Many of the world's largest organizations use supply chain modeling software to tackle these challenges. Supply chain modeling enables businesses to consider any eventuality and unlimited potential solutions before committing resources, within the safe confines of their model. Companies can optimize their inventory strategy based on assumptions with regard to demand, costs, lead times and availability.
Most companies have the same objective: improve profitability while keeping inventory levels at a minimum to reduce costs. That delicate balance is difficult to achieve. Too little inventory doesn’t allow you to ramp up production to meet demand for a popular product. Too much inventory results in unnecessary storage costs and perhaps additional waste.
Supply chain officers across the industry were recently surveyed about the biggest barriers to effective inventory management. The number one response was “Can’t optimize network holistically.” When inventory decisions are the last item in the supply chain to be considered, companies aren’t able to make the strategic decisions that allow them to function efficiently.
The second-biggest barrier to effective supply chain management cited was “demand volatility.” It’s a tough question to answer: how can you predict the optimal inventory level when you can’t predict demand?
Virtual supply chain modeling
Fortunately, companies have a tool that allows them to digitally envision supply chain models and see the impact of various inventory levels without experimenting in the costly real world. Models enable companies to have a holistic view of the interdependencies of each variable in the supply chain and provide true data-backed decision support.
The virtual model takes into account all of the considerations necessary for inventory strategy development: manufacturing network, distribution network, transportation network, customer demand, store operations and merchandising. For example:
Many companies find cycle stock to be a missed opportunity. Companies are overlooking money saving adjustments by not considering the impact on cycle stock.
Prebuilding inventory decisions can help alleviate seasonal demand spikes without increasing production capacity.
There are literally hundreds of these “what if” questions to be addressed and all impact inventory.
How does a company tackle the challenge of building an inventory strategy with a dynamic supply chain?
Six steps to better inventory optimization
This six-step process allows you to look ahead to determine which decisions made now can result in competitive advantages. There has always been value in inventory reporting and drill-down queries but they only provide information about how previous decisions impacted the company. True value is derived when you can look forward and anticipate optimization for any “what-if “scenario.
Specification and scoping out the business challenge
Data collection and demand classification
Inventory drivers and root cause analysis
Strategy and policy development: repeatable process defined
Model development and scenario analysis
This iterative process should be run as frequently as needed, as an ongoing business process, not just on an annual or once every several year basis. Infrequent evaluation of the supply chain could result in losses of millions of dollars every year. A word of caution: often people jump first to technology assessment before isolating the business challenge they want to address. This can make for ineffective choices.
Cardinal Health reduced costs
Consider how Cardinal Health was able to reduce inventory levels and transportation costs through modeling various scenarios before implementing a solution that optimized its supply chain structure.
The company had many disparate programs that were analyzing data for transportation, inventory and labor. They knew they would benefit from having all of that data on a common software platform to more easily create models for planning considerations. With so many siloed data systems, it was not uncommon for a supply chain analyst to take three months to develop a model.
Decision making had to be much more responsive to a rapidly changing marketplace.
Cardinal Health deployed the supply chain modeling technology and immediately tackled an important issue: studying the feasibility of combining deliveries to two distribution centers – one in Jacksonville, Mississippi, and the other in Stafford, Texas. They had historically made three replenishment runs each week from their distribution center in Ohio.
The question they asked: could the company make more frequent deliveries using one truck with half a shipment to each facility and still maintain an adequate inventory to meet the demands of local customers?
Using 18 months of operational data at the SKU level they built a model with multiple “what-if” scenarios that gauged the impact of various combo runs affecting transportation, inventory and labor. The resulting model indicated that Cardinal Health could make five combo load runs each week to the distribution centers in Jackson and Stafford instead of the six individual runs they had been doing.
A year after making the switch to double-stop deliveries, Cardinal Health was able to verify a reduction of inventory at one facility totaling $2.8m and $1.9m at the other. That translated into a $430,000 savings with inventory carrying costs. Dropping from six to five weekly deliveries reduced shipping costs by $276,000.
Since undertaking the first project, it has since conducted 20 additional supply chain studies.
Cardinal Health now routinely runs what-if scenarios in days as opposed to the weeks that previous models took. They are now able to fine-tune operations and easily consider the impact of possible changes to the system.
Having proven the cost benefits of supply chain modeling, Cardinal Health has embraced supply chain analysis as an ongoing business process to maintain the efficient distribution of its healthcare products.
What if they had never updated their supply chain modeling solution? They would be functioning at considerably less efficiency and with reduced profitability.
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