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Specialization: Transport Engineering and Logistics Report number: 2014.TL.7884
Title: Packaging and Pallet
Organization, Optimization, and Operation
Author: J.K. van Zeeland, BSc BA
Title (in Dutch) Verpakking en Pallet Organisatie, Optimalisatie en Operaties
Assignment: Research Assignment ME2130-15 Confidential: yes (until December 31, 2020)
Initiator (company): M. van de Loenhorst MSc, M. Loonen MSc (Unilever N.V., Rotterdam) Supervisor: dr. ir. H.P.M. Veeke
Summary
The research focusses on the Unilever company, it complements literature with real-life ex-amples; additionally it explores several aspects which constitute a conceptual framework—it explores parties involved and phsyical properties of the items in the supply chain. It, finally, analyses Unilever internal data to prove the need for a better understanding what makes up a ‘good design’.
Why packaging? The answer two the question turns out twofold; on the one hand packag-ing serves an ‘ease of transportation use’ and on the other hand it serves a customer purpose ‘product recognition, conveying message en information distribution’. Both impose different requirements on the product packaging design.
From the transportation view mostly structural requirements are superimposed; from the customer stance mostly soft (e.g. color depending on product and target audience) and legislative (e.g. readability, safeguarding).
Packaging within Unilever is the product of a large amount of distributed decisions; which is shown to lead to sub optimal overall designs—either targeted on customer or transport. Each party (e.g. logistics, research and development, finance, the customers) has particular goals and idea’s about what constitutes a ‘good design’, but no global strategy exists.
If the different parties were aligned it would make sense to locally optimize (within the global strategy). By and large current ‘optimization’ in pallet handling is based on heuristics. Standards with illusory origins range from underhang to pallet height to application of stretch wrap. It is shown that simple calculations can put a theoretical basis to daily practise and that benefits include more stable transportation units as well as higher floor space utilization. However, there is a large scope of external factors which are hard—if not impossible—to discount in a model.
This problem also arises when a ‘3D’ stack is considered. Theory—although it has a nice structured approach—not yet proven to be as effective as the practice. Practice includes half column stacked and half interlocked; this is currently impossible to model. However, practical models (such as PackDev Pro and Pack Expert ) based on heuristics also prove to be not fully reliable—yielding failed trials when the proposed strategies are adopted.
Accurate insight into the breakdown of this inefficiency could potentially lead to increased profits (or decreased losses) as well as reducing the environmental impact of the transporta-tion—something Unilever has vouched to do for the last few years. Therefore the proposed strategy tries to find a global optimum in such a way that overall the company wins; and not just a single party.
Abbreviations and Acronyms
Abbreviations
EUR European (pallet)
IND Industrial (pallet)
IR Infrared
Acronyms
CEO Chief Executive Officer
DC Distribution Center (warehouse) DS Double Stacked (pallets)
HSS High Single Stacked (pallets)
IP Integer Programming
KPI Key Performance Indicator
PE Pack Expert
PLP Pallet Loading Problem RDC Regional Deploy Center RFID Radio Frequency Identification
ROI Return On Investment
SC Supply Chain
SS Single Stacked (pallets) SU Sourcing Unit (factory)
Contents
1 Introduction 1 2 Where to begin. . . 2 3 Integral Design 3 3.1 Scoping . . . 3 3.2 Logistics . . . 3 4 Conflicting Interests 5 4.1 Key Players . . . 5 4.2 Symbiosis . . . 7 5 Supply Chain 9 5.1 Pallet Height Optimization . . . 95.2 Physical Integration . . . 10
5.3 ‘Quaternary’ Packaging . . . 10
6 Stacking Integrity 14 6.1 Optimality . . . 14
6.2 Pallet Organization Algorithms . . . 15
6.3 Floor Plan Optimization . . . 15
6.4 Cube Optimization . . . 16
6.5 Structural Integrity . . . 17
6.6 Additional Methods for Stability . . . 18
7 Package Considerations 20 7.1 Pallet Utilization . . . 20 7.2 Underhang . . . 21 7.3 Real Life . . . 22 8 Packaging 24 8.1 Primary Packaging . . . 24 8.2 Secondary Packaging . . . 25 8.3 Tertiary Packaging . . . 27 9 Requirements 29 10 Conclusions 31 11 Recommendations 32 A Full non-guillotine Pallet Loading Problem 35 A.1 Stage 1 . . . 35 A.2 Stage 2 . . . 35 A.3 Stage 3 . . . 36 A.4 Conclusions . . . 37 B Rich Picture 38 C CAPEPalletLayout 39
D Standard Scores 40
1
Introduction
Packaging of products is a very important part in the supply chain for all companies trans-porting goods. The product determines the pack material requirements and—to some ex-tend—the size of the primary package. In turn primary packaging material and shapes determine the maximum height a pallet can reach and this—in turn—determines the utili-sation of transport equipment. Additionally—or maybe primary—packaging safeguards this primary products; the goods that need shipping.
This research assignment is an integral part of the graduation project and aims therefore at synergy between the two. It will also provide a starting point (an initial research question) for the thesis. In this assignment I investigated literature, researched companies, visited sites and used internal data—which is (hopefully) reflected in this work. The graduation project takes place at Unilever N.V.—a fast moving consumer good company. An industrial giant such as Unilever transports goods cross borders—cross continents, but that is beyond the scope of this assignment, which will solely focus on Europe—and is a big consumer of packaging materials.
The aim is to see where the influences and requirements for packaging stem from? Who is responsible for these requirements? And to what extend to these parties influence the shape of the supply chain? These questions lead to the research question:
Which requirements do we superimpose on packaging, where do these require-ments originate from?
This question will enable the research to look at both the organizational structure behind the creation of packaging as well as the physical implementation of the aforementioned.
In order to find the answer we will assess literature and internal documents and compare this to the day-to-day operations; logistics planning and physical implementation.
This literature survey and logistics research aims at showing the similarities and dis-crepancies between the two. Incidentally hoping to stumble at interesting and potentially beneficial solutions that have been found in one area of expertise and apply this to the other.
2
Where to begin. . .
So where does packaging start? At the one hand it starts at the customer; a customer recognizes the product by its facing, by its packaging. On the other hand it starts when the product finished and should be packed to be handled.
This also becomes clear when we ask for the function of packaging. The function of packaging is threefold:
• convey information; • protect; and
• increase ease of handling.
This functional description can be interpreted in a multitude of ways. Conveying information can be done in a unlimited number of ways, judging from the gigantic number of products that are for sale nowadays.
Specific interpretations for ‘protection’ and ‘increasing ease of handling’ are also innu-merable. For all products such a specific interpretation exists. These all stem from the requirements that are superimposed on the product and the packaging.
Such requirements are, on the highest aggregation layer: • aesthetic requirements; and
• physical requirements.
Both are dominated by the legislative requirements that exist. Conceivably all requirements can be altered or undermined except the legislative ones. This research aims at giving an extensive list of these requirements, necessary conditions. The impatient reader is referred to table 7 for a concluding chart. This research’s focus will be mainly on the physical requirements, but will shortly touch upon the other two. This is mainly due to the fact that both aesthetics and legislation is not within the standard engineering discourse—not due to the fact that it is deemed unimportant.
We will start by identifying the supply chain within Unilever as a company, whilst keeping in mind that all is done to finally serve the customer (and companies profit). Additionally the reader should be informed that packaging starts whence the product is finished; we will therefore not look at the process before the finished product. We will be concerned with the process from when the product is packed to the point that the product is delivered to the customer (so we also do not bother with selling the product, this is part of a different process). Now, let this be the starting point for the assignment.
3
Integral Design
This research assignment is focussed on Unilevers as a company. This is the main constriction of the research; it will not be bothered by how razor blades or big scientific instruments are made or tansported—since these are both not in Unilevers portfolio. It will focus on the items that are part of Unilevers portfolio which mainly consist of food and food-related products. This chapter will serve to limit the scope of further chapters and provide initial company background.
3.1 Scoping
Unilever is mainly a marketing company; its aim is to sell food (and food-related) products to the largest possible audience. There are many ways one could assess the company; since both studies and assignment require me to assess transport and logistics this will be the main goal of the research assignment. What are the main influencers in the logistics? What are considerations in choosing a package material? Why are certain choices made in the supply chain? These are some questions we will seek answer to.
Including the one is ipso facto excluding the other. Since the focus is on logistics, we will not focus on sociological problems that exists within the company; nor will we try to design a new product—athough we will see that both these aspects have an influence on the supply chain and logistics.
Understanding Unilever is not very easy, Unilever is roughly spit into three parts: the categories, the regions, and the functional part. The hierarchy is schematically shown in Table 1; Unilever maintains that it has a matrix structure. This structure puts the cat-egories at the top of the columns and the functions lead the rows. This results in ‘cross functional teams’ consisting of quality, research and development, logistics, etcetera, which work together in a dedicated category (e.g. refreshments or foods)
Table 1: A schematic representation of the organisational structure of Unilever; it includes the regional, categorical and functional split in the company. Additionally a few relevant trees have longer branches. A more in-depth description can be found in the supporting text.
categorical
refreshments foods personal care home care
functional R&D quality
logistics cross functional strategic
brand development teams teams
planning customer service regional sourcing units
distribution centres cross category operative
MCOs teams teams
planning
3.2 Logistics
This section is zooming in on logistics—what is it we are talking about when we mention ‘logistics’ ? To some extend it is the transport of products; but to a larger extend it involves all changes the product undergoes from creation to ending up in the customers’ hands. Figure 1 shows, on a very high aggregation layer, the different aspects in the supply chain. Note that in a few alineas we have started from all possible research assignments and ended up at one aimed at the supply chain for Unilever. This supply chain is (of course) not a
Figure 1: A schematic showing the Unilever supply chain, from initial buying of raw materials and packaging to the delivery to the customers. The above layer shows the ‘buildings’ involved, sourcing units, regional deploy centres and distribution centres. The second layer shows the transport and naming thereof. Courtesey of Unilever N.V.
standalone; it is part of the Unilever company and internal going-ons. There is a big number of different influencers. Who are these influencers? And to what extend do they influence the packaging of materials?
For logistics physical influencers are, for example: • transport; • packaging cost; • packaging material; • storage; • handling operations; • picking;
• logistics trade terms; and • pallet rental.
In addition hereto the respective departments should agree in joining such a project. If R&D decides not to join the strategy than, from starters it is practically impossible to change the process. Parties involved in such a project (for Unilever) are:
• research and development; • product and innovation;
• distribution centres or warehouses; • sourcing units or factories;
• customers and clients; • logistics;
• marketing;
• transport operators;
• secondary packaging producers; and • finance.1
This renders a quick image of the complexity of such projects. During the review of literature it was found that very small parts are researched; singled-out problems rather than complete integration.
Chapter 4 will explore these parties involved and the influence they have on the packaging in the supply chain. This will gives us an idea of how to fit supply chain in the company. Chapter 5 will zoom in on the physical aspects and explore this further.
1Finance is included because sometimes it is necessary to have a capital investment in order to reduce
4
Conflicting Interests
This chapter aims at introducing some terminology that is used within the company as well as showing the magnitude of the operations and influencers. Additionally its goal is to provide the reader with an insight of different groups’ different targets; how they conflict and how they fit within the logistics weltanschauung. Who is involved—and to what extend—in the supply chain? Why is it so hard to make changes in the chain?—are the question we seek answer to.
4.1 Key Players
Reiterating the list from chapter 3; the key players in the design of a product and bringing that to market are: research and development, product and innovations, distribution cen-tres and warehouses, sourcing units and factories, logistics, marketing, operators, secondary packaging producers, finance and ultimately the customers.
We will quickly review these and see what their function is and their targets are. Research and Development
Research and development is tasked with the physical implementation of a given idea; this idea come in what is called the creative design brief and is nothing more than a vision for a (new) product. With this idea they will try to find or design the ‘best’ primary case to match the products vision.
Design related to secondary and tertiary packaging is only partially included in the design process. Something called the design funnel gives guidelines regarding ‘good’ pallet organi-zation and packaging strategies. It uses, therefor, the CAPE-tool, which will be explained later. Which at this stage gives no price indication of the different solutions. More often than not, heuristics are used in choosing the ‘optimal’ pallet design.
Consider for example the following: a bigger primary pack is (naturally) easier to spot in the supermarkets; making the likelihood of sales also increase. (More sales equals more income.) But bigger primary packs yield higher transport cost, because more pallets are need to ship big products than small products. (More pallets equals more expenses.) There is no way to see where the break-even point is since there is no (proper) way to asses the expenses (increase or decrease).
Research and development puts mainly aesthetic requirements in addition to the legisla-tive requirements that exists and should be obeyed.
Procurement and Finance
As mentioned before, in contrast to creating the ‘best’ product for customers, procurement is aiming at ensuring the lowest resource cost; this can be for the goods as well as the packaging. These two are (often) contradictory. From a research and development stance higher grade secondary packaging (to safeguard the integrity of primary packs) is always preferred, whereas load sharing could be preferred by procurement to reduce expenses.
Finance has a goal which is similar to procurement, i.e. minimizing expenses and ‘know-ing’ where the money goes.
Marketing
Marketing is responsible for selling the products; its goal is to make the customers aware of the ‘value’ of a product and thereby creating a need for that specific product.
Marketing is in some sense contradictory to the goals of finance, there is always a balance in marketing expenses and financial revenues; this should be balanced, but ostensibly both have the opposite goal.
Product and Innovation
P&I is key in communication between sourcing units (SU)2 and marketeers; they make initial sales projections and verify the SUs capacity to produce the estimates. P&I manages innovations from a supply chain perspective; they coordinate all product changes. Because they make initial estimates (regarding volumes etcetera) and manage the supply chain they are important in the introduction of new supply chain strategies.
SUs and DCs
Ultimately the sourcing units (or factories) have to implement the wishes from ‘higher-up’ they have to physically create the product and its packaging (if that is not produced elsewhere) whilst having limit say in the design thereof. They are a plaything of the external factors and can only reply with physical limitations of their systems. This more or less holds for distribution centres (DC) too.3 They can only induce their physical limitations on the system and have no further influence.
The only difference for both DCs and SUs is that they can influence the wrapping; this is not ‘designed’ beforehand and is subject to the skill fullness of the operator; which varies form operator -to-operator and day-to-day. There is some movement in this area; that is, in order to cut wrapping cost—which are in excess ofe220 M—a predefined wrapping strategy is chosen. This is superimposed on the operators; but this project is not fully advanced at this stage.
Requirements from sourcing units and distribution centres stem mainly from equipment or material restrictions. Sourcing units are normally build to fulfil the requirements of a certain product and are therefore normally limited only to material or equipment restrictions. Logistics an Operations
Logistics and operations ‘oversee’ the supply chain at Unilever. Starting at buying bulk goods—all the way to customers.
Reiterating the process: after the products are bought, the SUs create the product in its primary casing—the bottle of shampoo, the can of ice tea, the pouch of dried soup—and puts this in a secondary casing—the carboard box (or others, as we have seen). The ‘organisation’ or products in a secondary casing is called a collation. These cases are then put on a pallet and shipped to a distribution center (DC), the process thereof is called ‘primary transport’. Pallets can be equipped with corner posts or shrink wrapping to secure the load better or to avoid damage to the secondary and primary casing. Here the goods are stored for an amount of time; they are placed in racks either by hand (i.e. by usage of forklift trucks) or an automated system. When the pallets are too high to be handled by the warehouse or the racks or customer requirements they can be de-topped, meaning that a complete layer of cases is removed from the top and put on a new pallet. This process can be repeated when products are shipped to a second DC, but this does not change the basic concept.
2
Sourcing units or factories and co-packers are the same, the only difference being that sourcing units are owned by Unilever and factories and co-packers are hired to do a certain product for a certain amount of time (sometimes indefinite).
3
The distribution center is a euphemism for a warehouse, again the difference being that the DC is owned by Unilever
When a customer—parties such as Tesco, Ahold, Delheaze—requests a certain product they can either order a full pallet or a ‘picked’-pallet. In case of the picked pallet a few secondary cases are taken from a full pallet and put on a new pallet in order to satisfy customer demand. These are then shipped, in what is called ‘secondary transport’, to the customer. Finally, the customer put the Unilever brands in the hands of clients.
Annual expenditure per individual posts is given below. It can be seen that transportation is 66% of the total logistics cost, warehousing makes up for another 25% of the cost. (It should be noted that logistics cost make up for only 4%(!) of the total annual expenses.) Best results are therefore obtained by optimality in the transport-leg. One should not be led to believe that this is the only way, it could be simpler to change the warehousing and reducing cost more severe. Only in-depth analysis could provide the answer.
Table 2: Expenditure on different ‘posts’ in Unilevers SC, since these are Unilevers expenses the supplier and customer warehousing do not get any cost. Projects F1ve, Duplo and others try to reduce the total SC expenses. Costs are divided into static and dynamic cost posts. Courtesy of Unilever N.V.
Post annual expenditure (million e)
Supplier
-Sourcing Units 80
Primary Warehousing 260
Secondary Warehousing 15
Customer Warehousing
-Raw and Pack Transport 170
Primary Transport 235
Secondary Transport 290
Total expenditure 1050
Since each SU and DC has their own organisational structure and cost-limits they should be persuaded into joining the complete end-to-end renovations. This would be easier if Unilever managed support for the SUs and DCs accordingly, if this is not the case than it is easiest to also provide direct saving for each party. It is witnessed that logistics imposes structural restrictions or requirements on the goods. They require for example double stack-ing. This requirements then flows down in the design tree and finally imposes requirements on the primary packaging.
Customers
Finally, and arguably most important, the customers. Customers do not know what they want. . . however if we perceive Unilevers customers to be the companies such as Ahold, Delhaize, and Tesco than we might say a few thing about this. Companies want easy-to-handle goods at the lowest price (which sell well in their stores).
Quality is in constant contact with the customers; and make sure orders are fulfilled and issues are resolved. Notably quality issues mainly revolve around the ‘not being able to deliver due to shortage of supply in local DCs’ and not around ‘collapsed pallets or damaged cases’. However quality assured that its Unilevers task to make sure that no damaged boxes reach the customer in the first place.
4.2 Symbiosis
What this chapter aims at showing is that the design process is a decoupled system in which several unaligned targets are involved. These lead to (possibly) an overall less than perfect design; alignment of the end-to-end is crucial in creating products that are not only good for marketing but also good for transportation and for procurement, etcetera.
This also hints at the answer to the research question, in addition to physical constraints (imposed by material properties, machine settings and such) is a strong non-physical con-straints which is human interaction and their respective targets which makes it ‘so hard to implemented changes in the supply chain’. These non-physical constraints are as important as the physical constraints because they determine, to a large extend, what the focus is on. Again it is the synergy between distributed decision makers rather than a central meaner.
The aforemtioned information serves as background information to draw a rich picture. The rich picture is not bounded to any rules as its goal is to map the situation. It should map—as extensively as possible—the relations, interactions, stake holders, proposition and opposition, of the transportation and logistics within Unilever. Figure 2 shows an excerpt from the complete rich picture which can be found in Appendix B.
Figure 2: A small excerpt from the rich picture, it shows stakeholders (such as logistics), operational entities (the factories) and moved objects (the pallets).
When a new product is designed the involved should (at least) do the following.
1. Identify the influencers in the process of creating a new product (which is including the aforementioned splits, but not limited to).
2. Indicate the extend of the influence, determine the ‘weight’ of each different split (for instance based on the total expense or revenue).
3. Define an global strategy; not local—per split—but cross category. Ask the question: what is the eventual goal for this particular product? Maximum profit? Maximum impact? and act accordingly.
5
Supply Chain
We have touched shortly upon the supply chain this was, however, in a more sociological con-text. This chapter, as will the rest of the research, focus more on the physical implementation of different aspects.
Modern logistics problems concern end-to-end logistics rather than optimizing each in-dividual contributor. It is already known how to make the smallest, or most cost effective, secondary package and it is also known (to some extend) how to have the greatest shelf presence. However, the integration of primary casing into the complete supply chain is still in its early phases.
Melis (1989) calls ‘package optimization’ as the main concern of packaging developers. Its aim is to achieve an balance between performance, quality and cost, i.e. value for money. It involves a detailed examination of each cost element in the packaging system and an evaluation of the contribution of each item to the functionality of the system. This, obviously, holds for the complete end-to-end supply chain.
This has proven to be quite the hassle due to the enormous amount of conflicting require-ments; within Unilever it is noted that, although the company has one big strategy, different branches of the company have different targets, to name just a few:
• marketing’s target is to increase gross sales value; i.e. increase revenues; • procurement aims at lowering cost by ensuring cheapest resources;
• logistics aims at highest customer service (on time delivery) at lowest rate; and • research and development has to combine all of the above into a single product. This chapter aims at describing the processes and contradictions. It also serves 5.1 Pallet Height Optimization
One of the many projects unrolled in Unilever is that of ‘pallet height optimization’. To see what the implications of such a project are we shall investigate the subject a little further. Foremost, and most of the time the only considered variable, is the transportation.4
If the full chain is tried to be optimized certain variables are singled out and central meaner is assumed. Or as Bravo and Vidal (2013) puts it eloquently: “All the papers [concerning supply chain optimization] in this [extensive literature] review attempt to develop an integrated model that typically seeks to minimize the total production, inventory, and distribution costs. It is reasonable to suppose that integrated models assume that there is a central planner who is observing and controlling the model of the entire chain and has the power to implement the strategies provided by the model. So, in integrated models, the decisions of all the supply chain partners are included in a single formulation and all the partners are willing to do what the model determines for the benefit of the entire chain. Usually, integrated models achieve a better objective value than models in which each agent is optimized independently or hierarchically, and this has generally been the main reason for their widespread use.”
Within Unilever there is rather an alignment of distributed decision makers which are enabled by a central meaner. It is however very hard to pressure these different parties and they should often be persuaded into joining a project—as well as respecting their main targets. (In other words, here is another discrepancy between literature and application.)
4
Within Unilever transportation is about 66% of the total logistics cost per product as internal data suggests. This is backed by Wilson (2012) who notes that 64% of total logistics cost in the United States of America is made up by logistics; the remainder is made up by inventory cost (33%) and administrative cost (4%).
5.2 Physical Integration
In addition—or more alongside—with buisiness integration there is also a physical integration possible. Suppose parties are aligned and working towards the same goal than it is possible to integrate different functions. The question is: is it possible, if the different parties are aligned to, also, align the physical aspects.
One of those functions currently under revision within Unilever is that of load-sharing. In this case part (or the complete) layer weight is carried by the primary package. Looking at Figure 14 we see that ‘tray and overwrap’ as well as ‘naked shrinkwrap’ are already in place to let the primary casing (the product) carry all the weight of the stacked casings on the pallet. This can also be witnessed from Figure 11, where the products carry all the loads. Another form of problems that occur in the physical implementation is that of contra-diction of boundary conditions; suppose that, for instance, the optimal pallet (in terms of weight) is 1000 kg. This would yield a suboptimal cube fill for a truck, which is limited to 24,000 kg load and 33 pallets ground floor space. These contradictions can be solved if optimality is defined (which is, for a company, normally defined in maximizing revenues). If the weights of the variables that make up the total cost are known a good estimation of what optimality entails can be made. Note essentially that all problems can be solved with infinite amount of capital investment (CAPEX); however Unilever holds that CAPEX with a Return on Investment (ROI) of less than three years is allowed, whereas ROI larger than three years is considered unprofitable(!).
In line with the above mentioned the ‘optimal’ switch-over from high single stacked (HSS) to double stacked (DS) can be made. High single stacks concern pallets where hpallet ≈ 1.5 − 1.8m, double stacks concern two single pallets where for each hpallet ≤ 1.2m.
If a weight is given to the different variables (such as picking, truck fill, etcetera) it can be used to determine the optimal switch-over point (where the result can be dictated by the truck fill or by the buying rate of customers). Research into this topic has yet to be performed. Contemporary assumption is that cubical efficiency (mkg3) is the driver for this
switch-over—which further analysis have to prove right. Double stacking is trending, Procter & Gamble has vowed to (starting next year) double stack everything.
Other companies—such as Procter & Gamble—allegedly also plan on double stacking pallets (or use double decker trucks, of which the result is approximately the same). Some software on the topic exists (such as TOPS MaxLoad Pro); but these do not incorporate the product specific casing specifications such as maximum load to be placed on top of the first layer—a typical challenge for Unilever.
5.3 ‘Quaternary’ Packaging
Isomorphically to the optimization of tertiary, secondary, or primary cases we can also asses the ‘usage’ of warehouses or trucks (the ‘quaternary’ packaging). As mentioned before 66% of the logistics cost involve transportation; understandably large profits can be gained if the trucks are used to their full capability. Trucks have (at large) two constraints, weight and volume.
To get a feel for the multiplicity of pallet designs (on a single type of pallet, namely a EUR pallet) look at Figure 3; it plots the weights of the pallet against the height and the density (which is the total weight divided by the pallet height). These are only data points of the—arbitrarily chosen—‘dressings’ category which included some 2025 data points. (Refer to Appendix E for the code that produced the respective graphs.)
If we assess the data from ‘both’ sides we get the images shown in Figure 4 - 5. Quite unimportant is the location of individual data points. What this images aims to show is the wide spread of pallet designs—some are high and light (400 kg and 2 m high)and some are
Figure 3: A scatterplot with linear regression plane (not for analysis but for perspective) for the dressings category. The blue dots are refrigerated trucks, whereas the red indicates ambient trailers.
less than a meter and more than 900 kg. This becomes more clustered if we look at densities (weight divided by the volume)—however, here to the spread is large.
If we want to optimally (i.e. as ‘full’ as possible) load trucks we should conform to both the weight and volume criterion. A combined efficiency could be given as:
ηtruckf ill = ηweightf ill· ηvolumef ill, (1)
if both are in the order of 1 we have reached an ‘optimally’ filled truck; the equation can be further defined as:
ηtruckf ill=
current weight fill maximum weight fill ·
current volume fill
maximum volume fill, (2) suppose for example single stacked pallets in a truck (which has a capacity of 33 pallets, a maximum capacity of 24 tons, and a volume 92 m2). The 2 m, 400 kg add up to a truck
efficiency of:5 ηtruckf ill = 33 · 400 24000 · 0.96 · 2 · 33 92 = 0.55 · 0.69 ≈ 0.38. (3) The other case, 0.8 meters high and 900 yields an efficiency of:
ηtruckf ill=
26 · 900 24000 ·
0.96 · 0.8 · 26
92 = 0.98 · 0.22 ≈ 0.21. (4) Note that, although the maximum weight is reached the number of pallets cannot exceed 26 and that the overall ηtruckf ill is less than the previous.
We can use a datafile of Unilever which contains information on a lot of different char-acteristics, the way the data is assessed is shown in Appendix E. Applying this strategy to
5
Figure 4: Scatterplot of height versus weights for the dressings category.
Figure 5: Scatterplot of the height ver-sus density for the dressings category.
the all the ‘dressings’ we get Figure 6; which displays both in a single graph. Combining the data yield the total truck usage for the dressings category, shown in Figure 7. Finally ?? shows the truck fills under the same assumptions.
Figure 6: A scatterplot with usage data; further analysis and more refined data analysis is required. This is under heavy assumptions, but again, is meant to give an idea.
Preliminary conclusions are that truck are never used for more than 70 percent; off course the assumptions cannot be overstated, so therefore:
• a uniform fleet of trucks is assumed;
• it is assumed that trucks can be filled up to full capacity; • the master data is not filtered, errors could still exists; • calculations include ‘half’ and ’thirds’ of a pallet; and • all pallets are single stacked.
Figure 7: A histogram with usage data; further analysis and more refined data analysis is required. This is a histogram of the ‘dressings’ data; displaying the total truck usage, as defined above.
However, it is still remarkable how many trucks are loaded for less than 50%.
This, again, underlines the point that if we know ‘all’ aspects in a single split (e.g. logistics, or supply chain) we can use this to create a global optimum. We should ask ourselves:
1. Within my specific category (or functional, or regional) split what are the different factors contributing to a good (or even best) design?
2. And which parties can—or could, or should—have an influence on this specific aspect. 3. We should then actively communicate with the specific parties; and not just try to
6
Stacking Integrity
Let us now try to make explicit what was given tacitly: pallets are stacks and not ‘2D’ entities; stacking of cases on pallets; the organisation of secondary packaging on a tertiary package. A lot of research has been done on the ‘best’ organisation of cases on a pallet in order to get the ‘best’ results. A discussion on optimality can be found in the first section of this chapter since it is essential towards the answer of the research question.
Again we discern practice from theory, we will start off with a discussion on the literature and follow up with implemented models and their distinctions.
6.1 Optimality
Some notes on the notion of optimality are presented in this section. This is done for several reasons, first because the notion of optimality is widely used (myself included) and second because this notion is more difficult to understand than commonly assumed. Third, if we call for a ‘heuristics’-like method, or a combination of ‘model in close cooperation with real-life’, the notion of optimality is quickly lost. I will plea that even if heuristics are employed a notion of optimality for a certain problem still makes sense. In doing so I will also define what I mean by optimality, which I will consecutively use in the remainder of the thesis. The discussion also lead to a dichotomy between local and global optima.
In epistemology (on the nature of knowledge) in philosophy defines a optimality notion as consisting of both a learning rule and a decision making rule. In economic game theory, for example, the Nash6 equilibrium is a type of ‘non-cooperative strategy’ in which the players know the other players equilibrium strategy but in which no party gains by changing behaviour; this leads, thus, to a local optimum but (possibly) global sub-optimum (no gains are made by changing but the overall behaviour of the system can be sub-optimal). (Apt et al., 2008) Another such notion is that of Pareto equilibria. These notions make use of the distributive decision makers; however if we want to reach as global optimum in a company we have to change this a little.
Suppose for example the different strategies (i.e. minimizing cost for finance, maximizing customer satisfaction for research and development—however difficult to capture quantita-tively, or minimizing logistics cost). These strategies might be mutually exclusive (e.g. customer satisfaction cannot go up if logistics cost are minimized), suppose that this is true for some cases then by any equilibrium strategy a local (rather than global) optimum is the highest attainable goal.
(Relevance: employees of Unilever will always try to reach their personal (or groups) goals, these goals are in some cases mutually exclusive and employees have no incentive to divert to a less that optimal goal. If a (oppressive7) central meaner is imposed on the system it could lead to an overall or global optimum; whilst some locals have a strategy that is less that optimal.)
Keeping this in mind is important because, to cite Bostrom (2014): ‘[the] optimality notions can be of theoretical interest even if they are physically unrealized. They give us a standard by which to judge heuristic approximations, [. . . ]’.
Consider the following example; in truck fill, or truck fill efficiency, an local optimality notion exists. This can be negatively formulated by saying minimizing the wasted space; but positively by ‘optimal is the complete truck filled up to its limits (both weight and cube fill)’. No matter what the good is that is being transported and no matter what kind of truck is transporting the goods, this notion always holds.
6
Embodied by Russel Crowe in ‘a beatiful mind’.
A possible reaction could be that this notion of optimality will never be used or reached, i.e. there will always be head space and some room left for manoeuvring the pallets. True. So real life will always deviate from this sense of optimality. The effectiveness is thence defined as the realization divided by the feasibility (reachable in real life). In this case; feasibility is—let’s say—90% and the real life realization is just 80% (a best practice, so to say). That means that the effectiveness is around 89%.
Note that in this case we defined the feasibility as a deviation from the optimal 100%—in there lies my point; an optimality notion, reachable or not, gives a target to which we can strive, as well as a target which we can use to calculate effectiveness, etcetera.
6.2 Pallet Organization Algorithms
After this small digression lets get back to the subject at hand. Literature on pallet organisa-tion is vast. In a graduaorganisa-tion project Tica (2001) shows an extensive research into packaging and pallet floor utilisation, as well as stacking optimality. It is noted that models are either based on heuristics or integer programming (IP).
Both Letchford and Amaral (2001), Martins and Dell (2008), and Kocjan and Holmstrom (2010) refer to Beasley (1985) as being the first to define a linear program for the PLP. This model will be discussed in detail below.
Heuristics rely on gathered data of ‘proven-concepts’ and combining this knowledge to for a framework; when a new collation is presented the model draws upon this data to find ‘nearest’ solution. Heuristics is the use of previously gathered knowledge in order to solve problems more quickly. A discussion on the relation between heuristics and ‘optimal’ solutions flowing from IP is also presented in subsection 6.1.
A heuristics-based model is the Pack Expert (PE) (Smurfit Kappa, 2014), and a standard output is shown in Appendix C. This draws upon around 30 thousand previously conceived pallet stacks in order to give an indication of the packaging strength required for the to-be-made stack. The results of the PE tool differ, however, from the results that IP programs or ground floor optimization algorithms yield.
To see how they differ we will first introduce the PLP IP in an analytic manner. After this we will consider the discrepancies between the two methods.
6.3 Floor Plan Optimization
In Figure 8 the main problems with optimizing pallet floorspace can easily be seen. An optimally used floorspace means that all the floorspace is covered (excluding some underhang to account for placement inaccuracy). This is however, easier said then done. Smurfit Kappa alone, according to Berkenbosch (2012), creates over 4,500 different cardboard boxes per year, and there are about 2 different pallet sizes in Europe.
As mentioned before the PLP integer program is fully described by Kocjan and Holm-strom (2010) (as well as others). Additionally he expands the model to also entail the third dimension. We will look at the simple 2 dimensional definition of the model; for a full de-scription of the 3 dimensional model refer to Appendix A. Defined as Integer Programming, the optimal floor plan (in terms of space used) is:
max L−l X i=0 W −w X j=0 hij + L−w X i=0 W −l X j=0 vij, (5) min(r,L−l) X i=max(0,r−l) min(s,W −w) X j=max(0,s−w) hij + min(r,L−w) X i=max(0,r−w) min(s,W −l) X j=max(0,s−l) vij ≤ 1, (6)
Figure 8: The image shows a arbitrary floorplan layout. The grey area indicates the unused floor space, decreasing uf loor, the arrow indicates the underhang of the product. This is a
screenshot from CAPE, a pallet organisation tool. Courtesy of Unilever N.V.
hij ∈ {0, 1}(0 ≤ i ≤ L − l; 0 ≤ j ≤ W − w), (7)
vij ∈ {0, 1}(0 ≤ i ≤ L − w; 0 ≤ j ≤ W − l). (8)
Where r = 0 . . . L − 1 and s = 0 . . . W − 1. What this, effectively, means is that the floorspace is maximized under the condition that no two boxes can overlap each other (the cover constraint ). (Kocjan and Holmstrom, 2010) This is an adoption of the original solution by Beasley (1985). hij and vij represent a 0-1 variable which equal 1 if hij is horizontally or
vij is vertically aligned. (i, j) represents the position, or location, of the lower left corner.
Each summation, Σ, tries to find optimality in 1 dimension (maximization), combining these terms yield a floor plan optimal (in terms of space used). Additionally the guillotine contains is be added. These are prevent the stacks from having a full length or width cut. Again, refer to Figure 8 and witness the full-width ‘cut’—this is a guillotine cut. This would decrease the stability of the stack and is therefore overcome by introducing the specific constraint.
Although the model yields results it is yet unknown if the model is NP -hard to solve—we should not worry about this too much. (Letchford and Amaral, 2001) More important to note is that the presented PLP-program does not allow for full-width, or -length cuts, whereas Figure 8 clearly presents one. The floor plan presented comes from the program called CAPE. 6.4 Cube Optimization
The 2 dimensional solutions do only take into account the floor utilization. If we extend this to the third dimension different considerations play up. For instance the trade off between column stacking or interlocking stacking (see Figure 9) is based on a few factors:
• floor optimization; • ease of stacking; and • stack integrity.
The first is discussed before, the second is a very real constraint. It is much cheaper and faster, according to Smurfit Kappa, to column stack then to do the difficult interlocking. However, column stacking reduces the stability of the complete pallet significantly.8
8This is easily understood when you think that interlocking pallets are a much wider ‘tower’ and therefore
Figure 9: An image showing the difference between interlocking pallet patterns and column stacked patterns. Courtesy of Innovative Entreprises (2014)
Several tools exist to best use floor space of a pallet and to ‘think’ about stacking in-tegrity. Such programs are, amongst others, the aforementioned CAPE and PackDevPro. An example of a pallet loading scheme generated by CAPE can be found in Appendix C. Note, however that these programs all work with pre-defined casing sizes. There is no margin to optimize; in other words the programs just ‘deal with what is given’. Additionally, note that these programs have no structural calculations, they consider the boxes to be infinitely rigid, and they do not assume other boundary conditions than those given.
6.5 Structural Integrity
A program—the only one, according to Smurfit Kappa—that is concerned with structural integrity is Pack Expert by Smurfit Kappa. As said, over 30 thousand stacks have been used as input to create a tool which allows for designers and marketeers to calculate the required packaging strength. The tool can also be classified as a heuristics-model, but fairly theoretical. It supposes that a combination of column stacking and interlocking is the ‘best’ solution in many cases, because this gives advantages with respect to floor optimization and stacking integrity.
The model then calculates the required strength of the secondary package needed to support a x number of layers with a certain product, y. In an interview with Daragh Wall, European Account Director at Smurfit Kappa, it became clear that although the model seemed to be verified it lacked validation. After further contingency it became clear that on multiple occasions the PE suggested stacking pattern failed.
Tests by James Ross Consultants (JRC) commissioned by Unilever showed that the suggested pallets patterns failed to survive the ‘standard’ test to which new patterns are subjected. This discrepancy is, most likely, due to two factors:
• Theory-Practice; the model assumes that all boxes are perfectly stacked and aligned, meaning that all forces can be perfectly transmitted through walls and corner points, whereas real-life pallets always show some misalignments of cases on a pallet,
• External factors; such as shrink-wrap, accidental collisions, bad or insecure handling of cases, etcetera would all have a negative influence on the stacking strength of the boxes, and are almost impossible to discount.9
because the different boxes (in interlocking) touch multiple other boxes, the friction between those boxes keeps them in place.
9A method to do discount these variables would be to implement stochastic modelling; basically this
Although Smurfit Kappa claims that these effects are discounted in a safety factor, it seems like this is either too low or not complete.
It seems that the use of models are wide spread and oft implemented, albeit that they lack the practical side. The real challenge is to combine practice and theory—which is the subject of the next chapter.
To complicate the discussion another Unilever employee said that, when he used an existing (Unilever) product, say p, into the PE-tool it yielded result very close ‘what we are using nowadays’. An explanation that is likely that the data posed for analyses, the data p, is also used as input for the model (i.e. the database it draws upon), see Figure 10. When the model executes the request it retrieves the data p from the database and presents this to the user. If a new structure is requested, say m, which is not part of the input database it could yield sub-optimal results (like the ones failing, described above).
Figure 10: A schematic image showing arbitrary input and output into the PE-tool which is heuristics model drawing upon information from the database (and slightly altering the numbers).
6.6 Additional Methods for Stability
Figure 11: An image showing (left) the use of layer sheets to separate layers of products from each other, (right) the use of corner posts is shown. Note also that the left type of products does not allow for the use of corner posts, whereas the cardboard boxes and the rectangular shape of the stack easily allow the usage thereof. Courtesy of Unilever N.V.
afterwards. This method allows for irregular occurrences to be discounted in the model. The stochastic counterpart would be deterministic modelling, where all influenced are assumed known; this makes it nearly impossible to account for occurrences which occur less or more often.
In addition to secondary casings making up for the integrity of the pallet there are few additions that can be made. In terms of cardboard the factories or warehouses can equip the pallets with layer-sheets or corner posts, see Figure 11. These implementations ‘make it harder’ for products to fall over. In case of the layer sheets every layer is similar to the bottom layer and does not notice the influence of column stacking.
Both solutions have to be added manually to stack, and it also requires investment in cardboard from the factories. This process is therefore considered expensive and is as often as possible omitted from the process.
Another method of adding integrity to the structure is by adding (the aforementioned) stretch wrap to the pallet (also seen on Figure 11). This keeps products aligned and prevents small offsets. Additionally it also acts to secure load throughout the supply chain. However, applying the stretch-wrap is an art in itself. More is not always better. The wrapping is carefully designed and should therefore be applied in a similar way. If too much is applied, or the wrapping is applied with too much force it can damage the secondary packaging. Irregularly shaped products in a rectangular case or perforated cases are especially susceptible to these type of damages. Yearly expenses for stretch wrapping exceede200 M; and proper management of wrapping is assumed to yield savings in the order of millions.
So finally, when all parties are aligned and not singled-out goals are used as targets we can start by solving the issue that requires solving. This chapter aimed at showing that this local optimization should also be done with the utmost care; that is, reviewing the models that are used and not be easily persuaded by the marketing talk of the makers of certain products.
7
Package Considerations
If we zoom in further into the chain we find ourselves looking to optimize the entities entering the truck (since we have identified the truck efficiency as being ‘most’ important). These are, for Europe, the pallets.
Although one could, for example, try to optimize paths of transporting equipment (such as trucks) using models, which is extensively researched and documented by SteadieSeifi et al. (2013), or do operational planning, vehicle routing and point-to-point optimization, is extensively documented by Parragh et al. (2008), this is not very useful. The problems described and the solutions presented are already, to some extend, implemented by Unilever; in other words, they are, however interesting, not relevant for Unilever at this moment. The companies focus is mainly on truck fill efficiency in the supply chain (i.e. implementations that influence the complete chain, from creation to shelf).
When the question is posed: what influences supply chain end-to-end? it is found that this is mainly primary product, secondary packaging and tertiary packaging. These entities flow through the complete chain with relatively limited change.
The opposite approach would be looking at individual parts in the supply chain, such as: transportation, or individual warehouses or factories. This is not what integral design tries to do, however this is an option is easily implemented factory per factory, whereas the integral design requires all parties to be aligned.
In this chapter we will focus on the different aspects in the palletization of the supply chain. We will discuss items such as ‘underhang’ and ‘overhang’, and floor utilization—the ‘2D’—, but refrain from talking about optimizing stacks—the ‘3D’—, which was discussed in the last chapter. This optimization is called the the Pallet Loading Problem (PLP) and is defined as the problem of finding an optimal loading pattern for a set of identical boxes on a rectangular pallet. Supplemented by a few extra boundary conditions. (Kocjan and Holmstrom, 2010)
7.1 Pallet Utilization
Floor utilisation—a ‘2D’ utulisation—is defined as the total area taken in by all the secondary cases (in an optimal collation), Acasing, divided by the total area of the pallet, Apallet, often
expressed in a percentage. This is an key performance indicator (KPI) in the palletized systems.
uf loor =
Acasing
Apallet
× 100% (9)
If the utilisation, uf loor, is larger than 100% we have something called ‘overhang’, whereas
if uf loor ≤ 100% we have ‘underhang’. Underhang is often preferred to overhang because it
does prevent the secondary casing from being damaged when loading a truck, for instance. Additionally ‘underhang’ also sets clear boundaries for the algorithms to work with. Optimality can come in the form of best structural design, i.e. to keep the products stable during transit, or maximum used floorspace, sometimes leading to tower stacking, which is a very unstable stacking form.
3D utilisation—cube fill—follows the same line of reasoning. (It must be mentioned that when things are expressed as cube fill that this could lead to wrong conclusions, cube fill should always be expressed with respect to the largest height possible in the chain (say 1.7 m for a certain instance), if the pallet is then only 1.5 meters (and has an optimal floor utilization) its cube fill is around 88%, when the maximum height in the chain is 1.5 than it has an optimal cube fill. This makes comparing answers difficult.)
ucube=
Vcasing
Vpallet
This will give an indication of ‘how well the pace is used’.
Although underhang is preferred, often as little as possible is also preferred. This would mean that the floor space is used to the largest extend possible. Little underhang would allow pallets, when in transit, to support each other when the transporting vehicle is swaying. 7.2 Underhang
When underhang is further investigated an interesting fact occurs. Literature by Kocjan and Holmstrom (2010), Hodgson (2007), and Beasley (1985) all use a basic notion for underhang, i.e. the pallet should not be loaded such that the load sticks out over the edge of the pallet. This is easily discounted in the above PLP by, for instance virtually reducing the size of the pallet.10
In real-life heuristics are deployed. There are ‘standard’ measures which are used for designing a pallet. This ranges from 20 to 50 mm (or 10-25 mm per side) for an average pallet, according to Bas ten Brummelhuis.
A more educated approach would be that the underhang should depend on the margins in secondary packaging and the accuracy of the palletizer (the machine stacking multiple cases on a pallet).
µ is the mean, either for the casings size or the palletizer, and σ is the standard deviation.11 For completeness the formula, describing the behaviour of the normal distribution is shown in Equation 11. f (t) = 1 σ√2πexp − (t − µ)2 2σ2 , (11) for which: Z ∞ −∞ f (t)dt = 1, (12)
and for a set of data the average mean and deviation are calculated by: µaverage = 1 n n X i=1 ti, (13) σaverage= v u u t 1 n − 1 n X i=1 (ti− µ)2, (14)
where ti are incidences measured in an experiment, for a series of products:
σseries=
q σ2
1 + σ22+ . . . + σ2n. (15)
The mean of the series is, understandably, a summation of the individual means.
The idea is: if the standard deviations and allowed ‘failures’12 are known than the un-derhang distance can be scaled accordingly.
So let us provide a calculated example. From Smurfit Kappa data it is clear that margins for boxes are ranging from 1 to 4 mm depending on the machinery used. For the outer case
10If the real-life pallet is 1200 mm by 800 mm (a standard European pallet), than the respective model-pallet
would be 1150 mm by 750 mm. (This action would decrease the uf loor with around 9%.) 11
The assumption is that the secondary casings are made within error obeying the normal distribution. This means, effectively, that as many products are larger than the mean as are smaller; additionally this also means that the likelihood of a much larger (or smaller) product decreases with increasing deviation.
12Failure is defined as a fraction of the total stacked systems which are overhanging the pallet. In other
dimensions this means is usually closer to 4 mm than 1 mm. Palletizer data shows that the standard deviation is much higher, in the order of 25 mm.
If we superimpose this data on the KLAR project we can create Table 3. From Ap-pendix C we also deduce that there are thirteen boxes stacked next to each other and five in-line with each other.
Table 3: The normal distributions for the individual parts as well as the combined values. The numbers are an manipulation of the numbers provided by the KLAR data sheet.
Item mean, µ, [mm] deviation, σ, [mm]
Single KLAR case width 89 4
Single KLAR case length 220 4
Palletizer 0 25
Combined (width) 1157 25.3
Combined (length) 1100 25.3
When deviations are known, the chance of the combined width exceeding is calculated according to: t = µ ± z · σ, (16) z = t − µ σ = 1200 − 1157 25.3 = 1.70. (17)
This corresponds to a z-value of 0.9554, or consequently to 4.46% of the pallets showing overhang. For a list of z-values refer to Appendix D.
If we for example allow 1 in 50 pallets to show overhang (2%), we perform the same calculation but backward. Corresponding standard score is 2.06,
t − µ z = σ =
1200 − 1157
2.06 = 20.9, (18)
meaning that the accuracy of the combined boxes-palletizer should go from 25.3 mm to 20.9 mm. This, in turn, means that the accuracy of the palletizer should go up from 25 mm deviation to ≈ 20.5. (A significant improvement.)
The significance hereof—or the added value—for a company is the following. When the rule of thumb (let’s say 50 mm) for underhang is used the pallets, considerable space is left free, both in terms of floor and cube optimality. Additionally it provides a well-argued strategy rather than heuristics.
Also, if there is too much space left this creates new problems. When transported by truck or boat pallets are put side-by-side, if a sudden movement occurs (breaking, or quick turning) the loads sway; they then use other pallets for support; however when the swaying gets excessive (due to underloading) loads could collapse. The closer the products are together the less movement is possible.13 If this margin can be reduced, by smart calculation, less waste can be achieved as well as more stable configurations which can lead to better product transport performance.
7.3 Real Life
Regarding the aforementioned discussion there are still some undiscussed points. First of all, pallet fill does not only depend on stacking or manufacturing accuracy. Other factors are
13
Double Stacking takes away this issue for the lower pallets. The higher pallets feel more of the trucks movement—as a result of being further removed from its center of gravity—and are therefore more prone to this instability, however due to their lower stacking height this effect is reduced to some extend.
hugely important. Firstly humidity can greatly influence the structural integrity; secondly wrapping, as can be seen from Figure 12, wrapping—which will be explained in more detail further on—vastly influences the integrity and the size of cases.14
Figure 12: A small selection of pallets from the Keuhne+Nagel warehouse in Raamsdonkveer; it clearly shows a discrepancy between real-life and the theory, even when pallets are (near) per-fectly stacked there are many other influences, such as forklift drives, wrapping, other damages. Courtesey of Unilever N.V.
An example of unforeseen problems with innovation can be seen at Poznan (Poland). In a recent implementation of Unilever—in which case the height of a pallet stack was increased to 1.8 m—the bottleneck was not the structural integrity of the supporting cases, and neither the floor utilisation factor. The problem was that, in the sourcing unit in Poznan (Poland), there was a ramp which had an inclination of about 10% and the sourcing unit manager believed that the higher stacks could form a potential hazard to the forklift-driving employee. What this example nicely illustrates is that it is important to take all aspects of stacking (and changes thereof) into consideration. Boundary conditions tend to come from ‘unforeseen’ places. A more elaborate discussion hereof will follow in section 3.
In short in designing the pallet we should, at least:
1. Design pallets with the minimum waste (unused space) possible; in practice this means minimizing underhang.
2. The design thereof is dependent on primary and secondary packaging; therefore integral design between the different parts is advocated.
14Obviously, boxes are strongest when the forces can be directed downward through their walls, when the
8
Packaging
Finally; after a long digression we can finally name the different types of packaging (e.g. primary, secondary, tertiary). We have seen that Unilever uses pallets to transport goods; these pallets pack a multitude of secondary packages—which in turn pack a multitude of primary packages.
the packaging comes in a large variety of sizes and formats and is universally used to transport goods in a safe manner. Webster Online Dictionary (2014) describes a package as something that comes in a container ; which is not much of a help. If we are to make a larger subdivision in the term ‘packaging’, than Coles and Kirwan (2011) gives a clear definition of an often used trichotomy in packaging.
Primary packaging Packaging being in direct contact with the to be shipped good that is intended for the customer to take away. It also serves as the inter-actor between customer and company.
Secondary packaging Packaging that contains and collates a set of primary packs. Also referred to as case or (outer)casing. It serves to protect the primary packs and make them easy to handle.
Tertiary packaging Packaging that collates a number of secondary cases, pallets and roll cages—as well as stretch wrap around a pallet—are tertiary packaging. It also serves to make transport for large numbers easier.
As McGann (2012), the Smurfit Kappa Packaging CEO, puts it: “[primary] casings should be identifiable, easy to open, present on the shelf, ‘shoppable’ and easy to dispose.”
This chapter will mainly aim at describing the first two, primary packs and the secondary casings. The latter—the stacking of cases, pallets, etcetera—will be discussed later on, in section 7 and section 6.
8.1 Primary Packaging
Packaging has been around since man has started to store and transport goods. The history of modern primary packaging is an elaborate one, described in detail in Coles and Kirwan (2011). Packaging as we currently know it—folded boxes, cans, glass jars—came up around the end of the 19th century and the beginning of the 20th. Recent developments include the plastic bottle, the shrink labels, and off course the environmentally friendly 100% recycled bottles, in-line with this producers of packaging can certify their complete supply chain according to several certification standards. (FSC, 2014)
The main purpose of the primary packaging is, i., keeping the contained food (or ware) well preserved. That is, the good should be well preserved from its creation to its first encounter with the consumer, as well as the primary package itself. According to Unilevers research and development, the primary packaging should never be damaged during transit since this will have a negative influence on the customers buying behaviour. Additionally also the, ii., products ‘shelf presence’—the way it is presented to the customer. (This is the reason why some products are double packed, first a carton box for the ‘shelf presence’ and secondly a plastic pouch to preserve the food.) A few examples of primary packaging are shown in Figure 13
In an interview with a research and development employee at Unilever, Bas ten Brum-melhuis, it became apparent that the process of creating a completely new primary pack takes about three(!) years. In this process the form factor, its shelf presence and the (little understood) human aspect are the most important. In addition they work with requirements set by Regional Deploy Centres (RDCs) (i.e. the local requirements or legislation). For ex-ample, the space above a liquid may not exceed 9% in Brazil, or, likewise, the Japanese
Figure 13: A small selection of primary packaging. The first is a soda can, both food preserving as shelf presence are discounted, the second—a shampoo bottle—is designed for shelf presence and the third is also about shelf presence (the pouch inside is made for conserving the food). Courtesey of Unilever N.V.
market wants their liquids in bottles of 160 ml, where standard variants maybe 100, 200 and 500 ml. These numbers are all regionally determined.
This why more often than completely new designs re-launches are brought to the market. These include major or minor changes to the product or the packaging. Think, for example, of a new fragrance in the deodorant series of AXE. This does not add any new complexity but extends the current portfolio. (The recent ‘compressed’ relaunch for al deodorants is an example of a major adjustment.)
Primary packs have to adhere to the European legislation regarding print. Although it does not put any constraints to the design it does add particulars to the information that a. needs to be provided, b. how that information is conveyed (e.g. non misleading) and c. the unambiguous interpretation of how the information should be printed on the product. The self-proclaimed scope is: ‘This Regulation provides the basis for the assurance of a high level of consumer protection in relation to food information, taking into account the differences in the perception of consumers and their information needs whilst ensuring the smooth functioning of the internal market.’ (European Union Regulations, 2011)
8.2 Secondary Packaging
There are several types of secondary packaging (a taxonomy can be made on basis of material used and final goal—is it just for transport or also used for on-shelf display). An extensive, but not exhaustive number is shown in Figure 14. The main purpose twofold; firstly, sec-ondary packages are to keep the primary package’s integrity. In addition its purpose is to make the product easier to handle, i.e. rather than storing products one-by-one, whole cases can be lifted, and add stability to the set of primary packages. Secondly, it is also intended as branding and displaying the product. (Deufol, 2014)
From Figure 14 one could falsely conclude that secondary packaging is only shrink wrap and cardboard boxes. This is off course not true, e-tailers work often with plastic ‘unit load devices’ (ULDs)—wasting huge amounts of volume but increasing handling speed. Beers come in crates made mainly of plastics. However, within Unilever these cardboard solutions are often used.
Cardboard boxes are mainly spit into two categories corrugated and solid board, see Figure 15. The first, corrugated, is the sturdy material, used often in e-commerce and for outer casings. Solid board is often used for displays which require accurate printing.15
Figure 14: Images of an extensive range of secondary product packaging. The first row shows tray and hood packaging, the second row tray and overwrap, third: naked shrinkwrap, fourth: outer case, fifth: wraparound case and—finally—, sixth: perforated case (or shelf-ready packag-ing). Courtesy of Unilever N.V.
Additionally, when it comes to secondary pack design, there are few things that need to be taken into account, a list of design considerations is presented below:
• different countries have different humidities, weather conditions and storage capabili-ties; since most packaging is cardboard, these variables have a huge impact on structural integrity and the size of casings,
• countries have different requirements concerning amounts of, e.g., fluids (100, 160, 200 ml) and secondary casings should be applicable to all variations of the product (or different casings should be designed for each variant),
• safety margins depend on the primary packaging qualities, i.e. the load baring capacity of the primary product, combined with the secondary packaging quality.16
Within the secondary (cardboard) packaging world there are many players, McGann (2012) shows that there are (within Europe) ten producers who have a market share larger
the tops in of the flute but worse on the lower points—creating an uneven pint.
16For reference, the safety factor, j, used by Unilever, on average, is 4.7, cross competition this safety factor
