Other Voices: Solving the Emerging Market Packaging Puzzle
May 17, 2014
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A lot of research has been devoted to packaging optimization problems. However, there is one specific question that has received relatively little attention: how do companies define the optimal quantity of inner packs to be placed inside outer packs to minimize overall logistics costs?
Picture, for example, a pallet loaded with pharmaceutical product. There is a stack of boxes or outer packs, and within each box there are bottles of pills, the inner packs. The pills are referred to as consumption units.
How many bottles of pills (inner packs) should be carried in each box (outer packs) and how big should the cartons be in order to keep end-to-end logistics costs down to a minimum?
Now, envision the pallets of drugs in an emerging market where there are different types of customers. In wealthier districts supermarkets derive economies of scale from bulk deliveries. At the other end of socio-economic scale are mom-and -pop retailers in poor neighborhoods that serve customers who buy in smaller quantities. These micro stores require deliveries to be broken down into individual bottles. The small stores might even sell product by the consumption unit. In addition, there are institutional customers such as clinics that require a variation on these demands.
Which packaging configuration serves all these needs of these customers at the lowest overall logistics cost?
It’s a complex question, especially when each component cost is taken into account. The cost of handling outer packs at the production plant, distribution centers and retail stores, inner pack handling at DCs, transportation costs, and the cost of opening outer or inner packs at DCs and retail outlets, are examples of these individual costs. Various inventory carrying costs associated with packaging also have to be allowed for.
The CLI research team worked with a Colombian company in the non-perishable foods business to develop optimization models that help companies to solve this puzzle. The food company needed to find ways to lower the cost and improve the efficiency of its DC operations. For example, in order to meet the demands of small stores the enterprise has to repack boxes of product with too few inner packs. The shortfall makes it difficult to maximize the number of items on each pallet.
First, the CLI team mapped the physical flow of packages across the company’s value chain. The aim was to gain an understanding of the processes that are influenced by package size.
Armed with this information, the researchers built the first optimization model. The model defines the size of the outer package (in terms of the number of inner packs it contains) that minimizes the total cost of the processes identified in the initial phase of the project.
Next, they developed Model Two. This model identifies five types of outer packs (again, in terms of the quantity of inner packs housed) that minimize the number of outer packs that must be opened in order to meet customer demand.
The researchers then gathered the data needed to run the two models. The inputs for Model One, for example, included historical demand data for the selected products and related packaging/inventory costs.
Six packaging scenarios were evaluated in terms of their overall cost compared to current logistics costs. One scenario defined an outer pack size (in terms of the quantity of inner packs it contained) for each of the company’s distribution channels. The analysis also looked at the outcomes of specifying one, two, three, four, or five outer pack sizes to meet all customer demands in every channel.
All of the six scenarios tested achieved a reduction of least 4.5% in total logistics costs. The most striking savings were associated with the transportation of finished goods to end customers and the handling of inner packs in DCs.
On a broader level, the project highlighted how changes in packaging size can impact the following key areas of the value chain.
• Costs associated with a number of activities including the procurement of packaging and other materials, transportation, inventory management, and the handling of outer packs.
• The productivity of production lines and individual operators
• Space utilization in storage facilities and delivery vehicles.
The optimization models help companies to identify the most cost-effective packaging configurations for emerging markets where there are unique logistics challenges. For example, companies must deal with multiple distribution channels and the requirements disparate groups of customers.
The next phase in the research will look at consumption units; the optimum number of items to include in shipment inner packs.