DMS? YES! | Global Trade Magazine
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  November 11th, 2017 | Written by




  • We use machine learning and algorithms to understand specific patterns and can adjust supplier orders accordingly.

Dana Incorporated, a supplier of technologies that improve the efficiency and performance of vehicles, was struggling with increasing inventory costs. An analysis showed that the $6.6-billion, Maumee, Ohio-based company was carrying excess inventory in some areas and not enough in others. Dana was also experiencing rising transportation costs, because it often used expedited delivery services to cover inventory shortages.

Dana implemented a demand management solution from One Network to manage inbound supply for Dana’s Commercial Vehicles division, and One Network’s Intelligent Logistics and TMS solution for managing international freight.

We needed to find a way to deliver more value to our customers and trading partners while reducing inventory and operations costs,” explains Tobias Jendrell, Dana’s global director for Materials and Logistics.

A growing number of companies are discovering the benefits of implementing demand management solutions (DMS). DMS endeavors to reach beyond traditional forecasting to sense demand in real time, enabling nimble adjustments of purchases and inventories, thereby lowering costs.

Demand management has been around for well over a decade but has taken off in recent years thanks to the big-data phenomenon—the possibility of storing ever-growing volumes of data cheaply and the advent of processing capabilities that can make sense of it all. That has taken DMS into the realm of autonomously managing companies’ ordering processing and revolutionizing how people do their jobs.

Forecasting involves analyzing historical data to come up with statistics that are used to drive ordering,” says Tina Lalor, a senior business consultant at John Galt Solutions, a DMS provider. “Demand management does some of that but we go beyond forecasting to demand sensing and shaping.”

Demand sensing might involve, for example, gathering point-of-sale data to see what’s going on downstream at the consumer level. Demand shaping involves decisions about pricing and promotion that begin to actually generate future demand.

One Network’s a real-time, cloud-based network solution gathers data from various stakeholders in the supply chain and immediately propagates demand and supply information to enable supply-chain execution.

That allows businesses to run faster and smarter with real-time information,” says Adeel Najmi, senior vice president for Products at One Network. “We connect the dots from sensing demand, by listening to consumer trends and patterns, and then shaping demand and orchestrating the outbound side to be responsive to what we see in the marketplace.”

One Network’s Intelligent Supply solution automatically takes a gross demand feed from Dana’s many manufacturing resource planning (MRP) systems and creates orders for forecasted and actual demand. It also allows Dana a real-time view of their customers’ requirements and their suppliers’ material availability across a multi-tier supply network, including third party warehouses holding inventory on behalf of their suppliers. The system enables Dana to collaborate with suppliers on orders, and to resolve issues quickly. The logistics management piece is being used by Dana to manage international shipments.

The system is designed to do autonomous decision making, as opposed to one-off planning,” Najmi explains. “What typically happens is that the customer will initially run the system in a decision-support mode which provides a forecast and suggests what the company should order. Once they feel comfortable with the system they simply let it run on its own.” That means that the system is automatically and autonomously generating orders to suppliers without human intervention.

One Network worked with Dana to understand the company’s business and ensure the solution would meet and exceed requirements, and be easy to use for their employees and their trading partners. “One thing that separates One Network is their ability to adapt the network service quickly to get us the solution we need,” says Jendrell. “We have seen dramatic inventory reductions, on average more than triple what we had targeted.”

Dana’s target of an overall 10 percent reduction in inventory was exceeded with a 31 percent average reduction overall. Inventory levels of some parts were reduced by as much as 68 percent; a minimum reduction of 10 percent was achieved across the board.

One factor that enters into these kinds of results is the ability of the system to keep tabs on the supply chain on an hour-to-hour basis if necessary. “We use machine learning and algorithms to understand specific patterns and can adjust supplier orders accordingly,” says Najmi. “The information is propagated up and down the supply chain dynamically and without human intervention.”

Along with the inventory reductions, Dana saw a dramatic drop in shortages and the costs of expedited deliveries by having the right inventory at the right place at the right time. Dana also benefited from the synchronization of its multiple MRP systems and improved on-time delivery performance.

The demand shaping aspect of demand management involves influencing what customers are going to buy in the future. If you’re out of red widgets and want to move blue widgets, pricing, promotions, and marketing techniques can influence demand. “Store displays, advertising, all of these things shape demand,” Lalor points out.

John Galt is a provider of demand management and inventory management solutions that combine software and consulting services to its customers. “Our customers are planning not for the next month but for the next 18 to 24 months,” says Lalor. “They are planning their businesses to make decisions on capital expenditures about, for example, expanding manufacturing capacity. They also use our system to optimize production and pricing by analyzing how much they can sell at different price points.”

Big data capabilities will continue to shape the future of demand management, according to Lalor. She believes advanced machine learning—approaching artificial intelligence—will come into play to provide risk and probabilistic analyses.

These technologies are definitely on their way. The question remains how agile companies and the people who work for them will be to adapt to a new reality that revolutionizes how companies run their ordering processes and how people do their jobs. “Some people are still sitting there using spreadsheets,” she says. “The ordering business process needs to change with the technology. The biggest challenge will be getting the technology married to people and processes.”


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