Drivers of in- and outsourcing decisions made by planners in a collaborative transportation network.

Drivers of in- and outsourcing decisions made by planners in a collaborative

transportation network.

Master Thesis

MSc. Supply Chain Management University of Groningen. Faculty of Economics and Business

BY Justin Beentjes

S2482290

j.beentjes@student.rug.nl

Supervisor: Dr. ir. P. Buijs Co-assessor: Dr. X. Zhu

ABSTRACT

Purpose: This research addresses horizontal logistics collaboration among multiple carriers that are participating in a network setting. The main purpose of this research is to identify how the in- and outsourcing decisions of planners are influenced in a network of collaborating carriers.

Design / Methodology / approach: Data was gathered by conducting an exploratory case research. Multiple case studies have been executed among logistics planners, operations managers and general managers. Information is used from interviews, observations and documents.

Findings: This research constructed a inductive model with two dimensions that influence a planner’s in- and outsourcing decisions: i) level of network collaboration, and ii) level of adoption of systemized decisions. Each dimension in this model consists of multiple drivers that could move a planners in- and outsourcing decisions wihtin a network of collaborating carriers into a certain direction. It is found that these drivers are formed by either internal or external forces.

Research limitations / implications: Due to the explorative characteristic of this research some topics might be abstract and not fully generalizable to other industries. This leaves opportunities for further research to conduct more in-depth research on the drivers of in- and outsourcing decisions made by planners.

Originality: This paper offers a model with the drivers of planner’s in- and outsourcing decisions. Research on organizational causes that influence the planners’ behaviour is rather scarce in the field of behavioral operations management. Previous research has mainly focussed on techniques to optimize and solve route-planning problems by means of difficult algorithms

Keywords: Horizontal logistics collaboration, in- and outsourcing decisions, planners, joint network behaviour.

Paper type: Explorative multiple case study

CONTENT

ABSTRACT 2

1. INTRODUCTION 4

2. THEORETICAL BACKGROUND 6

2.1 Horizontal logistics collaboration 6

2.2 Collaborative transport management 7

2.3 Joint network behaviour 9

3. RESEARCH METHODOLOGY 10 3.1 Research design 10 3.2 Case selection 10 3.3 Data collection 11 3.4 Data analysis 14 4. FINDINGS 15

4.1 Dimensions of planning decisions 15

4.2 Main drivers on the level of network collaboration 16 4.3 Main drivers on the level of adoption of systemized decisions 20 4.4 Interrelations between drivers of planning decisions 24

5. DISCUSSION 27

5.1 Theoretical implications 27

5.2 Managerial implications 29

5.3 Limitations 29

5.4 Suggestions for further research 30

6. CONCLUSION 31

7. REFERENCES 32

8. APPENDICE 37

Appendix A. Research protocol 37

Appendix B. Case selection 39

1. INTRODUCTION

The transportation industry is highly competitive and the logistics service providers (LSP’s) are challenged with the growing pressure to increase their profitability (Verdonck, Caris, Ramaekers, & Janssens, 2013; Wang & Kopfer, 2014). In order to improve results, LSP’s can focus on reducing their operational costs and aim at increasing their efficiency (Gansterer & Hartl, 2018). Traditionally, these LSP’s relied on their internal potential and capabilities to reduce operational costs and to increase their efficiency and profitability (Verdonck et al., 2013). However, these internal potentials have been exhausted and in order to stay competitive, LSP’s are adopting a collaborative focus (Verdonck et al., 2013). This could lead to increased benefits that can hardly be achieved when working alone, such as competitive advantage and higher profits (Daudi, Hauge, & Thoben, 2016). Investing in developing stronger and mutually beneficial relationship with actors in the supply chain can either be done vertically with other customer or horizontally with other LSP’s that are proximate or distant competitors (Schmoltzi & Wallenburg, 2011). While acknowledging the important role of LSP’s in vertical collaboration, this research considers horizontal logistics collaboration (HLC) and focuses primarily on collaborative logistics planning and the eminent role of planners within that process.

LSP’s might consider HLC as an expected opportunity to reduce costs, increase productivity, improve service levels, and strengthen their market position (Cruijssen, Cools, & Dullaert, 2007a). However, LSP’s are still far away from utilizing all opportunities of HLC (van Dooren, 2017). The literature on HLC in land-based freight logistics is still in its infancy (Leitner, Meizer, Prochazka, & Sihn, 2011; Buijs & Wortmann, 2014; Pomponi, Fratocchi, & Rossi Tafuri, 2015; Sanchez Rodrigues, Harris, & Mason, 2015), which implies that there are still many gains that can be realised. (Sanchez Rodrigues et al., 2015). One of those gains can be made within the planning of collaborative logistics. Within HLC, LSP’s are collaborating with proximate or distant competitors and parts of these LSP’s logistics operations can be planned jointly (Gansterer & Hartl, 2018). Wang & Kopfer (2014) refer to this process of joint planning and decision making between LSP’s in a horizontal collaboration context as ‘collaborative transport planning’ (CTP). Creating a collaborative transport planning could be complex and it might be benificial to support the planners, who are responsible for the creation of a transport planning, with information technology (IT). However, current IT lacks the ability to offer a system that can support planners in making a transport planning by interactively exchanging orders (Buijs & Wortman, 2014).

planners is acknowledged by de Snoo, van Wezel, & Jorna (2011), who state that their transport planning activities have a large effect on business performance. Within a network setting, these planners play an even greater role, while their transport planning activities are directly linked to the performance of their partners within the network. Despite their importance, there is a lack of literature on the impact of horizontal collaboration on the daily tasks of the logistics planner individually (van Dooren, 2017). Next to this, Larco, Fransoo, & Wiers (2018) state that the activities performed by the planners should be investigated and registered. However, the role of planners has been taken for granted and no attention has been paid to their effect in a situation of network collaboration. Previous research has mainly focussed on techniques to optimize and solve route-planning problems by means of complex algorithms (De Snoo, 2011). Planners form the basis of making a transport planning and exchanging orders by deciding on which orders need to be in- or outsourced. Current literature lacks on analyses of the behavioral factors of planners that are related to their in- and outsourcing decisions. By knowing the factors that influece a planners’ in- and outsourcing decision, a next step can be made towards efficient joint planning of transport orders. Therefore, the aim of this reseach is to identify the drivers that influence the in- and outsourcing decisions of planners in a network setting. This aim results in the following research question:

- RQ. How are the in- and outsourcing decisions of planners influenced in a network of collaborating carriers?

To find an answer to the research question, an explorative case study will be conducted. This study has been done among different carriers who are part of a network. Multiple cases have been investigated by means on semi-structured interviews and direct observations. The semi-structured interviews have been conducted among general- and operations managers in order to gain in-depth informatation on the individual carriers to indentify the drivers that might influence their in- and outsourcing decisions. Next to this, direct observations on planners have been conducted to achieve insights on their daily tasks, usage of planning software and in- and outsourcing decisions.

2. THEORETICAL BACKGROUND 2.1 Horizontal Logistics Collaboration

Collaboration in logistics is one business strategy that has been thoroughly studied and discussed by practitioners in the literature (Schmoltzi & Wallenburg, 2012; Sanchez Rodrigues et al., 2015; Pomponi et al., 2015). Collaboration has been defined by Daugherty (2011) as “two or more companies sharing the responsibility of exchanging common planning, management, execution, and performance measurement information” (p.22). The idea behind collaboration is that companies who work together can jointly achieve a greater success than they could have done in isolation of each other (Daugherty et al., 2006). This is in line with Lambert, Emmelheinz, & Gardner (1999), who define logistic partnerships as “ the tailored business relationship based on mutual trust, openness, shared risk and shared reward that yields a competitive advantage resulting in business performance greater than would be achieved by the firms individually” (p.166).

Most of the literature that is focussed on collaboration in the supply chain is looking at the area of buyer-supplier collaborations (Van der Vaart & Van Donk, 2008; Lehoux et al., 2011; Wagner, Coley, & Lindemann, 2011; Nyaga, Lynch, Marschall, & Ambrose., 2013). This form of supplier-buyer collaboration is referred to as ‘vertical’ supply chain collaboration (Barratt, 2004). LSP’s play an important role in physically connecting the buyers and suppliers by means of freight transportation (Buijs and Wortman, 2014; Stank & Goldsby, 2000). According to Sanchez Rodrigues et al. (2015), LSP’s are realising that there is still a lot of wastes in their organisations. This leads to a renewed focus for the LSP’s to consider what, in addition to vertical collaboration, can be accomplished when the LSP’s will be collaborating and coordinating their logistics activities (Sanchez Rodrigues et al., 2015).

LSP’s have the possibility to horizontally collaborate with partner service providers by sharing or exchanging their transportation orders (Verdonck et al., 2013). These orders can vary in size, time restrictions, and pickup- and delivery locations (Buijs et al., 2016). In order to optimally share these customer orders between the collaborating LSP’s, there is a need for CTP (Verdonck et al., 2013). Within this order sharing method, the exchange of information among collaborating LSP’s is crucial for a successful partnership. This crucial activities are done by planners at the planning departments of the different LSP’s.

Most of the studies that deal with the exploration of horizontal collaboration are focusing on transportation management (Mason, Lalwani, & Boughton, 2007; Buijs & Wortmann, 2014; Buijs, Alvarez, Veenstra, & Roodbergen, 2016). As exception to this research focus, Bahinipati, Kanda, & Deshmukh (2009) investigated the concept of horizontal collaboration among manufacturers and Caputo & Mininno (1996) discussed horizontal integrations of logistics in the Italian grocery sector. Next to this, Leat & Revoredo-Giha (2013) explored horizontal collaborationship between agri-food producers. Furthermore, research on horizontal collaboration can also be applied to different fields of transportation. Research on naval (e.g. Agarwal & Ergun, 2010; Sheppard & Seidman, 2001), air (e.g. Fan, Vigeant-Langlois, Geissler, Bosler, & Wilmking, 2001; Houghtalen, Ergun, & Sokol, 2011), and rail (e.g. Kuo, Miller-Hooks, Zhang, & Mahmassani, 2008) transportation might lead to collaboration and planning problems. However, this research will exclusively address the concept of horizontal collaboration from the perspective of road carriers. These LSP’s receive transport requests from different customers concerning the transportation of goods to specified location(s) via roads (Cruijssen, Bräysy, Dullaert, Fleuren & Salomon., 2007; Verdonck et al., 2013). By collaborating with fellow LSP’s, significant cost savings, increased service levels and reduction of carbon footprints can be genereated (Saenz et al., 2017).

2.2 Collaborative transportation planning

While the total field of CTM is beyond the scope of this research, the focus will be on one specific subject, namely: collaborative transport planning. CTP has become increasingly important (Lai, Cai, & Hu, 2017) and the literature related to this topic has grown significantly. According to Stadtler (2009), CTP can be defined as “a joint decision making process for aligning plans of individual LSP’s with the aim of achieving in light of information asymmetry” (p.6). On the other hand, Wang, Kopfer and Gendreau (2014) refer to CTP as a joint decision making process between LSP’s in a horizontal collaboration context. Gansterer & Hartl (2018) reviewed the state of knowledge in collaborative transportation and identified three major streams of research: (i) centralized collaborative planning, (ii) decentralized planning without auctions, and (iii) auction-based decentralized planning. Whereas Zhang, Pratap, Huang, & Zhao (2017) identify two major methodologies that describe collaborative transportation, namely centralized- and decentralized logistics collaboration. Within decentralized collaborative planning, the LSP’s are not willing or able to give full information about the transportation orders to the collaborating LSP’s within the network (Gansterer & Hartl, 2018). According to Gansterer & Hartl (2018), a decentralized planning approach is mainly characterized by partner selection, request selection, and request exchange. The literature on decentralized collaboration is divided into research on non-auction based (e.g. Liu et al., 2010; Wang et al., 2014; Hernández & Peeta, 2014) and auction based (Berger & Bierwirth, 2010; Gansterer & Hartl, 2016; Verdonck et al, 2013) decentralized collaboration. Gansterer & Hartl (2018) refer to centralized collaborative planning as collaborative decisions that are made by a central authority that has accessibility to all needed information. An example for such a central authority is investigated in the research of van Dooren (2018), this researcher investigated an online platform that enables the collaborative decision making by matching LSP to sell trips or buy trips from other organizations (e.g. Synple Matchmaker). The translation of this online platform can also be made to a network perspective in which different partner have their own set of transportation orders and are willing to make these orders accessible to the other partners.

(Gansterer & Hartl, 2018). Next to this, planners will also decide on the orders they would like to acquire (Gansterer & Hartl, 2018). Transportation orders that are offered to another carrier could be declined based on different characteristics. The planners can be supported in making their in-and outsourcing decisions by means of route-planning tools and other IT application (Buijs & Wortman, 2014). Within their research, they indicate the interface of transaction processing systems (e.g. transport management system applications), real time systems (e.g. fleet telematics system applications), and decission support systems (e.g. route-planning apllications).

2.3 Joint network behaviour

3. RESEARCH METHODOLOGY 3.1 Research design

The aim of this research is to answer the question: ‘How are the in- and outsourcing decisions of planners influenced in a network of collaborating carriers?” As the literature on the in- and outsourcing decisions is new, a case study research has been chosen as a suitable method for developing a new theory that requires an exploratory design (Voss, Tsikriktsis, & Frohlich, 2002; Siggelkow, 2007; Eisenhardt & Graebner, 2007). An holistic multiple case study with an inductive research approach has been conducted to form a solid base (Yin, 2009) for obtaining in-depth knowledge about the drivers that influence the in- and outsourcing decisions of planners. The unit of analysis in these cases is the in- and outsourcing decision process of planners. This in- and outsourcing decision-making process has been investigated in a network setting of collaborating carriers. In a distribution network, the independent carriers form an interconnected group of storage facilities and transportation systems that deliver the incoming orders to the customers. The planning activities of these orders is executed by the planners at the planning departments of the different carriers, who are (actively) interacting with each other. Within these cases, the research investigated respondents from different hierarchical levels. This resulted in a rich pool of data that allowed the findings to be generalized behind the borders of this study. Within this research, an inductive case study research design was preferred to study the planners in their natural setting (Voss et al., 2002; Meredith, 1998). Furthermore, multiple case study is preferred over a single case study for its ability to warden against observer bias (Karlsson, 2016).

3.2 Case selection

Within this qualitative research, a convenience sampling strategy has been chosen. This implies that the carriers will be chosen based on their ease of access (Maruster & Gijsenberg, 2013). This can be seen as the carriers’ willingness to cooperate and their geographical distance. During the process of data collection, more carriers have been added until the point theoretical saturation had been reached (Strauss & Corbin, 1990). This point was reached after eight cases had been conducted. This is in line with the suggested range of cases (between 4 and 10) of Eisenhardt (1989). Furthermore, in order to build theory from the multiple cases, case selection using replication logic had been used (Karlsson, 2016). This research followed the literal replication logic during the case selection process, predicting that the same results occur at the different cases. The results have been validated by investigating one case from a different network.

3.3 Data collection

This research aims to find how the drivers influence the in- and outsourcing decision in a network setting. The primary source of data came from semi-structured interviews, direct observations, and documents. Investigating different data sources provided triangulation and enhanced the validity and reliability of the case study findings (Karlsson, 2016). Additional questions that arose during the data analysis are sent to the interviewees by email or they have been contacted by phone for explanation.

3.2.1 Semi-structured interviews

The first form of data collection, within this research, are semi-structured interviews. The aim of these face-to-face interviews is to gain in-depth information and insights on the effects that might influence a planners in- and outsourcing decisions. In order to provide a strong foundation for the data collection, a research protocol (Appendix A) have been developed to ensure consistency and secure reliability of the data collection process. This case study protocol contains the procedures and general rules before, during, and after the interviews. Next to this, the study protocol includes the topics that must be covered during the interviews. The topics that have been addressed during the interviews are: i) general information about the carriers, ii) usage of IT, iii) reasoning behind in- and outsourcing decisions, and iv) future perspective on planning department, and v) working hours.

these interviews have been conducted at the carriers’ sites between April and June 2018. An overview of the data collection per case can be found in Appendix B. Within this overview, the participated carriers are referred to in a numerical way to guarantee their anonymity.

The researchers already started interviewing the operations managers and chief executive officers and observing the planners behaviour, before they knew the literature in great detail. The underlying reason is to prevent this research from blinders or confirmation bias (Gioia, Corley, & Hamilton, 2012). Some of the interviews and observations have been conducted by two researchers. The advantages of this is that one researcher can make notes during the interview and guard the time, while the other researcher can lead the interview. This deals with the challenge of observer bias. Due to controversy in the researchers’ agenda’s, one researcher conducted the remaining interviews and observations. The interviews were (after approvement of the interviewees) recorded and transcribed, in order to provide an accurate reproduction of what has been said or discussed. Recording is beneficial and necessary to guarantee the exactness of the interviews (Karlsson, 2016). Next to this, using tape recordings can contribute towards the reduction of observer bias, because the gathered data has not been reduced into a summary of the interviews that could have been misinterpreted.

3.2.2 Direct observation data

3.2.3 Documents

One of the carriers provided the network operational manual to the researcher in order to be used. This document consist of general information on the consistency and history of the focal network. Next to this, the document provides formulated responsibilities of the network partners and the regulations and instruction related to the pick-up and delivery of orders, the transportation orders, the division of the areas, the distribution tariffs, additional duties, etc. This document helped to give insights in the decisions of planners that are based upon the regulations of the network operational manual or other factors, such as the planners individual knowledge and experience.

3.3 Data analysis

The data has been analyzed with the goal of understanding the drivers that influence the in- and outsourcing decisions of planners. A first step into the analysis of the data was by transcribing all the recorded interviews and elaborate on the notes. This elaboration on the notes included the field notes during the direct observations, non recorded phone calls, and the mailings. In order to capture and write down all the important insights and information, the (field) notes were extended directly or the day after the data collection. The transcriptions of the interviews and observations have been collected into a single database in order to ensure the reliability of this research. Furthermore, this single database allowed the researcher to easily access and use the obtained data.

After becoming familiar with each stand alone case, the researcher sought to generalize the emerged findings across cases. Within this second step, the researcher used the analytical approach to pick a specific driver that has been found during the within case analysis and searched within the other cases for similarities and differences. This analysis has been repeated for all the drivers of in- and outsourcing decisions that emerged during the first step. In order to clarify this analysis process, the previous named driver ‘usage of carriers specific BI’ showed similarities with other drivers on the level of a planners’ adoption towards systemized decisions, while other drivers were closely related to the level of network collaboration. This case analysis process has, in total, been repeated multiple times to capture information and insights that might have been overlooked during the first round of analysis.

4. FINDINGS

4.1 Dimensions of planning decisions

Among all carriers, the planners are accountable for fulfilling the planning activities and creating and optimizing vehicle routes. Planners are assigned with the responsibility to decide which of their orders should be offered to (a) collaboration partner(s) and which of them should be carried out by their own fleet (Gansterer & Hartl, 2018). This study aims at finding the drivers that influence the in- and outsourcing decisions made by planners. Based on the conducted semi-structured interviews and direct observations, this research found that the in- and outsourcing decisions are influenced by two major dimensions, namely: i) the level of network collaboration, and ii) the level of adoption of systemized planning activities. Both dimensions are based upon different drivers that are explained below.

First, the drivers that influence the level of network collaboration can move the planners either to depicting more myopic behaviour or joint network behaviour. A planner shows myopic behavior by acting in accordance to what the planner wants without considering that their decisions might affect the benefits of the planner (and the carrier) in the (near) future. This implies that decisions formed by the myopic behaviour of the planners are not in the network’s best interest and might only result in short term benefits. The main concern of a planner that shows myopic behaviour is their ability to finish all the daily tasks against acceptable or low costs. “The planner solely focuses on his tasks. These are the orders that the planner is confronted with. He is just happy to go home and having done all the tasks” (Chief Operations Officer). Opposite to this is the joint network behaviour that is defined as the attitude of the planners towards joint planning activities and continual exchanges between collaborating carriers. These planners might focus more on long term benefits (e.g. transport order reciprocity) for their carrier as well for their network partners.

4.2 Main drivers on the level of network collaboration

As presented above, the level of joint network behaviour can range between myopic behaviour (low level of joint network behaviour) and joint network behaviour (high level of joint network behaviour). This research found five drivers that move the planners either to myopic behaviour or joint network behaviour, namely: i) knowledge on carriers’ specialization, ii) order visibility, iii) usage of IT, iv) synchronized planning activities, and v) usage of KPIs.

4.2.1 Knowledge on carriers’ specialization

Carriers within a network might have their own specific core strengths. These specializations could be focused on the kind of orders that carriers receive or the geographical area that they wish to cover and are familiar with. This research found two factors on which the knowledge on carriers’ specialization is based: i) order specialization, and ii) geographic specialization.

i) Knowledge on order specialization

ii) Knowledge on geographic specialization

Carriers can be specialized on different geographical scales or locations. Carriers can specialize their distribution from a mondial towards a local scale. This research shows that carriers make a postal code division between the collaborating network partners. This makes each network partner a specialist within their specific area. Within these areas the carriers want to have a high drop-density. “We do not have to drive that much if we have a lot of stops within a small area” (head planner, 3). Area division gives clarity to the planner to which network partner an order could be outsourced. By giving less convenient orders to network partners (based on location of the orders), the planners receives orders that are located in its own specialized area. “I would rather be busy in my own area, than in another area” (general director, 4). A next finding is that knowledge on the geographical coverage of network partners leads to outsourcing decisions if the carrier itself lacks the coverage of this area. “Sometimes we receive international orders from our partner via the benelux network” (chief executive officer, 1). Planners outsource orders to a partners’ specialized geographic area and expect reciprocity from their network partners based on their own geographic specialization. This means that more knowledge on a partners geographical specialization might lead to more joint network behavior.

4.2.2 Order visibility

4.2.3 Usage of information technologie (IT)

Planners use computers to aid them with their planning activities in order to store, retrieve and transmit data or information. This research found that IT plays an important role among the carriers in the network. In order manager their data, carriers make use of transport management systems (TMS). Furthermore, order entry can be done by making use of web portals that are linked to the TMS. Some carriers use route-planning software, that is linked to the data of the TMS, to support planners with their vehicle routing activities. Route planning activities within this software can be done manually or automated. Half of the carriers within this research is making use of route-planning software, from which only a small amount has implemented a planning software that allows the planners to create an automated route. Furthermore, it has been found that planners are not using this software to create a fully automated route. “The planners will make a preselection and check what can be outsourced to the network or via another channel” (head planner, 3). Planners filter out the larger shipments and special orders. The orders that are left will be semi-automatically planned into vehicle routings. During those route planning activities, the planners are solely focussed on their individual carriers. Decisions on the in- or outsourcing of orders are not being made jointly between the planners. The findings show that planners do not use route-planning software or other IT systems that supports the planners in making joint planning decisions. However, planners can add restrictions to the route-planning software that result in a suggestioned route that indicates which orders should be outsourced to the network partners or not. “If we have to make a detour of more than 20 kilometer the planning software suggests to outsource it to a specific network partner” (planner & ICT, 5). These alterations to the software are mainly focused on the preferences of individual carriers. Planners check these suggested routes and comply with them, without considering the opportunities for the network partners. More and better usage of IT might lead towards more joint decision making that is in favor of the network. This implies that a higher usage of IT might to more joint network behaviour of the planner and therefore in- and outsourcing decisions that might be more benecial for the network as a whole.

4.2.4 Synchronized planning activities

contradiction in planning activities, we will provide two quotes of partners of the same network: (1) “We receive our national orders before 17:00. The decision on the BNX planning will be made between 16:00 and 18:00” (transport manager, 2), and (2) “50% of our customer orders will be announced between 16:00 and 18:00. So, the decisions will be made between 17:30 and 19:00” (assistant logistics planner, 5). This examples shows that there is only limited time for the planners to communicate with each other. Within this timeframe the planners need to make in- and outsourcing decisions that might concern multiple partners. While this example only relates to two contradicting cases, we have graphically represented the time at which the network partners start their network planning activities and when they tend to be finished (Figure 4.1). There can be seen that only during half an hour a day, all the planners of the different partners are represented to fulfil the in- and outsourcing activities. If a few hours later an opportunity occurs that might be beneficial for one of the network carriers, the lack of synchronization can impede this opportunity. There is a small timeframe in which the planners can interact on their consideration for network orders. The lack of synchronization between planning activities makes the individual planners to focus more on their own planning and how to deal with that. This means that less synchronized planning activities increases the likelihood that planners incur in myopic behaviour.

Figure 4.1: NBX planning activities

4.2.5 Usage of planner oriented key performance indicators (KPIs)

attitude and behaviour has been used as a soft measure for their performance. “You can think about effort, interaction with others, and being active” (transport and distribution manager, 1). The reason that quantitative measurements (e.g. costs of shipment) are not being used is because the planner is reliant on factors that the planner cannot influence, such as: rush-hours, roadblocks, etc. “We cannot judge a planner by its planning and it will never work like that. They are too dependent on the input” (transport and distribution manager, 1). Nevertheless, the findings indicate that planners are personally looking at indicators that measure the performance of their own route. During the planning process, the planners are mainly focussing on: i) maximizing the amount of orders in a single shipments. “A fully loaded truck must be profitable” (head planner, 6). And ii) the amount of kilometers. “We know that we are right, if we have a small area with a lot of stops” (head planner, 3). Planner lack the ability to take the cost of the shipment into consideration, because these cost are only available after creating the vehicle routing. These findings showed that planners are paying little attention to their network partners during the planning activities. This implies that more planner oriented kpi’s might lead to myopic behaviour of the planner, because the planner might focus more on obtaining a high individual performance than making in- and outsourcing decisions that are beneficial for the performance of the network.

4.3 Main drivers on the level of adoption of systemized decisions

4.3.1 Complexity of customer orders

Planners face many different order characteristics during their daily shifts. The complexity of these orders might increase the difficulty of their transport planning activities. This research shows that the complexity of customer orders is based on (i) diversity of the order locations, and (ii) the customer requirements. A higher level of customer order complexity might result in that the planner will make more ad hoc decisions.

i) Similarity of the order locations

in a systematic way, but the heterogeneous order location need more attention and creative thinking of the planner. This means that diversity of the order locations, increases the complexity of the customer orders and therefore might lead to more ad hoc decisions. Whereas it might be more likely that planners outsource complex customer orders (in terms of order locations) to one of their network partners.

ii) Customer requirements

Carriers are providing additional services in order to comply with the customer’s ideas and wishes. This research came across multiple requirements that are set by the customer that make the route-planning activities for planners more difficult. One of those requirements is the request of the customer to know the time at which their order arrives (i.e. Expected Time of Arrival). This ETA is prefered by customers to synchronize their acivities to the delivery. ‘Retailers need to hire warehouse employees to store the goods, therefore they want to know when they can expect the order at their location’ (transport manager, 2). Next, the planner & ICT (5) mentioned multiple requirement: ‘Here [in the planning software] we can see the restriction of the orders, they are based on: cooled transportation, delivery with an automated forklift truck, delivery within an environmental friendly zone, and containing Alternative Dispute Resolution (ADR)”. Another factor that leads to more complexity is the fixed timeframe in which specific orders need to be pick-up or delivered. “If a ontime delivery states that we have to be there between 18:00 and 18:15, than we have a real challenge to be there within that timeframe’ (head planner, 3). The planners need to plan the other orders around this on-time delivery in order to be on-time. Planners already incorporate a time buffer in planning on-time deliveries, because truck driver can experiences disturbances during its route. Furthermore, the planners need to assure their customers that their goods will arrive without damages to the orders, or the environment. In case of highly valuable goods or airfreight, the orders need to be secured during the whole process.

4.3.2 Ability to meet deadlines

Within this research, two factors has been found that might influence the ability to meet deadlines: (i) customer order announcement, and (ii) the presence of side activities. In case of late customer order announcement and/or a high presence of side activities, the planner will not be able to meet the deadlines.

i) Customer order announcement

A carrier can in consultation with their customers come to an agreement about the specific time before the customers should have announced their orders to this carrier. After the carrier received the customers orders, their planners can start with making a route and decide on which of the orders should be outsourced or not. However, these decisions have to be made before the deadline of the network partners. This deadline is based on the fixed time before the planners are obliged to announce the orders that they wish to outsource to the network partners. This is also the moment that the carriers receive reciprocity from their network partners. Althought the mutual deadlines are strict, it has been found that customers are playing fast and loose with their carrier’s deadlines. “We perceive that the customer are announcing their orders later and later’ (assistant logistics manager, 5). This external influence results in a smaller time window between the two deadlines. This leaves the planner with less time to make creative solutions for the optimization of their routes. This might lead to more outsourcing decisions by the planners in order to deal with the orders in a small time frame. A decreased ability to meet the deadlines will leave the planners with higher incentives to make systemized decisions.

ii) Presence of side activities

planner in executing their planning activities. Planners are searching for ways to perform these activities within a small timeframe as fast as possibile. This indicates that a higher pressence of additional activitites might lead to more incentives of the planners to adopt systemized planning activitities. The reason for this is that the planners lag time to think of create solutions for ad hoc decisions.

4.3.3 Allowed input to network

The investigated network is specialized in finely meshed distribution of customer orders. Within this network, planners prefer to outsource small customer orders to the network. “Nowadays, we can not stop with our truck or trailer to deliver a small package. The margins are too low on that” (assistent logistics manager, 5). The planners are adopting more automated planning decision for these small deliveries. “We have a restriction in our planning system that directly suggests on outsourcing decision for small orders” (planner & ICT, 5). This means that small packages are common to the network and will quite easily be outsourced. However, the planners have a fixed amount of loading meters that could be outsourced to the network partners in total. Next to that, each individual partner can not receive more than a fixed amount of loading meters. These are restriction that the planner needs to deal with and find ad hoc solutions if the planner is exceeded this fixed amount. A small allowed input, in terms of loading meters, might lead to more ad hoc decisions, while the planners needs to find other solutions if their prefered orders for outsoucring are exceeding the maximum amount of required input.

4.3.4 Usage of carrier specific business intelligence

heatmaps to check and specify the center of gravity of our shipments” (managing director, 7). Next to internal data, it is found that carriers are trying to understand data that might externally influence their planning activities. “We once did a research to investigate the information on rush-hours, weather, and holidays on our shipments structure” (managing director, 7). This data might help to optimize the parameters in the planning software. “BI can be seen as a tool to improve the planning process” (chief executive officer, 1). Improving the planning process that guides the planners might lead to a higher adoption of systemized decisions, while the planners might acknowledge the complex business analytics that underlie those decisions in the planning software.

5.4 Overview of drivers on planners decisions

This research found multiple drivers that influence the in- and outsourcing decisions of planners. These drivers have been summarized in figure 4.2 ‘Planners behaviour chart’. In this figure the blue arrows represent drivers that internally influence the planner’s behaviour, because these drivers are dependent on individual carriers. Next to this, the red arrows represent external drivers that are dependent on factors outside the individual carriers. The internal and external drivers are moving the behaviour of the planner between myopic and joint network, and their decisions between an ad hoc or systemized way. In order to indicate the position of a planner, a combination of different drivers will place the planner into a specific setting. The position of a planner on the chart can change over time if the influencing drivers change. The workfield in which the planner has to make their in- and outsourcing decisions is dynamic, where multiple drivers are constantly influencing these decisions. These are not standalone drivers, but could interrelate with each other. At first, customers can require that the planner provides an ETA of their order(s) as soon as possible. This is normally done directly after the phase in which the orders is planned onto a specific route. If these orders are outsourced to a network partner, problems might occur when the network partners does not communicate this information correctly. This might occur due to wrong scanning activities of the orders or inconsistent announcement of the ETA. By harmonizing these requirements into the IT systems of all the network partners, this problem might be less likely to occur. Harmonized IT systems might decrease some complexity of the orders, which can lead to more adoption towards systemized decisions.

planning. This means that the joint network behaviour gained by order visibility might be hindered by possible usage of IT.

Third, it has been found that carriers are increasingly using BI to analyze data and make sense out of it. Insights generated from the data analysis might help to set parameters in the route-planning systems. BI could help to improve the planning process by adding data to IT tools. When these improvements are made based on the carrier specific BI, only the individual carrier who implemented these improvements will benefits from the change. This implies that planners can work with improved route-planning tools at their individual carriers. This improvement might lead towards more adoption for systemized decisions making. However, those decisions will be made based on their myopic behaviour because it is solely benificial for the carrier who implements it. The in- and outsourcing decisions might therefore be made without a sense of joint network behaviour.

5. DISCUSSION 5.1 Theoretical implications

This research has focused on the drivers that influence the in- and outsourcing decisions of planners by investigating carriers who are horizontally collaborating in a network setting. Within the field of HLC, previous research has mainly focused on techniques to optimize and solve transport planning problems by means of complex algorithms (De Snoo, 2011; Buijs et al. 2016). It is therefore that, this research answers to the to the call for research to investigate and register the activities that planners perform (Larco, Fransoo, & Wiers, 2018) and discover new behavioral factors that arise in an operations management context by means of a field study (Gino & Pisano, 2008).

Within the research of Wang et al. (2014) and Gansterer & Hartl (2018), they acknowledge that carriers fulfill the activity of deciding on the orders that should be executed in-house, or by a collaborative carrier. This research adds to current literature by thoroughly investigating these in-and outsourcing decisions that are made by planners. By analyzing the conducted interviews and observation, several drivers emerged that might have an influence on one of the two identified dimensions, namely: i) level of network collaboration, and ii) level of adoption of systemized decision-making. To the knowledge of the researcher, this research on the influential drivers of in- and outsourcing decision within a setting of collaborating carriers is the first of its kind. The first dimension ‘level of network collaboration’ indicates that the five drivers push the in- and outsourcing decisions of planners either towards myopic- or joint network behaviour. Next to this, the research found that within the second dimension ‘the level of adoption of systemized decisions’ a total of four drivers move the planners between adoption of systemized decisions and the ad hoc decisions that apply to a specific situation. Furthermore, this research found that these drivers are either forces within the carriers (internal) or forces coming from outside the carriers (external).

orders and make in- and outsourcing decisions. This means that external forces (e.g. customers) could influence the synchronization process and lead to more myopic behaviour of the planner. This research adds to literature by investigating the effect of a desynchronized planning department on the in- and outsourcing decisions of planners.

The following influential driver that will be highlighted is the usage of information technology (IT). This research noticed that carriers within the investigated network are lagging behind in the implementation of automated route planning systems. However, some carriers within this research invested in route planning systems to support its planners with their route planning activities. In line with Buijs & Wortman (2014), it has been found that planners are doing their route planning and decision-making activities in isolation of each other. The planners are using the planning software to create an optimal planning for their own carrier and try to indicate the orders that do not fit well within this route. Based on these inefficient orders, the planner will decide to outsource them to a network partner. This is in contrast to the research of Verdonck et al. (2013), who indicate that carriers share customer orders with all collaborating carriers in a common pool. The main reason for this difference is that current IT lacks the ability to offer a system that can support planners in making a joint route planning by interactively exchanging orders (Buijs & Wortman, 2014). Although, this research showed that the usage of IT tools might lead to more joint network behaviour of the planner. However, the data of other carriers is not visible and available for the planners, which in return hinders to possibility to achieve more joint network behaviour. This research extends current literature by discussing the influence of data visibility and the usage of IT tools on the in- and outsourcing decisions made by planners.

time and without any damages. However, during the interviews and observation, trust has not been found as an explicit driver of in- and outsourcing decisions of planners.

5.2 Managerial implications

For managers, this research has several implications. Managers can benefit from this research by knowing the drivers that influence the in- and outsourcing decision-making process of planners. Insights in these decisions shows the managers which drivers will lead to more joint network behaviour or an increased adopted of systemized decision making. The schematic overview (fig. 4.2) allows them to see all the drivers and there influence on the planners’ decision making process. Next to this, the managers can see if these drivers are forces located within or outside their company. This provides guidance to achieve in- and outsourcing decisions that are based on joint network behaviour and therefore more beneficial for the network as a whole.

5.3 Limitations

5.4 Suggestions for further research

6. CONCLUSION

This research started by indicating that opportunities in horizontal logistics collaboration can be made on the basis of transport planning between collaborating carriers. Furthermore, it stated the important role of planners in making the in- and outsourcing decisions for creating a collaborative transporation planning. Current literature mainly focussed on complex algoritms to solve transport planning problems, but lacked the ability to indicate the daily tasks and behavioural aspects of planners. Therefore, the aim of this research was to identify the drivers that influence the in- and outsourcing decisions of planners in a network setting. In order to achieve this aim an explorative multiple case study has been conducted. Eight carriers participated in this study. In total, nine semi-structured interviews and seven direct observations have been conducted among these carriers. The in-depth information gained through the interviews have been abstracted from different hierarchical levels (i.e. general directors and operations managers). The observations have been applied on planners or head planners. Analyzing the data have led to i) drivers that influence the level of network collaboration, and ii) drivers that influence the level of adoption of systemized planning activities. The results show that the drivers of these two dimensions are influecing the in- and outsourcing decisions of planners in a network of collaborating carriers. Within this research, a distinction has been made between the internal or external influence of the drivers.

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8. APPENDICES 8.1 Appendix A: Research protocol.

The aim of this research protocol is to create a consistent and repeatable research. The next steps will be followed in accordance to this protocol:

Pre-visit:

- Selection of carriers that are eligible for the research based on the case selection criteria. - Sending a pre-established and controlled e-mail to the selected carriers.

- In absence of any indication to the contrary: send a reminder mail after 4 days.

- Getting in contact by phone with the concerning carriers to ask for confirmation for participation in the research.

- Arrange meeting with the participating carriers for a specific date and time in order to conduct the interviews and the observation with the planners and the operations managers.

- Gather the necessary materials that are required for administering the interviews and observations (questionnaire, recording device, pen, paper, and watch).

- One day before the company visit, the passive researcher should contact the contacted person to confirm the meeting.

- Take care that the researcher arrive on time at their location in order to meet the contacted person. On site data collection:

- Attending interviews and observations with 2 researchers. (1) Active researcher: in charge of the interview and asks the (in-depth) questions. (2) Passive researcher: in charge of recording the interview, making notes (also during the observations), and guards the time.

- Tell the participant what they can expect during the interview and ask to what extent they would like to keep anonymous. Also ask for their confirmation to record the interview.

- Do the interview and observations in accordance the to research topics (table 1) and reformulated unclear or imprecise questions.

- Record the interview, take notes, and start the timer. The notes will be linked to the minute in which the interviewee said an interesting quote. This makes it easier to find these quotes back after the interview.

- (Related to direct observations) make field notes during the observations

Post-visit:

- Transcribing the interviews within one week after an interview has been conducted.

- Directly (at max. 1 day later) after the observation, the notes should be worked out as excessive and precisely as possible.

- Send a document of transcript to the interviewee in order for them to confirm their quotes. - Start the process of analysis the results and data of the interviews and observations.

- Contact the interviewee or observee in case of inclarities or missing information during the data analysis.

TABLE 1

Interview and observation topics per case

Topics Description

1 General information about carriers Size of carriers in terms of employees, trucks, daily orders;

Activities outside the network (distribution to other countries);

2 Usage of IT Usage of TMS, WMS, (automated) planning applications,

business intelligence.

3 Reasoning behind in- and outsourcing

decisions

Find out what the interviewee sees as the main in- and outsourcing decisions of orders.

4 Future perspective on planning department Centralized planning; automated planning software.

5 Working hours Amount of shifts; working hours of planners; amount of

planners; start and end time of the planning department.

6 Route planning software (observation) Gaining in-depth knowledge on the used route planning

8.2 Appendix B: Selection criteria Carrier /criterion Availability of Planning department Member of distribution network Exchanging orders Centralized planning software Member of focal network Carrier 1 + + + - + Carrier 2 + + + - + Carrier 3 + + + - + Carrier 4 + + + - + Carrier 5 + + + - + Carrier 6 + + + - + Carrier 7 + + + - + Carrier 8 + + + - (incorrect information) -

8.3 Appendix C: Overview of the data collection per case

Carrier Interviewee(s) Observee(s) Duration of interview

Carrier 1 1. Chief Executive Officer (CEO) & 2. Chief Operations Officer (COO)

Head planner A 50 minutes

Double interview at carrier 1

Transport and distribution manager

- 45 minutes

Carrier 2 Transport manager Transport manager 35 minutes

Carrier 3 Head planner B Head planner B 25 minutes

Carrier 4 General director A / planner

General director / planner

70 minutes Carrier 5 Assistant logistics

manager

Planner / ICT 30 minutes

Carrier 6 1. General director B &

2. Office manager / PO Head planner C 55 minutes

Carrier 7 Managing director (none) 70 minutes

Carrier 8 Transport coordinator 1. Transport

coordinator & 2. region planner

75 minutes