies with their supplier. This study adds this new insight to the operations literature since the phenomenon where return policies are used by organisations to mitigate demand uncertainty is not captured yet. Besides, this study complements existing literature that suggests other ways to mitigate demand uncertainty. An example in literature is the risk-sharing-contract where Ghadge et al. (2017) adds a relational perspective into the buyer-supplier-contract to mitigate demand uncertainty. Also, social capital in a buyer and supplier relationship contributes to mitigating demand uncertainty since it positively affects buyer performance (Villena et al., 2010). For the organisations in this study who take more risks in their inventory of new shopping products when they have flexible return policies with the supplier, it is important to take the downsides of this relationship into account. A collaboration between a buyer and supplier can result in a negative agility performance of the supplier (Narayanan et al., 2015), as well as the influence of the performance of the buyer by the power imbalance of the supplier (Reimann and Ketchen, 2017). Despite the downsides, flexible return policies of suppliers deserves a place in the operations literature since the organisations in this study highlight it as important.

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29 take all short term changes into account and rely in these cases on the experience of employees. This study creates new insights in applying experience and knowledge of employees into the inventory decision making. When further investigating the literature, it is already investigated that intuition of experts is an important part of general organisational decision making (Salas et al., 2009). Understanding how they use their intuition can have a great influence on organisational effectiveness and practices and it contributes in for example time-pressured situations to the effectiveness of the decision making (Salas et al., 2009). Although literature highlights the benefits of intuition in the organisational decision making, this study found that these benefits are also applicable in the inventory decision making of a new shopping product.

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30

6. CONCLUSION

The main objective of this research is investigating the factors that are taken into account in the inventory decision making process of new shopping products before the launch and how important they are. It is important to study these factors since literature has proven that the diversity and short product life cycle of shopping products make it extremely difficult for decision makers to make inventory decisions (Chien et al., 2010). This study contains a multiple case study among eight organisations. They are selected based on their intrinsic motivation of launching a new shopping product where the decision making process is located in the investigated organisation and the shopping product has been launched recently. Also a diversity of online and offline oriented organisations are selected. The data of the interviews is coded to create structure and perform a within and cross-case analysis.

This study adds new insights to the operations literature in different manners. First of all, this study shows how the factors acknowledged in literature are applied in demand forecasting and inventory decision making process of new shopping products. Strikingly only a few organisations in this study use the product life cycle of new shopping products in the inventory decision making, where literature shows how they can be used for demand forecasting as well. Also, applying internet search terms in the demand forecasting and inventory decision making of new shopping products happens only within one of the three online organisations. Where other organisations are aware of the growing e-commerce, they face the challenge of translating the data of internet search terms into forecasting data and into inventory decisions. The factor that all organisations agree on is holding inventory as buffer against product unavailability. New insights for the operations literature is the use of sales data of comparable products and, advertisement and publicity in the demand forecast and inventory decision making of new shopping products.

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31 contrast to the growing data driven decisions according to operations literature. The last contribution to the operations literature is the use of input from different disciplines in the organisations in demand forecasting and inventory decision making process of new shopping products. Even though marketing literature paid attention to this subject, the operations literature does not acknowledge this yet. Besides the theoretical contribution, the practical contribution of this study is improving the inventory decision making process by applying the obtained factors in practice.

Limitations and future research

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32

REFERENCES

Acimovic, J., Erize, F., Hu, K., Thomas, D. J., & Mieghem, J. A. V. (2018). Product Life Cycle Data Set: Raw and Cleaned Data of Weekly Orders for Personal Computers. Manufacturing & Service Operations Management, Articles in Advance, 1-6.

Adams, C. A., & Frost, G. R. (2008, December). Integrating sustainability reporting into management practices. Accounting Forum, 32(4), 288-302. Elsevier.

Agrawal, N., & Smith, S. A. (2013). Optimal inventory management for a retail chain with diverse store demands. European Journal of Operational Research, 225(3), 393-403.

Atan, Z., & Snyder, L. V. (2012). Inventory strategies to manage supply disruptions. Supply Chain Disruptions, 115-139. Springer, London.

Balakrishnan, A., Pangburn, M. S., & Stavrulaki, E. (2004). “Stack them high, let’em fly”: Lot-sizing policies when inventories stimulate demand. Management Science, 50(5), 630-644.

Baum, D., Spann, M., Füller, J., & Thürridl, C. (2018). The impact of social media campaigns on the success of new product introductions. Journal of Retailing and Consumer Services.

Benedetto, C. A. (1999). Identifying the key success factors in new product launch. Journal of Product Innovation Management, 16(6), 530-544.

Bijvank, M., & Vis, I. F. (2011). Lost-sales inventory theory: A review. European Journal of Operational Research, 215(1), 1-13.

Bourland, K. E., Powell, S. G., & Pyke, D. F. (1996). Exploiting timely demand information to reduce inventories. European journal of operational research, 92(2), 239-253.

(33)

33 Burmester, A. B., Becker, J. U., van Heerde, H. J., & Clement, M. (2015). The impact of pre-and post-launch publicity pre-and advertising on new product sales. International Journal of Research in Marketing, 32(4), 408-417.

Cachon, G. P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management science, 46(8), 1032-1048.

Chaturvedi, A., & Martínez‐de‐Albéniz, V. (2016). Safety Stock, Excess Capacity or Diversification: Trade‐Offs under Supply and Demand Uncertainty. Production and operations management, 25(1), 77-95.

Chaudhuri, A., Mohanty, B. K., & Singh, K. N. (2013). Supply chain risk assessment during new product development: a group decision making approach using numeric and linguistic data. International Journal of Production Research, 51(10), 2790-2804.

Chien, C. F., Chen, Y. J., & Peng, J. T. (2010). Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. International Journal of Production Economics, 128(2), 496-509.

Ching-Chin, C., Ieng, A. I. K., Ling-Ling, W., & Ling-Chieh, K. (2010). Designing a decision-support system for new product sales forecasting. Expert Systems with applications, 37(2), 1654-1665.

Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2-9.

Claessens, M. (2017). Four types of consumer products and marketing considerations. Marketing Insider.

Cooper, R. G., & Kleinschmidt, E. J. (1987). New products: what separates winners from losers?. Journal of Product Innovation Management, 4(3), 169-184.

(34)

34 Debruyne, M., Moenaertb, R., Griffinc, A., Hartd, S., Hultinke, E. J., & Robben, H. (2002). The impact of new product launch strategies on competitive reaction in industrial markets. Journal of Product Innovation Management, 19(2), 159-170.

Demeter, K. (2003). Manufacturing strategy and competitiveness. International Journal of Production Economics, 81, 205-213.

Disney, S. M., Maltz, A., Wang, X., & Warburton, R. (2015). The safety stock and inventory cost paradox in a stochastic lead time setting. Production and Operations Management, 26, 1-10.

Ernst, H., Hoyer, W. D., Krafft, M., & Krieger, K. (2011). Customer relationship management and company performance—the mediating role of new product performance. Journal of the academy of marketing science, 39(2), 290-306.

Fichtinger, J., Ries, J. M., Grosse, E. H., & Baker, P. (2015). Assessing the environmental impact of integrated inventory and warehouse management. International Journal of Production Economics, 170, 717-729.

Fuchs, C., Prandelli, E., & Schreier, M. (2010). The psychological effects of empowerment strategies on consumers' product demand. Journal of Marketing, 74(1), 65-79.

Ghadge, A., Dani, S., Ojha, R., & Caldwell, N. (2017). Using risk sharing contracts for supply chain risk mitigation: A buyer-supplier power and dependence perspective. Computers & Industrial Engineering, 103, 262-270.

Hopp, W. J. & Spearman, M. L. 2008. Factory Physics (3rd ed.). U.S.A.: Waveland Press Inc.

(35)

35 Hultink, E. J., Hart, S., Robben, H. S., & Griffin, A. (2000). Launch decisions and new product success: an empirical comparison of consumer and industrial products. Journal of Product Innovation Management, 17(1), 5-23.

Hoyer, W. D., Chandy, R., Dorotic, M., Krafft, M., & Singh, S. S. (2010). Consumer cocreation in new product development. Journal of service research, 13(3), 283-296.

Kahn, K. B. (2002). An exploratory investigation of new product forecasting practices. Journal of Product Innovation Management, 19(2), 133-143.

Karlsson, C. 2016. Research methods for operations management (2nd ed.). London and New York: Routledge.

Kulkarni, G., Kannan, P. K., & Moe, W. (2012). Using online search data to forecast new product sales. Decision Support Systems, 52(3), 604-611.

Landeta, J. (2006). Current validity of the Delphi method in social sciences. Technological forecasting and social change, 73(5), 467-482.

Larivière, B., Bowen, D., Andreassen, T. W., Kunz, W., Sirianni, N. J., Voss, C., ... & De Keyser, A. (2017). “Service Encounter 2.0”: An investigation into the roles of technology, employees and customers. Journal of Business Research, 79, 238-246.

Lee, Y., & O'Connor, G. C. (2003). New product launch strategy for network effects products. Journal of the Academy of Marketing Science, 31(3), 241-255.

Lee, W. I., Shih, B. Y., & Chen, C. Y. (2012). Retracted: A hybrid artificial intelligence sales‐ forecasting system in the convenience store industry. Human Factors and Ergonomics in Manufacturing & Service Industries, 22(3), 188-196.

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36 McLaren, N., & Shanbhogue, R. (2011). Using internet search data as economic indicators. Quarterly Bulletin.

Mollenkopf, D. A., Frankel, R., & Russo, I. (2011). Creating value through returns management: Exploring the marketing–operations interface. Journal of Operations Management, 29(5), 391-403.

Montaguti, E., Kuester, S., & Robertson, T. S. (2002). Entry strategy for radical product innovations: A conceptual model and propositional inventory. International Journal of Research in Marketing, 19(1), 21-42.

Narayanan, S., Narasimhan, R., & Schoenherr, T. (2015). Assessing the contingent effects of collaboration on agility performance in buyer–supplier relationships. Journal of Operations Management, 33, 140-154.

Pauwels, K., Silva-Risso, J., Srinivasan, S., & Hanssens, D. M. (2004). New products, sales promotions, and firm value: The case of the automobile industry. Journal of marketing, 68(4), 142-156.

Petersen, K. J., Handfield, R. B., & Ragatz, G. L. (2005). Supplier integration into new product development: coordinating product, process and supply chain design. Journal of operations management, 23(3-4), 371-388.

Piercy, N. (2010). Improving marketing–operations cross-functional relationships. Journal of Strategic Marketing, 18(4), 337-356.

Rahman, M. A., & Casanovas, L. (2017). Strategies to Predict E-Commerce Inventory and Order Planning. International Journal of Technology Diffusion (IJTD), 8(4), 17-30.

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37 Restuccia, M., de Brentani, U., Legoux, R., & Ouellet, J. F. (2016). Product Life‐Cycle Management and Distributor Contribution to New Product Development. Journal of Product Innovation Management, 33(1), 69-89.

Salas, E., Rosen, M. A., & DiazGranados, D. (2010). Expertise-based intuition and decision making in organizations. Journal of management, 36(4), 941-973.

San-José, L. A., Sicilia, J., & García-Laguna, J. (2009). A general model for EOQ inventory systems with partial backlogging and linear shortage costs. International Journal of Systems Science, 40(1), 59-71.

Seifert, R. W., Tancrez, J. S., & Biçer, I. (2016). Dynamic product portfolio management with life cycle considerations. International Journal of Production Economics, 171, 71-83.

Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., & Sinsoysal, B. (2016). OR/MS models for supply chain disruptions: A review. IIE Transactions, 48(2), 89-109.

Srinivasan, M., Sun, R., & Thapliyal, S. (2012). Allocating inventory levels. U.S. Patent No. 8,165,904. Washington, DC: U.S. Patent and Trademark Office.

Stock, G. N., Greis, N. P., & Fischer, W. A. (2001). Absorptive capacity and new product development. The Journal of High Technology Management Research, 12(1), 77-91.

Tanaka, K. (2010). A sales forecasting model for new-released and nonlinear sales trend products. Expert Systems with Applications, 37(11), 7387-7393.

Tang, O., & Musa, S. N. (2011). Identifying risk issues and research advancements in supply chain risk management. International journal of production economics, 133(1), 25-34.

Thonemann, U. W. (2002). Improving supply-chain performance by sharing advance demand information. European Journal of Operational Research, 142(1), 81-107.

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38 Villena, V. H., Revilla, E., & Choi, T. Y. (2011). The dark side of buyer–supplier relationships: A social capital perspective. Journal of Operations management, 29(6), 561-576.

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APPENDIX A Interview guide (Dutch version)

Introductie:

Bedankt dat u wilt meewerken aan ons onderzoek. Zoals uitgelegd in de email onderzoeken wij voorraadstrategiën voor en na de lancering van een product product. Het interview bestaat uit drie delen: de periode voor de productlancering, de periode na de productlancering en een ranking. Mocht de ranking niet meer binnen de tijd kunnen, dan versturen wij deze graag nog per email naar u toe.

Het interview duurt ongeveer 60 minuten. Zoals vermeld is de data compleet anoniem. Als u het goed vindt, zouden wij het interview graag 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. Hoe ziet de hiërarchie van uw organisatie eruit?

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40 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 1: periode voor de lancering

1. Welke factoren nemen jullie mee in het beslissingsproces over de voorraden?

Factoren die de vraag in de markt beïnvloeden: kosten & service level 2. Nemen jullie voorraadkosten mee in het voorraad beslissingsproces?

a. Ja:

i. Welke kosten nemen jullie mee? ii. Waarom nemen jullie deze kosten mee?

iii. Welke kosten heeft u liever dan andere kosten? b. Nee: waarom niet? Zouden jullie dit wel willen doen? 3. Is het mogelijk om backorders te plaatsen?

a. Hebben jullie liever te veel producten op voorraad of te weinig? 4. Hoe belangrijk is het om producten op voorraad te hebben?

a. Wanneer het product niet op voorraad is, zoeken klanten naar een alternatief product of wachten zij tot het product weer op voorraad is?

Factoren die de vraag aangeven: PLC, sales data van vergelijkbare producten, internet zoektermen

5. Wordt de product life cycle van het nieuwe product meegenomen in het voorraad beslissingsproces?

a. Ja: Hoe wordt dit gedaan?

b. Nee: waarom niet? Zouden jullie dit wel willen doen?

6. Wordt sales data van vergelijkbare producten meegenomen in het voorraad beslissingsproces?

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41 a. Ja: Hoe wordt dit gedaan?

Factoren die de vraag stimuleren: klant loyaliteit en advertenties en publiciteit 8. Wordt klantloyaliteit meegenomen in het voorraad beslissingsproces?

a. Ja:

i. Welke rol heeft de klant? ii. Hoe wordt dit gedaan?

9. Worden advertenties en publiciteit over het nieuwe product meegenomen in het voorraad beslissingsproces?

Omgeving/duurzaamheid

10. Wordt de impact op de omgeving meegenomen in het voorraad beslissingsproces? a. Ja: waarom en hoe?

Deel 2: periode na de lancering

11. Welke informatie is beschikbaar kort nadat jullie het product hebben gelanceerd? a. Hoe gebruikt u deze informatie voor de keuze van een voorraadstrategie? 12. Wanneer is deze informatie beschikbaar?

13. Kunt u nu informatie bedenken die u wel graag zou willen gebruiken, maar die momenteel niet gebruikt wordt bij de bepaling van de voorraadstrategie?

a. Hoe zou u deze informatie willen gebruiken?

Customer reviews

14. Verzamelen jullie informatie uit customer reviews?

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42 a. Verzamelt u informatie over het aantal reviews?

b. Verzamelt u informatie over verdeling tussen positieve en negatieve reviews? c. Verzamelt u informatie over snelheid waarmee de informatie zich verspreidt in

de markt?

17. Hoe gebruiken jullie deze informatie voor beslissingen over voorraadstrategieën? a. Hoe beïnvloedt informatie over volume, verdeling en snelheid de keuze voor

een voorraadstrategie? Publiciteit

18. Verzamelen jullie informatie uit publiciteit?

19. Nee: waarom niet? Denkt u dat het wel waardevol is om dit te gaan doen? 20. Ja: wat voor informatie verzamelt u uit publiciteit?

a. Verzamelt u informatie over de hoeveelheid publiciteit in de markt? b. Verzamelt u informatie over verdeling tussen positieve en negatieve

publiciteit?

c. Verzamelt u informatie over snelheid waarmee de informatie zich verspreidt in de markt?

21. Hoe gebruiken jullie deze informatie voor beslissingen over voorraadstrategieën? a. Hoe beïnvloedt informatie over hoeveelheid, verdeling en snelheid de keuze

voor een voorraadstrategie?

Sales data

22. Verzamelen jullie informatie van sales data?

23. Nee: waarom niet? Denkt u dat het wel waardevol is om dit te gaan doen? 24. Ja: welke informatie verzamelt u van sales data?

a. Gebruikt u uw eigen sales data?

b. Gebruikt u sales data van partners in de supply chain? c. Zo ja, van welke partners?

25. Hoe gebruiken jullie deze informatie voor beslissingen over voorraadstrategieën?

Adverteren

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43 28. Ja: hoe gebruiken jullie deze informatie voor beslissingen over voorraadstrategieën?

Marktdata

29. Verzamelen jullie informatie van marktdata?

30. Nee: waarom niet? Denkt u dat het wel waardevol is om dit te gaan doen? 31. Ja: Welke informatie verzamelen jullie over de markt waarin jullie je bevinden?

a. Gebruiken jullie informatie over huidige substituten in de markt? b. Gebruiken jullie informatie over acties en reacties van concurrenten? 32. Hoe gebruiken jullie deze informatie voor beslissingen over voorraadstrategieën?

a. Hoe gebruiken jullie de informatie van substituten en concurrenten voor de keuze van een voorraadstrategie?

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? Vindt u het goed als wij het transcript van het interview naar u toesturen voor bevestiging van onze interpretatie van de antwoorden?

Deel 3 Ranking:

Hoe belangrijk vindt u onderstaande factoren tijdens het voorraad beslissingsproces?

Factors Weight (0-10)

Inventory costs

Target service level

Product life cycle

Sales data comparable products

The inventory decision making process of new shopping products

The inventory decision making process

of new shopping products

Contents