From long-term, strategic decision-making to tactical production planning, supply chain modeling technology can enable food and beverage companies to reduce cost and risk and edge out the competition.
Supply chain and transportation network design technology is enabling global food and
beverage companies to model, optimize and simulate supply chain and transportation
network operations and right-size inventory levels, leading to major improvements in
cost, service, sustainability and risk mitigation. From long-term and strategic initiatives
to short-term and tactical planning, here are nine ways food and beverage companies
around the world are using modeling technology to tackle industry challenges and drive
1. Manufacturing Strategy
2. Merger and Acquisition Strategy and Execution
3. Cost-to-Serve Optimization
4. Production Capacity Planning
5. Inventory Optimization
6. Ensuring Product Freshness
7. Transportation Route Optimization
8. Sales, Inventory and Operations Planning
9. Risk Management
1. Manufacturing Strategy: What Should We Make and Where?
One of the most important decisions food and beverage companies face occurs prior to any decisions about how they will make and distribute their products: should they make those products at all? This question is often referred to as the “make vs. buy” decision. A second critical decision in this stage is where to make things. This challenge is often referred to as the “off-shore vs. near-shore” or “low-cost vs. local” question. In making these fundamental decisions, companies often fall into the trap of focusing only on the production costs or investment costs and forget to focus on the entire end-to-end supply chain, which includes the interdependencies of many cost factors including transporta- tion, inventory, raw material costs and tax. Modeling technology can help companies identify the tradeoffs across all the different cost elements to develop the most cost effective way to serve customers. This also provides data to support decisions such as how much material to buy and when, hedging strategies and pre-build considerations.
Case Example: Make vs. Buy
Challenge: A major global beverage retailer was planning to introduce a new product line. The big question was, should they produce the new product in their own facility or use a contract manufacturer? There was considerable risk involved in the launch. The new product was untested; it was uncertain whether it would be successful or not.
Solution & Results: In order to determine the optimal strategy, the company, taking into consideration the demand forecast, looked at the cost structure over three, five and 10 years of investing in the capital required to make the new product compared to outsourcing production. Outsourcing would be costlier on a per-unit basis, but given the risk and up-front investment cost required to produce the product themselves, this alternative may be better in the short term. Once the initial model was built, the company did sensitivity analysis around fluctuations in demand to determine the best production decision at varying demand levels.
2. Merger and Acquisition Strategy: Integrating New Supply Chains into an Existing Network
Mergers and acquisitions (M&A) present an incomparable number of options for the design of the new organization’s supply chain; a staggering percentage of initiatives fail to meet ex- ecutive and shareholder expectations. Supply chain design technology enables companies to model their supply chains, evaluate alternatives, optimize the structure and simulate multiple scenarios in order to predict the resulting operational performance of the merged organi- zations. There are ways to apply supply chain modeling at each stage of the M&A process: pre-merger, post-merger and divestiture/spin-off situations. Supply chain design technology enables companies to build models that include all of the potential inter-related operations and incorporate time and variability in order to identify the best M&A opportunities and the resulting strategy for combining the organizations. This includes identifying the optimal footprint (i.e. number and location of facilities, production capacity, suppliers, transportation assets, etc.) and capitalizing on economies of scale, such as combining customer deliveries for fuller truckloads.
Case Example: Making Data-Driven Site Selection Decisions
Challenge: One of North America’s largest packaged foods producers acquired a major frozen food manufacturer. With fuel costs on the rise, the company needed to re-evaluate their inbound and outbound distribution strategies given the expanded network. Specifically, which DCs should service which customers and what was the optimal quantity and location for DCs and what the fuel cost ‘tipping point’ would be where the optimal network would not be advantageous.
Solution: A network optimization model was used to establish and validate the baseline merged supply chain, determine the optimal network configuration and conduct sensitivity analysis.
Results: The analysis showed that savings could be achieved by simply realigning which
customers were sourced by which DCs, eliminating redundant shipments coming from multiple sources and reducing expensive cross country shipments. Because of the high construction cost and costs associated with closing an existing distribution center, Memphis was not a good option to build a new distribution center. The most significant savings could be found by expanding the existing Southeast warehouse and moving the West distribution center east out of California.
3. Using Cost-to-Serve Optimization for Pricing Strategies and Margin Analysis
Cost-to-serve is the analysis and quantification of all supply chain activities and costs incurred
to fulfill a customer’s product demand. This is accomplished by modeling all the supply chain
activities in the network, and properly allocating fixed and variable costs. An accurate cost-to-
serve model enables informed decision making by answering questions such as:
Top image shows a dashboard comparing baseline to optimized profit, landed
costs and margin; bottom image shows compar- ison of baseline to optimized
customer lanes. Green lines are profitable, red are not
- Is this customer or customer segment profitable, given the supply chain configuration and costs?
- Does it make good business sense to continue to stock and distribute this product?
- What is the right amount to charge to at least cover my costs?
- How does my cost-to-serve change when I make a network change such as adding a new DC, changing a customer’s sourcing or adding a new supplier?
- How does my margin change in this new network structure?
- How should I price a specific product for a specific customer?
These models provide fixed cost structures for facilities, processes, process steps, labor, equipment and transportation assets and also account for variable costs throughout the entire supply chain. Food and beverage costs included in this analysis are: raw materials sourcing cost (purchase, transport, store), variable production cost (labor, utilities), fixed production costs (production lines, facilities), transportation costs, inventory costs, DC costs and taxes.
4. Production Capacity Planning: Utilize Existing Capacity for Lowest Total Cost
Network optimization can also perform more detailed production modeling to right-size the production footprint as well as to optimize production capacities—globally, or with drill-down detail for individual facilities. Depending on corporate goals, companies may choose to optimize for maximum utilization or maximum profit. Models can incorporate all production-related details, including production lines, labor resources, production processes within the plant and for each line, bills of material and converting semi-finished goods to finished goods, throughput rates, costs and yields. Modeling results can also determine lot sizing, labor shift allocation, working hours or best utilization of changeovers.
Case Example: Product Allocation
Challenge: 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 fluctuates to different regions of the U.S. and the company wanted to evaluate the impact of shifting locations from which raw materials were sourced in order to provide a lower total cost. For example, if the company has 10 plants where a certain kind of soup is made, where and in what quantities should the soup be made, based on current raw material sourcing costs, transportation and facility costs?
Solution and Results: By utilizing capacity modeling to simply balance variables and capacity, the company uncovered $50 million in cost savings in just one year, without any changes to the physical production footprint.
5. Inventory Optimization
Multi-echelon inventory optimization enables food and beverage companies to create accurate models across all echelons of the supply chain, with a full inventory plan that includes optimal safety stock, cycle stock and pre-build inventory, as well as strategies for promotional products. Companies can also use supply chain simulation to predict the service rates, inventory levels and site capacity constraints for any potential supply chain structure. Inventory optimization allows companies to answer questions such as:
- How much inventory do we need to meet our service requirements?
- Where should we stock product?
- When and how frequently should we replenish?
- How do we buffer against risk factors such as lead time variability and demand uncertainty?
- How frequently and in what lot sizes should we produce?
- What is the optimal transportation mode to use to properly balance working capital?
- How should we plan our inventory to accommodate for seasonality and/or production constraints?
- How can we take advantage of volume discounts?
- What is the space requirement for a given facility?
Using supply chain modeling for inventory optimization enables:
- Automatic demand analysis and classification to identify different buying behaviors and unique variability traits
- True multi-echelon inventory right-sizing for all product types, including fast movers, slow movers, intermittent demand, etc.
- The optimal mix of postponement versus finished goods inventory placement
- The ability to simulate any inventory strategy to test service level performance and capacity utilization