Efficiency or responsiveness? How do you balance your ATM cash supply strategy?
Sept. 3, 2014
by Neringa Murauskiene, marketing manager, Fobiss
The primary purpose for the existence of any supply chain is to satisfy customer needs and generate business profits — the cash supply chain is no an exception to this rule.
Businesses manage their ATM networks by providing cash where and when customers want it. But they must provide this service efficiently if they are to make a profit. An effectively managed cash supply chain must be efficient and responsive at the same time.
Responsiveness can be defined as the ability of the supply chain to respond purposefully and within an appropriate timeframe to customer requests or changes in the marketplace.
In contrast, a supply chain can be considered to be efficient if the focus is on cost reduction and no resources are wasted on non-value added activities. Balancing the conflict between these two dimensions is where the real challenges lie.
Decision-makers in ATM supply chains face conflicting priorities that involve a trade-off between efficiency and responsiveness. The technological tools that have been available on the market usually focus on solving problems to do with either efficiency or responsiveness.
For instance, enterprise resource planning applications predominately support efficiency, while various process automation applications support responsiveness. Depending on its business strategy, a company chooses to emphasize either efficiency or responsiveness as the main way to differentiate itself from competitors.
But if the organization manages to improve both, it can create a sustainable competitive advantage. It is possible to achieve this goal by implementing an intelligent business operations working style.
Intelligent business operations tools have proven effective in improving business processes. These tools provide visibility, situational awareness and flexibility for those who manage cash supply chain operations and make the decisions on a daily basis.
Intelligent business operations addresses the task of connecting internal and external business information systems within a common environment where data is analyzed and made available to operations managers in real time. IBO also serves as a work method for integrating real-time analytic and decision-management technologies into a company's operational activities and processes.
Intelligent business operations and business intelligence should not be compared; these methods do not perform the same function, but rather, complement one another.
The main aim of BI systems is to gather and analyze historical data for the past period. Meanwhile IBO compares historical and real-time data, determines trends, links actions, and makes projections for the future.
IBO also automates the process of making optimal decisions. Here, forecasted demand, existing and
planned limitations and outcomes are applied in real-time to determine the most cost-efficient combination of cash quantity, denomination, delivery time and route, among other factors.
Graphical presentations of information, customization of the user interface in accordance with functions performed, contingency notifications and decision-simulation tools give decision-makers full visibility into a situation, allowing them to respond rapidly and flexibly to real-time changes.
In the meantime, managers can carry out real-time monitoring and evaluations of key performance indicators, track the implementation of strategic goals, and keep up to date with tactical actions proposed by the system.
The IBO platform uses integrated artificial intelligence algorithms to carry out the complex tasks of cash-demand forecasting and optimization. These algorithms are capable of processing large amounts of information in real time, evaluating patterns, relationships, trends and projections, and then choosing the optimum actions from millions of possible combinations — something the human brain and standard analytical tools simply cannot accomplish.
However, it is important not to underestimate the importance of human input in the process of algorithm training. The system does not answer the question “why”; it simply evaluates causes based on insights provided by expert staff.
For example, while analyzing an error in system forecasting, an employee notices that this might have been influenced by an unusual or anomalous event that occurred in a particular region.
The algorithm receives this information and re-evaluates the relationships, thereby learning to make even more precise projections. Such a work method can eliminate the risk of losing know-how in a staffing change. It can also prevent an "information gap" should an employee in Region A be called upon to fill in for an employee in Region B, due to a vacation, sick leave or other unexpected absence.
The precision of these complex algorithms can be demonstrated and quantified. In studies and pilot projects, Fobiss found that a network of six thousand ATMs can save 22 percent in logistics costs, 17 percent in money-handling costs and up to 55 percent of interest-related costs. Expressed in monetary terms, the ATM deployer saves 11 million euros ($14.5 million) per year.
The key is choosing technology that supports high levels of both responsiveness and efficiency. Such a solution will incorporate demand-management tools, optimization engines, complex event processing capability, role-based dashboards, decision management technologies, performance measurement analytics and, most importantly, real- or nearly real-time data.
Fobiss BV (www.Fobiss.com) helps companies in the financial and retail industries manage supply chain operations through integrated real-time analytics, as well as decision management and artificial intelligence technologies.Source: www.atmmarketplace.com