Supply chain and operations management professionals are under constant pressure to reduce costs. The recession has forced many manufacturers to get creative when seeking savings opportunities in their supply chains. Ultimately, the key to reducing supply chain expenses is to understand cost drivers. Product unit handling configuration is one such driver. It has great savings potential and has not yet been explored by many companies.
Product unit handling configuration refers to the method used to move a product through the supply chain in pallets, cases, or totes, for example. In the fast-moving consumer goods sector, where large product volumes mean that any variable cost reduction can have a major impact on the bottom line, understanding how product unit handling configuration affects supply chain and logistics costs is critical.
How many layers?
In consumer goods, pallets tend to be the primary handling configuration, so that is largely where we focus our efforts. In most companies, packaging engineers—usually with some input from marketing—design the pallet configurations. Engineering focuses on optimizing pallet surface use while balancing physical packaging strength to minimize product damage. However, there are many ways the pallet configuration affects the total supply chain, and the packaging team does not always consider the cost impact of decisions once a product enters the outbound logistics chain.
As an example, say a company has a pallet with four layers and 10 units per layer for a total of 40 units. If engineering adds a layer, making a 50-unit pallet, the following outcomes are likely.
At the manufacturing plant, workers experience reductions in
- pallets needed for the same amount of product
- other packaging supplies, such as shrink-wrap, labels, and corrugated pallet sheets finished goods handling.
- Likewise, at the distribution center, the company sees decreases in labor hours to unload and put away products
- loading moves of pallets into outbound trailers
- product damage due to less overall handling.
Further, case picking—typically a more labor-intensive activity than full pallet picking—might be increased or decreased, depending on customer order patterns. Assuming a decrease, more units per pallet per move leads to fewer replenishment moves and reduced need for supplies. Warehouse space usage also might go down or up depending on storage method.
Inbound to the distribution center, costs will either increase or decrease depending on product type and loading and transportation methods. And outbound from the distribution center, the unit-per-pallet change might be positive, negative, or nonexistent—for instance, if products are shipped less-than-truckload, there would be minimal change.
Examine the costs
Your first step should be to analyze the costs and benefits associated with changing the number of layers per pallet. You can be as conservative or aggressive as you wish when modeling scenarios. For example, for a product with four layers of 10 cases per pallet, analyze what happens when the number of layers becomes three or five. This focus makes it easier to look at a limited number of change scenarios for each stockkeeping unit (SKU), which is advantageous for a project incorporating several hundred SKUs—a common occurrence in the consumer goods industry.
Our company, UTi Worldwide, is a global supply chain and logistics consulting firm performing services including air and ocean freight forwarding, contract logistics, customs brokerage, and more. Recently, we worked with a large commercial cleaning products manufacturer to develop a model that could capture each potential logistics impact and display the results for five scenarios for each SKU, ranging from a two-layer reduction to a three-layer increase. We evaluated about 1,450 SKUs, excluding those with packaging that precluded additional layers, such as drums or totes. This model enabled us to determine the optimal number of layers for each SKU and packaging type based on logistics costs.
In building this model, we settled on the following steps for conducting a layer evaluation project.
1. Define the variables
. These include material costs such as pallets and labor, the labor for essential warehousing tasks, and basic constraints. It’s important to think about the relevant constraints at work. Some of the constraints we imposed included pallet stack height, trailer door height, and total allowable trailer weight. Also bear in mind soft constraints, which should be flagged as problem areas but can be overcome with sufficient will or investment. Our soft constraints included the warehouse rack opening and the rack’s weight capacity.
2. Set the correct scope. Think through all the activities that take place, from the end of the manufacturing line through delivery to the customer. Some are easy to model, while others are more difficult or impossible and should be avoided. You also may want to take a close look at each SKU and eliminate those that can’t feasibly be changed or are too slow-moving to warrant consideration.
3. Clean up the data. Your project model is only as accurate as your data. Reliable productivity studies, accurate samples of historical order data, and precise dimensions and weights for each SKU you wish to model are essential.
4. Determine the right scenarios. You’ll need to know how aggressive you can be in recommending changes, as well as what degree of layer change falls within your risk tolerance.
5. Model future behavior. Many dynamics demand creative model building and assumption making. Some must be tackled on a case-by-case basis, but looking at historical patterns can help. One of the most complex dynamics in our model was determining how the number of layers affected case picking. We wound up looking at a historical sample of orders and judging how pallet layer changes would have affected them then.
6. Prioritize savings. We found that flagging soft constraints early on made it easier to see which cost savings were the low-hanging fruit and which would not be easily attainable, considering the investment required to implement them.
Some factors are not as easy to model and may require expertise from other parts of the organization. For example, we have never been able to discover a simple mathematical function that predicts the cost effects of modifying pallet layers up or down. However, we presented our data to the packaging engineers, who processed them and suggested changes in packaging and pallet layer configurations to accommodate the new units per pallet. They used our concrete data to determine if investing in increased packaging strength would be a feasible way to reduce logistics costs.
After analysis of all our data, we found that only 55 percent of SKUs showed savings for any of the five layer change scenarios. Initially, we estimated total savings at $437,000 annually. Once we applied soft constraints, the amount we saved from just the low-hanging fruit was still a healthy $200,000. A good portion of that amount was represented in a relatively small number of about 10 SKUs.
The whole enterprise
Pallet layer optimization seemingly affects only a few business areas, but this type of project has significant cross-functional potential. Upon completing our analysis, we presented our recommendations to the packaging and manufacturing teams. Additionally, marketing and Customer Relations were brought into the conversation. Ultimately, the project had four distinct analytic phases: logistics, packaging, manufacturing, and customer. We ensured that we listened to and addressed the concerns of all stakeholders. Note that, in order to ensure internal alignment, internal stakeholders were addressed first.
Despite being performed last, customer analysis is vitally important. Marketing and Customer Relations groups must work with the customer base to ensure they can accept pallets in the new configurations and to encourage them to purchase in the new full-pallet quantities. If, for example, the units per pallet are increasing for a fast-moving SKU, it may be necessary to demonstrate how customers will benefit from improved logistics to balance out increases in inventory carrying costs.
In the current economic landscape, costs are constantly put under a microscope. Through examining the traditionally isolated task of pallet configuration design and examining it from a new perspective, we were able to present our organization with big cost reductions—without sacrificing customer value.
Homayoun Taherian is a senior consultant in the supply chain design and innovation group at UTi Worldwide, a global supply chain and logistics consulting firm. He has 10 years of experience in supply chain management and manufacturing across the automotive, chemical, and consumer goods industries. He may be contacted at email@example.com.
Kristen Kravitz, CSCP, is a manager in UTi Worldwide’s supply chain design and innovation group. She has conducted supply chain optimization and strategy projects across a number of industries, including pharmaceuticals, chemicals, mining, defense, and consumer goods. She may be contacted at firstname.lastname@example.org.
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