Companies have developed a network of market-facing distribution centers (DCs) to meet customers’ requirements for quick, consolidated delivery. For example, in the consumer products industry, this network of warehouses will be supplied by many plants and copackers, each producing various products in different places. The lead time to produce is far longer than the customers’ expectations when they place and receive the order. The challenge is to have the right product in the market-facing DC before the customer order arrives to ensure that when the order comes, it can be shipped complete and on time. This is achieved through inventory set by a demand-planning system (also known as replenishment planning or the distribution requirements planning-DRP system). 

Read also: Automation Effect on the Delivery Process

What most people don’t realize is …

Most supply-planning systems can inflict cost and, in some cases, even position things to prevent the DC from shipping in full and on time. Here is why: Supply-planning systems don’t consider if the supply chain–carriers and warehouses–can move all the products it wants to deploy. They assume infinite:

• carrier capacity with the same level of service and cost
• capacity for all the facilities to ship and receive
• space in each facility.

All these assumptions are wrong. This leads to a deployment signal that can violently change day-to-day and needs to consider cost, storage space availability, and throughput capabilities. On one lane, we saw 24 trucks deployed one day and three the next. How is any transportation manager supposed to deliver cost-effective service with that level of variability? And, when the 24 loads arrive at the receiving location, even assuming that the shipping location can pull together enough trailers and people to load them, they are faced with dilemmas: 

• Many vans are waiting to unload.
• How do we explain all the detention and overtime expense?
• Which shipment should we bring in first?

The implication of leaving the products on trailers is that they may be needed for immediate customer orders. The result is often a service failure.

SIMPLE MANUAL SOLUTIONS MAY HURT 

A simple approach may be to set some boundaries on each lane. For example, limit the lane to between five and 10 trucks. However, this needs to include the total picture. What if there are urgent customer requirements? Is it better to save a few dollars in freight while paying customer fines for poor customer service than spending extra freight dollars? 

Instead, there needs to be a tradeoff and understanding of the right balance of cost vs. service. Also, what if there is limited origin-site shipping capacity and another lane urgently needs that limited shipping capacity? In the CPG world, where moves between sites are in full truckloads, it’s essential to understand what is happening in each vehicle. So, it is challenging to ascertain whether the “urgency” of need on lane “A” is more critical than the needed products that will ship on lane “B.”

THE SOLUTION HAS TO BE HOLISTIC AND AUTOMATED

Any solution must encompass the whole network; otherwise, it is just like squeezing the proverbial balloon: fix something in one place, and it pops out somewhere else. Add to this a variety of deployment shipment lead times and the complexities of shipping only full-truckload among facilities, which means any solution must be automated. And yes, with new technology, it can be done.

Because the supply planning solution suggests a significant number of requirements–in many instances, more than can be shipped in a capacity-constrained world–what goes on with a limited number of trucks must be prioritized. This is not a trivial problem, so it must be automated. Such automation needs to build shipments to maximize payload and ensure that the most urgent product is loaded and arrives damage-free.  

IT MAKES LIFE BETTER

Making optimized tradeoffs across the whole network generates many vital benefits: The most needed shipments are prioritized, enhancing customer service. At the same time, as cost and capacity are considered, the operational cost is minimized using a uniform set of tradeoffs. This uniformity is another benefit of automation.

Automation of the process of modifying supply-planning solutions to consider real-world constraints and optimally building loads is a major win-win-win.

• Carriers win because they see significantly less volatility and can operate more efficiently.
• Shippers win because they can now fulfill orders more completely and at a lower cost.
• The environment wins as carriers travel fewer deadhead miles, and load optimization generates fewer trucks, reducing carbon emissions.

Author Bio

Thomas A. Moore is the founder and CEO of ProvisionAI, the only provider of a patented optimized replenishment transportation scheduling solution. He has founded multiple successful supply chain software companies. Working with industry leaders such as Procter & Gamble, Unilever, Nestle and Kimberly-Clark, he has led the creation of warehousing, truck loading, and network optimization solutions like AutoScheduler, AutoO2 and LevelLoad. Mr. Moore has also held line positions in manufacturing, warehousing, and trucking operations.