Like finding unexpected money in a coat pocket, modeling technology can reveal numerous cost savings opportunities hiding just beneath the surface.
There’s nothing better than putting on an old coat and finding money in the pocket. The money was right there, but hidden just below the surface of that coat pocket.
This “found money” has three attributes that make it so great:
It’s Fast – You just reached into your pocket and there it was in your hand.
It’s Easy – You didn’t work long hours, toiling and sweating to earn that money.
It’s Unexpected – You hadn’t planned on having that extra cash when you started the day.
Much like that money in your coat, there are numerous supply chain cost saving opportunities that are hidden just below the surface at most companies, and today’s savvy professionals are using supply chain modeling technology to uncover that money.
This white paper will use real-life examples to explore four modeling techniques used by industry leaders to “find money” in the supply chain. These techniques include:
- Product flow-path optimization
- Demand segmentation and inventory right-sizing
- Production footprint analysis
- Transportation route optimization
Each of these techniques can deliver the same fast, easy and unexpected benefits. They are fast in that they do not require years to implement. They are easy because they do not require major structural changes to the supply chain. Finally, they are unexpected because modeling technology often uncovers solutions that contradict intuition or legacy business practice.
1. Product Flow-Path Optimization
The process of moving your products from supply through production and eventually distributing them out to customers or stores presents a myriad of choices.
The collective set of these choices make up a product’s flow-path through the supply chain. Modeling these flows can provide you with a total landed cost or total cost-to-serve for each product. Modeling all the alternative flow options and using smart algorithms to determine the best choice is called product flow-path optimization.
Case Example: Inbound Consolidation
A consumer goods manufacturer operated five production plants across the eastern half of the country. A modeling analysis showed that there were nearly 400 items from 25 unique suppliers that were sourced across all five production plants. In an effort to reduce the total supply chain costs including sourcing, production, warehousing and transportation, the company analyzed the effects of using one or more plants as an inbound consolidation center. The results showed that for 12 suppliers and nearly 300 items, it was more cost effective to purchase product from a single plant to receive higher piece-price discounts, even though there were added handling costs and extra transportation runs. These minor product flow changes resulted in millions of dollars in yearly cost savings.
Modeling analysis showed that it was more cost effective overall to purchase product from a single plant rather than across all five, despite the additional transportation cost.
Case Example: Port Selection & Rebalancing
A retailer with 260 store locations and a strong e-commerce business was flowing products into their market through two ports, one on the east coast and one on the west coast. From the ports, they utilized a combination of rail, LTL and FTL for delivery to four main DCs, 71 hubs, company stores and consumers. Using data including store demand, store sites, DC and hub locations, supplier and port information and average shipment weights and cubes, the company modeled the flow of each product to determine which product should flow through which port and in what quantity. The optimization project recommended a shift of 20 percent product volume to the east coast port which netted annual total supply chain savings of seven percent and simultaneously improved DC, store, hub and consumer delivery times.
2. Demand Segmentation & Inventory Right-Sizing
One of the biggest sources of variability in the supply chain is demand, and demand can be highly unpredictable. Despite the fact that there are many widely-varying demand patterns, most inventory optimization tools assume that all demand is “normal”, leading to either too much inventory or stock-outs and lost sales. Multi-echelon inventory optimization determines how much inventory must be kept at each level and location in the supply chain to deliver the desired service level at the lowest cost. This analysis includes the inherent supply chain variability on both the demand side and the supply side to identify the lowest total cost inventory stocking solution that meets the service requirements for each product/site combination.
Case Example: Demand Segmentation
An automotive manufacturer has over 20 regional DCs to supply service parts to their dealer and repair part network of over 2,000 locations. The number of parts supplied throughout this network was over 120,000, with widely varying demand behaviors. The company used demand segmentation technology to analyze and automatically classify the demand patterns into 10 unique categories such as smooth, erratic, lumpy, unit-sized, etc. They then applied the proper inventory policies to recommend the appropriate stocking levels required to achieve their service level targets. The result was a total on-hand inventory reduction of nearly 20 percent, and better-fitting policies for the items with irregular demand.
Case Example: Inventory Optimization
A grocery store chain has seven regional distribution centers that stock product for, and deliver to, over 500 stores throughout the country. The top 2,500 SKUs are stocked at all DCs. These represent more than 70 percent of the overall sales volume. The company established seven service-level categories between 85 percent and 99 percent, based on product characteristics. Multi-echelon inventory optimization analyzed the demand and lead-time variability for each product/site combination and recommended a $5M reduction in overall inventory, even though numerous locations required a higher level of inventory. In specific cases, inventory for a product was increased in three or four sites and decreased in others. The result was the “right-size” inventory for the organization. Savings are achieved by actualizing the lowest total landed-costs and not incurring excessive supply chain costs due to buying improper quantities.
Image: Seven service-level categories with historical inventory stocking level (depicted by the orange bar), the newly-calculated and optimized stocking level (depicted by the green bar), and the difference—either positive or negative (depicted by the blue bar). Blue bars extending below the line imply a reduction in inventory and those extending above the line imply an increase in inventory level is optimum. The length of the bars depicts the inventory level in millions of dollars.
3. Production Footprint Analysis
Put simply, the “footprint” represents the physical facility and quantity in which each product is manufactured, along with the capacity required to make it happen. Oftentimes demand for products shifts over time to new regions or different quantities, and suppliers and cost structures change as well. As these changes occur, the production footprint should also change to keep in-sync. This may mean investing in additional capacity in certain locations or perhaps completely moving production capacity to other facilities within the network. Modeling the production footprint and analyzing varying scenarios helps a company balance existing capacity with the investment required to add additional production.
Case Example: Product Placement and Balancing
A large food manufacturer had already made investment decisions around facility locations and production footprint. The next question was how best to utilize that footprint. Over time, demand for its product evolved and migrated to different geographical regions and the company wanted to evaluate the impact of shifting locations from which raw materials were source in order to provide a lower total cost. For example, if the company has 10 plants where a certain kind of product is made, where and in what quantities should the product be made, based on current raw material sourcing costs, transportation and facility costs? By utilizing production modeling to simply balance variables and capacity, the company uncovered over $50 million in cost savings in just one year, without any changes to the physical production footprint.
Case Example: Distribution Capacity Planning
A global apparel manufacturer has a multi-million square foot warehouse in northern Europe utilizing automated conveyors and high-bay storage systems. Even with all their sophisticated automation equipment, the company was experiencing significant capacity shortages and throughput issues. To address the issue, they created a multi-time period model to identify capacity bottlenecks within the DC and to determine the right staffing levels. Optimization was used to propose actions to level the workload by bringing shipments forward, delaying or re-routing them to direct delivery. A short-term version of the system considers requirements week by week, while a long-term version looks forward over two years. The new system replaced a host of spread sheets and enabled more accurate matching of capacity to requirements, thereby reducing costs.
A multi-time period model identifies capacity bottlenecks and proposes actions to level the workload.
4. Transportation Route Optimization
Transportation route optimization can be done alone or in conjunction with either supply chain optimization or simulation. Using advanced algorithms, transportation routes are defined to minimize the cost of inbound or outbound shipments, while considering realistic cost and constraint structures. This helps answer the questions, “What’s going to happen to our transportation routes when the network design is changed?” or “Could there be a more efficient way to get our product from the manufacturer to the customer?”
Case Example: Vehicle Route Optimization
A global convenience store chain with 300 locations in a major metropolitan area had been using a legacy spreadsheet tool for route design. By graduating to modern transportation route design technology, the company was able optimize its routes to minimize route costs. Testing a series of truck options and varying service levels, it found an optimal combination that reduced the number or routes, trucks used and miles driven but maintained target service levels. The new routes would generate cost savings of 8.9 percent of total outbound transportation cost, as well as reduce emissions in a high-smog area of the U.S.
Case Example: Multi-Stop Route Design
A major provider of fresh and frozen seafood products was considering changes to its distribution strategy, which previously was managed completely by their 3PL. Using transportation network design technology, the company developed optimal multi-stop outbound distribution routes to deliver frozen seafood from 20+ regional warehouses to more than 1,800 customers throughout the US. They used a less-than-truckload comparison capability to determine the optimal proportion of multi-stop routes and less-than-truckload shipments (for small and/or far-flung shipments) for the company’s revised strategy. Identified savings from the project were nearly 20 percent of outbound transportation cost.
Uncovering Your Own “Found Money”
Modeling technology can uncover millions of dollars in supply chain savings just by optimizing existing assets and processes. Oftentimes, simply visualizing existing supply chain structure and flows can reveal hidden inefficiencies and present opportunities for further analysis and optimization.
Many of the world’s largest and most successful companies are continuously redesigning and improving their supply chains. They often begin by uncovering cost-saving “quick win” projects, and then examining how their supply chain will perform under a wide range of market conditions and assumptions, analyzing the trade-offs between cost, service and risk.
So the question is: how much “hidden” money might be found in your company’s supply chain?