Integrated Industrial Solutions
Our Comprehensive S2P Services
CFOs face challenges with their S2P transformation initiatives. These
challenges typically fall into the three categories of people, process and technology. Our comprehensive S2P outsourcing services address each of these categories.
challenges typically fall into the three categories of people, process and technology. Our comprehensive S2P outsourcing services address each of these categories.
PROVIDING THE RIGHT PEOPLE
To succeed in the S2P arena, business leaders must attract and retain talented individuals who have the skills to deliver top performance. Backed by years of experience as an outsourcing provider, IISC has the capability to relieve you of the burden of recruiting, managing and retaining top talent to support your S2P transformation initiatives to secure the right people who can drive high-quality results in three key areas: strategic sourcing and procurement, accounts payable and working capital management.
To succeed in the S2P arena, business leaders must attract and retain talented individuals who have the skills to deliver top performance. Backed by years of experience as an outsourcing provider, IISC has the capability to relieve you of the burden of recruiting, managing and retaining top talent to support your S2P transformation initiatives to secure the right people who can drive high-quality results in three key areas: strategic sourcing and procurement, accounts payable and working capital management.
LEVERAGING THE RIGHT PROCESS
We can help your organization reach its S2P goals by leveraging our approach to process design and implementation. IISC’s methodology, calculated to help our clients achieve operational excellence, spans five elements:
• Customized Solutions
One way we deliver operational excellence is by providing a greater
level of customization than competitors, integrating more tightly with your culture and technology when implementing an S2P solution.
One way we deliver operational excellence is by providing a greater
level of customization than competitors, integrating more tightly with your culture and technology when implementing an S2P solution.
• BPO Value Cycle
Our approach to delivering operational excellence spans five steps
along the Business Process Outsourcing value cycle: assess and analyze, design process, transition, manage operations and improve
and control. On this path you journey from the current state of your
S2P process to a desired future state, which includes continuously
improving your S2P workflow.
Our approach to delivering operational excellence spans five steps
along the Business Process Outsourcing value cycle: assess and analyze, design process, transition, manage operations and improve
and control. On this path you journey from the current state of your
S2P process to a desired future state, which includes continuously
improving your S2P workflow.
• Quality Management
Our specialists have completed Six Sigma training. These professionals monitor outsourced S2P processes in order to help you improve operational efficiency by uncovering and eliminating inefficient practices as well as continuously improving performance.
Our specialists have completed Six Sigma training. These professionals monitor outsourced S2P processes in order to help you improve operational efficiency by uncovering and eliminating inefficient practices as well as continuously improving performance.
• Integrated Service Delivery
Our integrated global service delivery model enables you to run your S2P processes more efficiently. We can deliver services onsite at your location; offsite in our U.S. business processing centers; and offshore via our Asia-based operations, leveraging different time zones for faster cycle time and lower cost.
Our integrated global service delivery model enables you to run your S2P processes more efficiently. We can deliver services onsite at your location; offsite in our U.S. business processing centers; and offshore via our Asia-based operations, leveraging different time zones for faster cycle time and lower cost.
• Transition Methodology
When we begin a transition, our goal is that every person in your
organization notices a positive difference from day one. Our ability
to meet this objective is based on successfully implementing
comprehensive transition plans at numerous client installations.
When we begin a transition, our goal is that every person in your
organization notices a positive difference from day one. Our ability
to meet this objective is based on successfully implementing
comprehensive transition plans at numerous client installations.
Supply Chain have swept into every industry and business function and are now an important factor of production, alongside labor and capital. There are five broad ways in which using big data can create value. First, big data can unlock significant value by making information transparent and usable at much higher frequency. Second, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time.
Where digital natives were built for analytics, legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics.
The first challenge is incorporating data and analytics into a core strategic vision. The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. All these aspects of transformation need to come together to realize the full potential of data and analytics. The challenges incumbents face in pulling this off are precisely why much of the value we highlighted in 2011 is still unclaimed.
The urgency for incumbents is growing, since leaders are staking out large advantages, and hesitating increases the risk of being disrupted. Disruption is already happening, and it takes multiple forms. Introducing new types of data sets (“orthogonal data”) can confer a competitive advantage, for instance, while massive integration capabilities can break through organizational silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets.
Leading companies are using their capabilities not only to improve their core operations but also to launch entirely new business models. The network effects of digital platforms are creating a winner-take-most situation in some markets.
The leading firms have remarkably deep analytical talent taking on various problems—and they are actively looking for ways to enter other industries. These companies can take advantage of their scale and data insights to add new business lines, and those expansions are increasingly blurring traditional sector boundaries.
Where digital natives were built for analytics, legacy companies have to do the hard work of overhauling or changing existing systems. Adapting to an era of data-driven decision making is not always a simple proposition. Some companies have invested heavily in technology but have not yet changed their organizations so they can make the most of these investments. Many are struggling to develop the talent, business processes, and organizational muscle to capture real value from analytics.
The first challenge is incorporating data and analytics into a core strategic vision. The next step is developing the right business processes and building capabilities, including both data infrastructure and talent. It is not enough simply to layer powerful technology systems on top of existing business operations. All these aspects of transformation need to come together to realize the full potential of data and analytics. The challenges incumbents face in pulling this off are precisely why much of the value we highlighted in 2011 is still unclaimed.
The urgency for incumbents is growing, since leaders are staking out large advantages, and hesitating increases the risk of being disrupted. Disruption is already happening, and it takes multiple forms. Introducing new types of data sets (“orthogonal data”) can confer a competitive advantage, for instance, while massive integration capabilities can break through organizational silos, enabling new insights and models. Hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets.
Granular data can be used to personalize products and services—including, most intriguingly, healthcare. New analytical techniques can fuel discovery and innovation. Above all, businesses no longer have to go on gut instinct; they can use data and analytics to make faster decisions and more accurate forecasts supported by a mountain of evidence.
The next generation of tools could unleash even bigger changes. New machine-learning and deep-learning capabilities have an enormous variety of applications that stretch into many sectors of the economy. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories.
These technologies could generate productivity gains and an improved quality of life, but they carry the risk of causing job losses and dislocations. Previous MGI research found that 45 percent of work activities could be automated using current technologies; some 80 percent of that is attributable to existing machine-learning capabilities. Breakthroughs in natural-language processing could expand that impact. Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass—and as machines gain unprecedented capabilities to solve problems and understand language. Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.
The next generation of tools could unleash even bigger changes. New machine-learning and deep-learning capabilities have an enormous variety of applications that stretch into many sectors of the economy. Systems enabled by machine learning can provide customer service, manage logistics, analyze medical records, or even write news stories.
These technologies could generate productivity gains and an improved quality of life, but they carry the risk of causing job losses and dislocations. Previous MGI research found that 45 percent of work activities could be automated using current technologies; some 80 percent of that is attributable to existing machine-learning capabilities. Breakthroughs in natural-language processing could expand that impact. Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass—and as machines gain unprecedented capabilities to solve problems and understand language. Organizations that can harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.
The use of big data will become a key basis of competition and growth for individual firms. From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information. Indeed, we found early examples of such use of data in every sector we examined.
