AN EXPLORATORY INVESTIGATION TO THE EFFECTIVE USE OF PENALTIES IN BUYER-SUPPLIER RELATIONSHIPS
Master thesis, MSc. Supply Chain Management University of Groningen, Faculty of Economics and Business
August 7th, 2013 By Tjeerd Spierdijk Student number 1855328 Email: t.spierdijk@student.rug.nl Supervisor/University dr. N.D. van Foreest Co-assessor/University prof. dr. D.P. van Donk
Supervisor/ field of study
G.de Jager Supply Chain Manager In-Store Logistics 2th version (repair)
2 ABSTRACT
The use of penalties in buyer-supplier relationships is common in practice, however mainstream literature offers a few empirical findings on the beneficial effects of penalties. This paper aims at generating a number of propositions about the conditions under which the employment of penalties by a buyer could be effective in improving the suppliers’ delivery performance. For our case study research we collected data at a global operating wholesaler and four of its suppliers. We identified five moderating variables, which influence the relation between the employment of penalties by a buyer and supplier delivery performance outcomes. Based on our results, we present 7 propositions of conditions under which penalties by a buyer are effective for achieving supplier delivery performance improvements. These propositions should be worth pursuing further in future research.
3 MANAGEMENT SUMMARY (DUTCH)
Bedrijven worden steeds afhankelijker van hun externe partners sinds de toenemende belangen voor aaneensluitende goederenstromen binnen de supply chain. Helaas kampen bedrijven nog altijd met verstoringen binnen deze goederenstromen. In het verleden hielden bedrijven voorraden aan tegen slechte uitlever prestaties van leveranciers. De slechtere economische omstandigheden betekenen echter dat bedrijven steeds bewuster worden van de kosten van bedrijfsvoering. Dit bewustzijn maakt het aanhouden van voorraden steeds minder gewenst door toenemende kosten voor het houden van voorraad en veroudering van goederen. Tegenwoordig zijn strengere maatregelen, zoals het gebruik van financiële boetes alomtegenwoordig in de praktijk.
De heersende stroming in de wetenschappelijke literatuur dekt voornamelijk de negatieve effecten van boetes op een leverancier zijn vertrouwen en tevredenheid. Er is echter nog weinig empirisch onderzoek gedaan naar de positieve effecten van boetes. Deze gelimiteerde literatuur maakt het voor praktijkmensen onduidelijk onder welke voorwaarden boetes effectief werken om betere leveringen bij leveranciers tot stand te brengen. Dit gat in de literatuur proberen wij te overbruggen door een aantal empirische beweringen aan te leveren die de moeite waard zijn om verder na te streven in vervolg onderzoek.
Onze studie is uitgevoerd bij een wereldwijd opererende groothandelaar die actief is in de verkoop van levensmiddelen en luxegoederen. Dit bedrijf telt ongeveer 50.000 leverbare verkoopeenheden afkomstig van ongeveer 2.000 verschillende leveranciers. De vooraf gespecificeerde inkoper-leverancier verstandhouding heeft ertoe geleid dat vier onderliggende leveranciers van deze groothandelaar zijn meegenomen in dit onderzoek.
De resultaten laten de aanwezigheid van verschillende variabelen zien. Hier blijken het moment waarop de boetes worden opgelegd, de intensiteit van de boetes, de waargenomen procedurele rechtvaardigheid van de boetes door leveranciers, het verschaffen van leveranciers ondersteuning en afhankelijkheid gebaseerde macht van belang. Deze variabelen hebben we weergegeven in een diagram om hun onderlinge causale ordening te verduidelijken. Op basis van deze identificatie en onze interpretatie van de resultaten presenteren we de volgende voorstellen waaronder boetes effectief kunnen zijn in het behalen van verbeteringen in de uitlever prestaties van leveranciers:
4 Voorstel 2. De intensiteit van de boetes moeten van gemiddelde hoeveelheid zijn en in verhouding staan tot de netto inkoop waarde van de orders die niet worden geleverd zoals afgesproken.
Voorstel 3. Boetes moeten eerlijk en rechtvaardig door leveranciers worden waargenomen. Voorstel 4. De inkopende organisatie moet de leverancier ondersteunen door transparantie
te verschaffen in de leveranciers uitlever performance.
Voorstel 5. De inkopende organisatie moet nauwkeurige (promotionele) forecasts delen met haar leveranciers.
Voorstel 6. De inkopende organisatie moet leveranciers uitnodigen voor een gezamenlijke discussie in geval van (ernstige) wanprestatie van de levering.
Voorstel 7. De inkopende organisatie moet met zijn leverancier overeenstemming hebben bereikt over de oplegging van boetes door het tekenen van een contract die een boeteclausule bevat.
5 TABLE OF CONTENT
ABSTRACT ...2
MANAGEMENT SUMMARY (DUTCH) ...3
TABLE OF CONTENT...5
GLOSSARY OF TECHNICAL TERMS ...6
1. INTRODUCTION ...7
2. THEORETICAL BACKGROUND ...9
2.1 Supplier Delivery Performance ...9
2.2 Historical Perspectives on Penalties...9
2.3 Moderating variables ... 10
2.4 Conceptual model ... 12
3. METHODOLOGY ... 14
3.1 Research design ... 14
3.2 Case selection ... 14
3.3 Data Collection & Measurements ... 15
3.4 Data Organization and Analysis ... 16
4. RESULTS ... 17
4.1 Penalizing Practices of Corporation A ... 17
4.2 Identified Moderating Variables at Suppliers ... 18
4.3 Causal Diagram ... 22
5. DISCUSSION & CONCLUSION ... 23
5.1 Discussion ... 23
5.2 Conclusion ... 26
6. REFERENCES ... 28
APPENDIX A- MEASUREMENTS ... 31
APPENDIX B- INTERVIEW PROTOCOL ... 33
6 GLOSSARY OF TECHNICAL TERMS
Delivery Performance Performance with respect to promises made for on-time-in-full deliveries
Dependence Based Power The level of dependence of the supplier or buyer of one another Dependent Variable The presumed effect/outcome of our study i.e. supplier delivery
performance improvements
Fair Penalty The recipient’s believe about the honesty of the buyer’s decision-making procedures
High Penalty Intensity Penalty (amount) that creates supplier anxiety
Independent Variable The presumed cause of our study i.e. a buyer penalizing its suppliers
Just Penalty The recipient’s belief that they deserve the penalty Low Penalty Intensity Penalty (amount) that does not provide enough stimuli Moderate Penalty Intensity Penalty (amount) that balances between enough stimuli and
preventing supplier anxiety
Moderating Variables Variables that influences the relation between the independent and the dependent variable
On-time Delivery The extent to which orders are delivered on-time
Order Accuracy The extent to which orders contain the right amount of products Penalty Financial penalties imposed by the buyer to force its supplier to
deliver reliable
Perceived Procedural Justice The evaluative judgment about the rightness of a penalty by recipients (i.e. suppliers)
Punitive Capabilities The ability and willingness to impose negative consequences (such as penalties) on suppliers
Service Level Agreement Contract in which a delivery service is defined by a buyer and supplier
7 1. INTRODUCTION
Wholesalers find their strengths in serving their customers with a wide variety of products, which sometimes reaches to 50.000 SKU’s. The buying behaviour of wholesalers’ customers can be characterized by infrequent visits with high sales per visit1. During these limited visits, high on-shelf availability is of utmost importance. However, the provision of good availability is a challenging job for such a variety of products (Kaipia & Tanskanen, 2003; Thonemann & Bradley, 2002). Besides, wholesalers focus more and more on stock minimization due to the associated holding costs and obsolescence of SKU’s over time, especially for fast-moving consumer goods. A long time ago, buying companies would have used buffers (e.g. inventories) against poor supplier delivery performance (Vachon & Klassen, 2002). However, the pursuit for a minimized stock made buffers undesirable. That is why buying companies became increasingly demanding of their suppliers and why the supplier’s delivery performance has become a critical indicator of success (Vachon & Klassen, 2002). Unfortunately, wholesalers face poor supplier delivery performance, which causes out-of-stock situations (Corsten & Gruen, 2003) and hence increases their lost sales due to consumer responses towards out-of-stocks (Dadzie & Winston, 2007; van Woensel, van Donselaar, Broekmeulen & Fransoo, 2007; Fernie & Grant, 2008; Trautrims, Grant, Fernie, & Harrison, 2009).
The illustrative situation described above outlines that buying companies are more and more reliant upon the performance of their external partners. Hennet & Arda (2008) describe both the buyer and supplier as players in a game with a common goal, but with conflicting objectives. That is to say that the common goal of the supply chain is the alignment of supply and demand at every value-added stage to maximize the overall value. Unfortunately, each partner pursues an individual objective in which they try to optimize their own (supply) policies to satisfy their own gain (Hennet & Arda, 2008). Therefore, formal contracts between the buyer and its suppliers exist to coordinate desired delivery performance outcomes. However, good supply chain performance is not guaranteed anymore if suppliers do not meet these agreements (Hennet & Arda, 2008). Therefore, buying firms often include financial penalties in its contracts to ensure reliable supplies of suppliers (Corsten, Kumar, & Kucza, 2009; Grout, 1997). Grout (1997) and Kumar, Scheer & Steenkamp (1998) mention that the majority of penalties in purchasing contracts are focused on non-performance of delivery.
1
8 Therefore, these penalties are imposed to force suppliers to deliver reliable. At first sight, these financial penalties do not generate benefits among suppliers (Kumar, Scheer & Steenkamp, 1995), because mainstream literature deals with the damaging effects of penalties on a supplier’s trust and satisfaction (Geyskens, Steenkamp, & Kumar, 1999; Kumar et al., 1995; Frazier & Rody, 1991; Gaski, 1984). On the other hand, from Corsten et al. (2009) we can justify that the beneficial effects behind penalties on a supplier’s performance are (empirically) barely proven.
Corsten et al. (2009) conducted the first empirical research on the beneficial effects of penalties on the supplier performance in the automotive sector. These authors point out the importance of having a good buyer reputation and exchanging enough information besides the use of penalties. Till now, we presume that it is unclear for scientists and practitioners under which (additional) conditions the use of penalties is beneficial.
The purpose of this research is to generate a number of propositions about the conditions under which the use of penalties could be effective in stimulating higher supplier delivery performance. These propositions should be worth pursuing further in future research. We apply a single embedded case study research design to obtain great dept in data. Specifically, data is collected at a global operating wholesaler and four of its suppliers. A case study is appropriate, because of the lack of detailed preliminary research and its ability to create new insights (Karlsson, 2009). The research question of this study is:
RQ. Under which conditions may penalties by a buyer be effective to accomplish supplier delivery performance improvements?
9 2. THEORETICAL BACKGROUND
In this chapter the current literature is explored to offer a better know-how on penalties. This literature review enables us to create a conceptual model. This model set the limits of our research by presenting the selection of the variables under study and their presumed relationships.
2.1 Supplier Delivery Performance
As discussed before, this research focuses at the achievement of supplier delivery performance improvements by the use of penalties by a buyer. Slack & Lewis (2002) define delivery performance as the fulfilment of delivery promises made. However, these delivery promises can be decomposed in many performance indicators such as order condition, personnel contact quality, flexibility, order quality, timeliness etc. (Mentzer, Flint, & Hult, 2001). Later, other indicators joint such as order information, best-before-dates and tracking & tracing requirements due to stricter safety regulations (Van Donk, 2001). According to a large scale benchmark of Korpela & Tuominen (1996) delivery reliability seems the most important logistical indicator. We define supplier delivery performance, based on the paper of Korpela & Tuominen (1996), as on-time-in-full deliveries. Specifically, on-time delivery represents the extent to which orders are delivered on-time and in-full delivery (i.e. order accuracy) relates to the extent to which orders contain the right amount of products (Mentzer et al., 2001).
2.2 Historical Perspectives on Penalties
As derived from Corsten et al. (2009); Grout (1997) and Gupta & Weerawat (2006) we define penalties in this paper as financial penalties imposed by the buyer to force its suppliers to deliver reliable (i.e. on-time-in-full).
10 focus of these authors was on the damaging effects of penalties on a supplier’s trust and satisfaction. Although the reporting of these damaging effects, Grout (1997) writes that nearly 50% of the purchasing contracts in practice still include financial penalties for late deliveries of suppliers.
For some time the research to penalties gained less interest. However, in 2009 Corsten et al. continued the first –valid- research about the beneficial effects of penalties in inter-organizational relationships in the automotive sector. These authors show a positive supplier operational performance (i.e. reduced disturbances caused by the supplier) in combination with the use of penalties. The effectiveness of these penalties are due to the conditions of a having a good reputation as a buyer and the exchange of enough information towards the supplier. These authors suggest for future research to explore additional conditions under which penalties are effective. In the following paragraph we describe a number of variables selected for our research which will support us in the exploration for these additional conditions.
2.3 Moderating variables
In this section we describe five moderating variables discovered in literature, which will guide our study. These moderating variables are expected to produce an interaction effect between the employment of penalties and supplier delivery performance outcomes.
11 2.3.2 Intensity of the penalty. Grout (1997) state that penalty intensity levels must be proportional to the associated delivery failure. This means that the penalty intensity should relate to the number of units not delivered, as well as the time that a delivery is late. By doing so, penalties are proportional to the severity of its underlying delivery failure. Arvey & Ivancevich (1980) discuss that penalties can exist of different amounts. However, Arvey & Ivancevich (1980) highlight that it is hard to define low, moderate and high intensity levels of penalties. These authors discuss, based on intra-organizational and psychological literature, that relative low level amount penalties will not provide enough stimuli to remove undesirable behaviour. On the other hand, penalties of relative high level amounts will create supplier anxiety, which in turn can inhibit the learning process. Moderate amounts are expected to be more effective, because these penalties balance between enough stimuli to perform better and preventing supplier anxiety (Arvey & Ivancevich, 1980). Therefore, we expect that penalties are effective for achieving supplier delivery performance improvements when the penalt y intensity exists of moderate financial amounts which are proportional to the associated delivery failure.
12 2.3.4 Supplier support. The study of Corsten et al. (2009) concludes that the effects of penalties are beneficial in combination with the provision of supplier support. The buying company can support its suppliers by the provision of rationale i.e. providing insights in the poor delivery performance and reasons why the penalty occurred (Arvery & Ivancevich, 1980). ‘The information exchange creates convergent objectives and suspicions are likely to be replaced by mutual understanding’ (Corsten et al., 2009:10). Besides the exchange of delivery performance information, forecast information need to be provided by the buyer as well. Corsten & Gruen (2003) conclude that suppliers receive not enough information from the buyer about actual customer demand. By providing this forecast information suppliers can anticipate on expected demand, which in turns enables them to achieve the requested delivery performance. From Frohlich &Westbrook (2001) we learn that the most successful companies are those companies who are able to carefully link their internal processes to their external suppliers. The buyer can provide this link by providing continuous support to its supplier. Therefore, we expect that penalties are effective for achieving supplier delivery performance improvements when at the same time the supplier is supported by its buyer.
2.3.5 Power. Kumar et al. (1998) state that power plays a central role in the employment of penalties. These authors discuss power as a determinant for whether or not to impose penalties. These authors state that buying firms need punitive capabilities (i.e. the ability and willingness) to impose negative consequences such as penalties on their suppliers. Here, Kumar et al. (1998) discuss the dependence based power as relevant, i.e. the level of dependence of the supplier or buyer of one another. From the suppliers perspective dependent suppliers are more likely to meet the penalties imposed by the buyer (Keith, Jackson & Crosby, 1990) and independent suppliers on the other hand simply do not accept the penalties, because they have less need to maintain the poor relationship (Payan & McFarland, 2005). From the buyer’s perspective the less dependent the buyer is, the little it has to lose and therefore has little fear and few restraints on its penalizing actions (Kumer et al., 1998). Therefore, we expect that penalties are more effective for achieving supplier delivery performance improvements when the buying company has more dependent based power than its penalized supplier.
2.4 Conceptual model
13 variables. Moderating variables influence the relation between the independent variable (i.e. the cause) and the dependent variable (i.e. the effect/outcome).These moderating variables produce an interaction effect between the penalties and the expected outcome. We expect that the presented moderating variables will positively influence the effectiveness of the use of penalties by a buyer to achieve supplier delivery performance improvements. Most variables are derived from intra-organizational theories and non-empirical research. Therefore, we will retrace these moderating variables in this paper in order to asses them as relevant in an empirical inter-organizational context. This will enable us to provide an answer on our research question; ‘under which conditions may penalties by a buyer be effective to accomplish supplier delivery performance improvements?’ Based on the assessment of these moderating variables we are able to create a number of propositions about the conditions under which penalties by a buyer may be effective to accomplish supplier delivery performance improvements.
FIGURE 1.
Conceptual Model Timing of the Penalty Intensity of the Penalty
14 3. METHODOLOGY
This section presents the methods for conducting our empirical research. That is to say that this section describes the research design, the case selection and the methods used for data collection in order to retrace the moderating variables presented in our conceptual model. Moreover, the methods for analysis are described to assess the moderators as relevant in our inter-organizational context.
3.1 Research design
This paper has an explorative research purpose, which is appropriate to uncover new areas for research (Handfield and Melnyk, 1998). Specifically, we pursue to generate a number of propositions of conditions under which penalties by a buyer are effective in stimulating supplier delivery performance improvements. These propositions should be worth pursuing further in future research. A case study methodology seems to be appropriate for an explorative research purpose, because of the ability of a case study to create new insights and the lack of detailed preliminary research (Karlsson, 2009; Handfield and Melnyk, 1998; Yin, 1994). The buyer-supplier relationship is chosen as the unit of observation. We selected this unit, because the outcomes of penalties depend upon the deployment by the punisher (Corsten et al., 2009) and the way how the recipients (i.e. suppliers) make sense of the disciplinary event (Sims & Gioia, 1986).Therefore, we pre-specify both the buyer and supplier as the units of analysis. We chose to do a single embedded case design to obtain great depth in data. According to Scholz & Tietje (2002) embedded cases are relevant in a situation where the boundaries between the phenomenon under study and its contexts are not clearly evident. In our research the interaction between the penalties under study and the way the supplier makes sense of it neither is clear.
3.2 Case selection
15 Corporation A by telephone, unfortunately two cancelled at the last moment. The final cases are presented in table I. The suppliers are selected on the criteria of volume, because here we expected that the informants were possibly more active on future innovative solutions for supply chain efficiency. Therefore, they could reveal interesting insights. Furthermore, it is more profitable and efficient to achieve small improvements in the supplier delivery performance of big suppliers rather than putting much effort in many small suppliers by obtaining small increases. The suppliers finally selected are presented in table I below. The cases were selected for theoretical replication, because we expect contrary results due to the different levels of power (as stated in section 2.3.5). From Keith et al. (1990) we can describe supplier power dependent on whether the buyer has a stake in the relationship e.g. a small proportion of sales that arise from the relationship. Therefore, during the case selection we looked at the purchasing volume and sales flowing out the relationship. Besides, we looked whether or not the suppliers has signed for a penalty clause, because we thought this would indicate the different levels of power as well.
TABLE I. Supplier Business
activities
Informants Power Purchase value of
orders (2012) A Supplier of
nutrition, health and wellness products
Account- & Logistic Manager High € 2.978.500 B Supplier of home designs for creative plastics Account- & Operations Manager Low € 524.880 C Supplier of grain-based snack foods and beverages
Account Manger & Supply Chain Director
High € 2.237.276
D Supplier of cookware sets, kitchen tools and knives
Account - & Logistic Back Office Manager
Low € 507.382
Embedded Case Selection. 3.3 Data Collection & Measurements
16 interviewing persons of different departments. At Corporation A we conducted a semi-structured interview (Appendix A) with the supply chain director and supply chain specialist. Furthermore, the case study allowed multiple informal talks with purchasers and the supply chain coordinator. For the semi-structured interviews at suppliers, we ensured the presence of a supply chain (or logistics/operations) manager as well as an account manager. We did this because of the responsibility of the account manager for maintaining the contact with Corporation A and the responsibility of the supply chain- or other sort of logistic-related manager concerning delivery performance issues. By doing so, we ensured that the informants had enough competencies to answer the questionnaire. The length of the interviews were on average 1,5 hour. We had a site visit at suppliers for conducting the interviews. The semi-structured interviews function as the main measurement for the moderating variables in our conceptual model. A pilot test with two supply chain specialists of Corporation A is executed to check the comprehensibility of the questionnaire. The semi-structured interview meant that we mainly asked questions to start in-depth open-ended dialogues (see Appendix A). Archival data of Corporation A is requested to function as an additional measurement for the constructs of our conceptual model to enhance the internal validity of our findings. We analyzed formal penalty procedures, complaint letters of suppliers and penalty related data as recorded in Corporations A’s information system. For specific details about the measurements we refer to Appendix A.
3.4 Data Organization and Analysis
17 variables, including its sub-variables. Hereafter, we executed a cross-case analysis to detect within group similarities. The result is a causal network of all variables showing their linkages. After the analysis, we were able to report the findings. Validation and objectivity of the findings are obtained by peer debriefing and short-loop feedback sessions of supervisors and team members, by providing multiple presentations at the university and focal company. After the verification of findings, we were able to draw and report the conclusions.
4. RESULTS
In this section we present the findings of our research. We start with a detailed description of Corporation A’s penalties. Hereafter, we present the 5 identified moderating variables including several sub-variables from the supplier analysis. We summarize our results into a diagram showing the causal relationships among these variables.
4.1 Penalizing Practices of Corporation A
18 past couple of years. The numbers show that only 25% of all penalized suppliers actually pa y a penalty. The other 75% is excluded due to supplier complaints about the unreliability of performance data, the fact that they do not agreed with a penalty clause or due to the bad forecasts provided by Corporation A (see figure 2).
FIGURE 2
Number of Penalties Paid and Unpaid (Including Reasons for Penalty Exclusion)
The purchasers of Corporation A point out that in most cases a penalty clause is agreed upon in the service level agreement. Furthermore, they state that the presence of power is in most cases the turning point for whether or not to agree upon a penalty. However, it could happen that the best negotiator may win, which could result in the agreement upon penalties with a high power supplier.
4.2 Identified Moderating Variables at Suppliers
Table II, as presented further on, includes the moderating variables identified (left column) during our data analysis. The sub-variables function as properties of the moderating variables for which we listed per case the informants’ appreciation. Based on these appreciations we provide a short description of each moderating variable and its underlying sub-variables. We present the frequency of codes within each moderating variable (most right column) to indicate their potential importance.
4.2.1 Timing of the penalty. As can be seen in table II, all suppliers indicate the lateness of the moment when the penalties are imposed and the infrequency of the executed performance checks. The supplier informants explain that the current timing of the penalties imposed by Corporation A causes that they spent much time on detecting the initial root-causes of their
25% 45% 20% 10% 75% Penalties paid Unreliable performance data
19 bad delivery performance, because they find it hard to recall detailed information (e.g. freight letters) over such a long period.
4.2.2 Intensity of the penalty. The high power suppliers A & C state that the current penalties do not motivate them to perform better. Two arguments are provided. First, the penalties exist of relative low amounts and second they do not pay the penalties anyhow. The low power supplier B considers the current penalty intensity as high. Their informants think that the penalties are a serious amount of money. On the other hand, low power supplier D thinks that the current intensity is acceptable in relation to the severity of the underlying delivery failure.
4.2.3 Perceived procedural justice of the penalty. All suppliers perceive the penalties of Corporation A as unfair. In two cases of supplier A & C the penalty is never agreed upon i.e. the suppliers never signed for a contract including a penalty clause, though they constantly receive penalty letters. At second, all four suppliers state that the penalties are perceived as unfair, because of the inaccurate performance data in which the penalties rely on. Figure 2 supports these arguments by showing that 45% of the penalties are not paid, because of complaints about these performance data inaccuracies. In these situations, the suppliers compare their own measured performance with the one measured by Corporation A. By doing so, the suppliers are investing much time in resolving the misalignment of the actual performance rather than resolving their own delivery performance.
20 Moreover, the suppliers prefer a joint discussion with Corporation A to discuss together about how to get the performance to a higher level. The informants of supplier A state that a joint discussion can contribute to the understanding and alignment of each other’s processes. Unfortunately, the supplier informants mention that this discussion seems useless if both companies do not agree about the actual performance, because that is not where the discussion should be about.
21 TABLE II.
Summary of Within-Case Analysis Results Moderating Variables
Sub-variables
Corporation A
Supplier A Supplier B Supplier C Supplier D Frequency of codes
Timing of the penalty
11
1.Moment of penalizing 2,5 Month Late Late Late Late
2.Schedule of penalizing Quarterly Infrequent Infrequent Infrequent Infrequent
Intensity of the penalty
12
3.Proportion of penalty intensities Variable, but fixed
minimums
Low High Low Acceptable
Perceived procedural justice of the penalty
45
4.Agreement on the penalty Depends on
power & negotiation
Not agreed Agreed Not agreed Agreed
5.Accuracy of Performance Data Face many problems
Inaccurate Inaccurate Inaccurate Inaccurate
Supplier support
40
6.Need for performance information exchange Corporations’ Website
Preferably Definitely Preferably Definitely 7.Need for (promotional) forecast information
exchange
Shared 4 weeks in advance
Definitely Definitely Definitely Definitely
8.(Joint) Discussion In severe
cases
Preferably Preferably Preferably Preferably
Power division
9. Need to maintain the relationship Depends upon the relationship
22 4.3 Causal Diagram
The results of our cross-case analysis are shown in figure 3 below. This diagram displays the five most important variables, its sub-variables and summarizes the relationships among them, as described in the sections before.
FIGURE 3. Timing of penalty Intensity of penalty Perceived Procedural Justice by Suppliers Supplier Support Power division Moment Schedule Agreement upon the penalty Accuracy of Performance Data Level of Proportionality Performance information (Promotional) forecast information Dependence based power Information exchange Discussion
Pe
n
a
lt
y
e
ff
ec
ti
v
en
ess
Causal Diagram of Identified Variables from the Cross-Case Analysis Moderating Variables Sub-variables
23 5. DISCUSSION & CONCLUSION
In this chapter we interpret and discuss our results by finding explanations in previous research. Furthermore, we highlight our research limitations, which are complemented by our suggestions for future research. At least, we provide an answer to our main research question and emphasize the practical implications.
5.1 Discussion
Based on mainstream (non-inter-organizational) literature we explored the relevance of five moderating variables related to the timing-, the intensity-, the perceived procedural justice of the penalties by suppliers, the provision of supplier support by the buyer and the division of power between the buyer and supplier. In figure 3 we present a causal diagram, showing all sub-variables identified, which should clarify the five main moderating variables and the potential link with the effectiveness of penalties. Below, we provide a discussion to interpret the results by using previous literature.
The results in comparison to our expectation of the timing of the penalty show a partial match with the suggestions of Arvey & Ivancevich (1980). We found that if the moment at which the penalty is imposed is relatively late, then suppliers find it hard to recall important information such as specific freight letters, which in turn made it difficult to detect the initial root-cause. Here, we see similarities with Arvey & Ivancevich (1980) which propose that penalties should be imposed immediately after a delivery failure. Based on Arvey & Ivanchevich (1980) we presume that a late timing of the penalty imposition inhibits the learning process of suppliers and hence the expected improvements. We did not find proves for the application of a continuous schedule. In contrast, our case maintains a fixed interval schedule to check the suppliers’ performance. By averaging the performance over a fixed period the buying company perhaps allow some deviation by distinguishing incidents from structural delivery events.
24 relative high. However in the second case this intensity is 0,2%, which might be classified as relative low. The results show the responses of criticism of suppliers towards the penalties as well, which are received by the two relative big suppliers as low and on the other hand by one relative small supplier as high. Arvey & Ivanchevich (1980) explain that low amount penalties do not provide enough stimuli and that high penalties raise supplier anxiety.
Our findings suggest the relevance of the perceived procedural justice of penalties by suppliers. The results correspond with the statement of Sims & Gioia (1986) that the outcomes of penalties depend upon how the suppliers make sense of the disciplinary events. Our findings confirm the gap as defined by Giannakis (2007) that arises between the supplier’s perception of its own performance and the buyer’s perception of the supplier’s performance. In our case, this gap mainly arises due to the inaccuracy of performance data of Corporation A. As a consequence the suppliers perceive the penalties as unfair. On average 45% of all penalty payments are eliminated (figure 2) of which we presume that no improvements will occur through the penalties, because the stimuli of ‘the payment’ is removed. A quite logical insight is that the powered suppliers perceive the penalties as unfair, because they never agreed upon or signed for a penalty clause in the service level agreement. Though, they do receive penalties from the buyer. Therefore, buyers should agree the consequences of a delivery failure with its suppliers at the beginning of the relationship, otherwise suppliers perceive the buyer’s decision-making procedures as unfair and as a result the penalties can become ineffective (Ball et al., 1994).
25 However, our findings suggest before starting such a discussion both parties need to agree upon the actual performance otherwise the buyer and supplier would have an incorrect discussion.
Our final result about the power division supports our initial expectations. The number of suppliers which do not agree with penalties in the service level agreement seems to count for 20% (figure 2). We expect higher supplier delivery performance when a contract, including a penalty clause, is signed by a supplier, because Eisenhardt (1989) discusses the agreement of a penalty as the guarantee of a supplier that he has the skill and the capacity to deliver the service as agreed upon. Our results show that the high power suppliers do not accept the penalties. This corresponds to the paper of Payan & McFarland (2005), because these suppliers have less need than the buying company to maintain a poor relationship. For the low power suppliers the relationship does generate a significant proportion of sales and therefore they are inclined to accept the penalty (Keith et al., 1990). An interesting result is that Corporation A is not always dependent upon its power for imposing penalties, because a good negotiation can enable them to agree with the penalty clause with high power suppliers. Kumar et al. (1998) explains that the human element can defeat being imprisoned in its underlying interdependency.
While our results correspond to previous research from different psychological and intra-organizational fields, they are new and interesting in the context of buyer-supplier relationships. However, the major limitation of this study is that the results are derived from a single case. We opt for an explorative research purpose including multiple embedded cases to obtain greater depth in data. Yet, this choice went at the expense of a multiple case study design. There is a high potential available of misjudging the representativeness of the single events underlying this research. As a consequence, this may limit the generalizabilty of our findings. Although we explore and clarify five moderating variables, we expect that more variables can be investigated in a multiple case study research design. A complete list including all relevant variables can only be presented after the completion of multiple studies in different contexts. In the newspaper, frequently articles appear about the rigorous mechanisms wholesalers and retailers apply on their suppliers e.g. the penalties of retailer Lidl2. Corsten et al. (2009) point out many other retailers, all of which are known for their use of penalties such as Carrefour, Tesco and WalMart. That is why we definitely recommend repeating this study several times, including multiple cases, before future (survey) research
2
26 should test the final propositions on significant results in relation to the suppliers’ delivery performance. Furthermore, future research in mathematical oriented fields can determine the optimal ‘moderate’ penalty scheme by using a principal-agent model (Fayezi, O’Loughlin & Zutshi, 2012).
5.2 Conclusion
In the central question addressed in this paper we question under which conditions the use of penalties by a buyer may be effective to accomplish supplier delivery performance improvements. Our findings suggest that five moderating variables can play a role in the link between the deployment of penalties by a buyer and the accomplishment of supplier delivery performance improvements. The single case study research design means that we are careful with judging the generalizability of the identified moderating variables and its sub-variables, because of the misinterpretation of single events. Based on our results, we suppose that the timing-, the intensity- and the perceived procedural justice of the penalties, as well as the provision of supplier support and the division of power between the buyer and supplier are relevant moderators concerning the effectiveness of penalties. Here, based on our research we offer the following proposition of conditions under which we think that penalties by a buyer are effective in achieving higher supplier delivery performance:
Proposition 1: The penalties should be imposed immediately after a delivery failure of a supplier within a fixed interval schedule.
Proposition 2: The intensity of penalties should exist of moderate amounts and should be proportional to the net purchase value of the orders which are not delivered as agreed upon.
Proposition 3: Penalties should be perceived as just and fair by suppliers.
Proposition 4: The buyer should provide transparency into the supplier’s delivery performance.
Proposition 5: The buyer should share accurate (promotional) forecast information with its suppliers.
Proposition 6: The buyer should invite its suppliers for a joint discussion in case of a (severe) non-performance of delivery.
28 6. REFERENCES
Arvey, R. D., & Ivancevich, J. M. (1980). Punishment in Organizations: A Review, Propositions and Research Suggestions. Academy of Management Review, 5 (1), 123-132. Ball, G. A., Trevino, L. K., & Sims, H. P. (1994). Just and unjust punishment: Influences on subordinate performance and citizenship. Academy of Management Journal, 37(2), 299-322.
Church, R. M. (1963). The varied effects of punishment on behavior. Psychological Review, 70(5), 369.
Corsten, D.S., & Gruen, T. W. (2003). Desperately seeking shelf availability: an examination of the extent, the causes, and the efforts to address retail out-of-stocks. International
Journal of Retail & Distribution Management, 31(12), 605-17.
Corsten, D. S., Kumar, N., & Kucza, G. (2009). Mitigating the deleterious effects of punishments in buyer-supplier relationship, working paper, IE Business School, University of Pompeu Fabra.
Dadzie, K. Q., & Winston, E. (2007). Consumer response to stock-out in the online supply chain. International Journal of Physical Distribution & Logistics Management, 37(1), 19-42.
Eisenhardt, K. M. (1989). Agency theory: an assessment and review. Academy of
Management Review, 14 (1), 57-74.
Fayezi, S., O’Loughlin, A. & Zutshi, A. (2012). Agency theory and supply chain management: a structured literature review. Supply Chain Management: An International
Journal, 17(5), 556 – 570.
Fernie, J., & Grant, D. B. (2008). On-shelf availability: the case of a UK grocery retailer. The
International Journal of Logistics Management, 19(3), 293-308.
Frazier, G. L., & Rody, R. C. (1991). The use of influence strategies in interfirm relationships in industrial product channels. The Journal of Marketing, 55(1), 52-69.
Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: an international study of supply chain strategies. Journal of Operation Management, 19(2), 185-200.
Furby, L. (1986). Justice: Views from the social sciences. New York: Plenum, 153-203 Gaski, J. F. (1984). The Theory of Power and Conflict in Channels of Distribution. Journal of
Marketing; 48(3), 9-29.
29 Giannakis, M. (2007). Performance measurement of supplier relationships. Supply Chain
Management: An International Journal, 12(6): 400 – 411.
Grout, J. R. (1997). A model of incentive contracts for just-in-time delivery. European
Journal of Operational Research, 96(1), 139-147.
Gupta, D., & Weerawat, W. (2006). Supplier–manufacturer coordination in capacitated two-stage supply chains. European Journal of Operational Research, 175(1), 67-89.
Hamner, W. C., & Organ, D. W. (1978). Organizational behavior: An applied psychological
approach. Dallas: Business Publications.
Handfield, R. B., & Melnyk, S. A. (1998). The scientific theory-building process: a primer using the case of TQM. Journal of operations management, 16(4), 321-339.
Hennet, J. C., & Arda, Y. (2008). Supply chain coordination: A game-theory approach.
Engineering Applications of Artificial Intelligence, 21(3), 399-405.
Kaipia, R., & Tanskanen, K. (2003). Vendor managed category management—an outsourcing solution in retailing. Journal of Purchasing and Supply Management, 9(4), 165-175. Karlsson, C. (2009). Researching Operations Management. Routledge: New York.
Keith, J., Jackson, D & Crosby, L. (1990). Effects of Alternative Types of Influence Strategies Under Different Channel Dependence Structures. Journal of Marketing, 54(3), 30–41.
Korpela, J., & Tuominen, M. (1996). Benchmarking logistics performance with an application of the analytic hierarchy process. Engineering Management, 43(3), 323-333.
Kumar, N., Scheer L.K., & Steenkamp, J.B.E. (1995). The Effects of Supplier Fairness on Vulnerable Resellers. Journal of Marketing Research, 32 (1), 54-65.
Kumar, N., Scheer, L. K., & Steenkamp, J. B. E. (1998). Interdependence, punitive capability, and the reciprocation of punitive actions in channel relationships. Journal of Marketing
Research, 35 (2), 225-235.
Mentzer, J. T., Flint, D. J., & Hult, T. M. (2001). Logistics Service Quality as a segment-customized process, Journal of Marketing, 65(4), 82-104.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage Publishing.
Payan, J. M., & McFarland, R. G. (2005). Decomposing influence strategies: Argument structure and dependence as determinants of the effectiveness of influence strategies in gaining channel member compliance. The Journal of Marketing, 69(3), 66–79.
30 Sims, H. P., Jr., & Gioia, D. A. (1986). The thinking organization. San Francisco:
Jossey-Bass
Slack, N., & Lewis, M. (2002). Operations Strategy, Harlow: Prentice Hall
Thonemann, U. W., & Bradley, J. R. (2002). The effect of product variety on supply-chain performance. European Journal of Operational Research, 143(3), 548-569.
Trautrims, A., Grant, D., Fernie, A., & Harrison, T. (2009). Optimizing on-shelf availability for customer service and profit. Journal of Business Logistics, 30(2), 231-47.
Vachon, S., & Klassen, R. D. (2002). An exploratory investigation of the effects of supply chain complexity on delivery performance. Engineering Management, 49(3), 218-230. Van Donk, D. P. (2001). Make to stock or make to order: The decoupling point in the food
processing industries. International Journal of Production Economics, 69(3), 297-306. Van Woensel, T., van Donselaar, K., Broekmeulen., R. & Fransoo, J. (2007). Consumer
responses to shelf out-of-stocks of perishable products. International Journal of Physical
Distribution & Logistics Management, 3(9), 704.
31 APPENDIX A- MEASUREMENTS
Questionnaire Corporation A
1. Timing of the penalty- What is the procedure in relation to the moment and schedule in which you check non-compliance of the agreed service levels and why?
2. Intensity of the penalty- What is the procedure in relation to the intensity of penalties and why?
3. Procedural justice of the penalty- Do you have the feeling that your penalties are perceived as just and fair by suppliers and why?
4. Supplier support- Do you provide insights in the measured delivery performance of suppliers and why? And do you provide forecast information as well?
5. Power division- In what way do you think that power plays a role in the effectiveness of your penalties?
Supplier’s questionnaire
1. Timing of the penalty- Corporation A uses a quarterly penalty schedule in which they check non-compliance of the agreed service levels. The check takes 1,5 month and 1 month is provided to you for making objections. What do you think about this procedure and why? 2. Intensity of the penalty- Corporation A imposes penalties with intensities based on the net purchase value of the non-performed orders. What do you think about this intensity of penalties and why? Do you perceive them as low, moderate or high?
3. Procedural justice of the penalty- Corporation A imposes penalties based on non-performance of on-time-in-full deliveries. Do you have the feeling that these penalties are just and fair and why?
4. Supplier support- Does Corporation A provides enough insights in the measured delivery performance and why? Do you think that you receive (enough) forecast information of Corporation A and why?
32 Additional Data
1. Timing of the penalty- formal penalty procedure of Corporation A 2. Intensity of the penalty- formal penalty procedure of Corporation A
3. Procedural justice of the penalty- penalty complaint letters of suppliers and electronic data about the number of penalties credited from Corporation A’s information systems of the past two years
33 APPENDIX B- INTERVIEW PROTOCOL
Beforehand
1. Make date and time arrangement for the interview by telephone
2. Sent questions by email a week before the meeting so the informant knows what to expect 3. Request the interviewee for permission for recording the conversation
4. Check the tape recorder (in this case the battery level of the mobile phone)
During
1. Introduce myself and explain the aim of this research 2. Re-confirm the permission to record the conversation 3. Start the tape recorder
4. Take notes of key findings
5. Request permission for transcribing the conversation as well as handing it over by e-mail 6. Thank the informant for his/her time
After
1. Write up interview notes 2. Transcribe the records in Word
3. Send the transcriptions to the informants by e-mail and ask for confirmation 4. Save transcriptions and notes in the database
34 APPENDIX C- FORMAL PENALTY PROCEDURE CORPORATION A
Figure C1 below shows a graphical representation of Corporation A’s penalty process. FIGURE C1.
Quarterly Penalty Check C.1 Performance indicators
On-time delivery performance:
Order accuracy performance:
C.2 Performance Standards
The on-time delivery is measured on a daily basis and the order accuracy is measured on collo (package) level.
The standard of the on-time delivery is set at 95%, i.e. the supplier delivered 95% of the orders on the original scheduled delivery date.
The standard of the order accuracy is set at 98%, i.e. the supplier delivered 98% of all colli ordered by Corporation A.
Number of orders delivered on-time per quarter (date delivered = date scheduled original) Total number of orders per quarter
