Abstract
Information Technology has become a significant component of new capital investment (some estimate put it at 30%) and several economists look to computers as the best hope for a sustainable increase in economic growth rates. Thus the question arises, can computers and the usage of information technology help attain the break-even earlier resulting in more and more profits? Thomas K. Landauer, in his book, The Trouble with Computers (MIT Press, 1995), argues that computers have been unproductive due to poor design and deployment. To support this thesis, Landauer Drwasn on economic data and analysis as reported by other researchers, and offers voluminous anecdotal evidence of computer shortcomings. He then proceeds to offer his own solution to this problem and provides an argument that computers can be improved in ways that would make them more useful, usable and productive.
Introduction
In India, Information Technology has built up a valuable brand equity over a decade. It is estimated that IT sector in the country has been growing at a rapid rate of over 35% per year. The contribution of IT has not been only limited to Banking, ITES and BPO sector, but also to the ever-growing Manufacturing sector. IT today provides the communication and analytical power to the organization to conduct business and compete at the global level. In the broadest sense, information technology refers to both the hardware and software that are used to store, retrieve, and manipulate information. At the lowest level you have the servers each with an operating system. Installed on these servers are things like database and web serving software. The servers are connected to each other and to users via a network infrastructure. And the users accessing these servers have their own hardware, operating systems, and software tools. Source: Nasscom
Information technology has been of use to manufacturing Sector through various applications such as SAP, ERP packages for implementation to Production Planning, Human Resource, Material Management etc. Though some of the companies have also been using it for monitoring Productivity, but it has been limited due to huge costs attracted due to outsourcing along with sharing daily production figures, a risk associated with disclosing company’s internal figures with outsourced agencies. Another implication of this study would be identifying methods to use Radio Frequency Identification technology (RFID) for improving productivity.
Literature Survey A growing number of academic studies report positive effects of information technology on various measures of economic performance. As more research is conducted, we are gradually developing a clearer picture of the relationship between IT and productivity. However, Productivity measurements isn’t an exact science. While one study shown a negative correlation between total factor productivity and high share of high-tech capital formation during 1968-1986 period [Berndt and Morrison, 1995], another study suggests that computer capital contributes to growth more than ordinary capital during the similar period [Jorgenson and Stiroh, 1995]. Hitt and Brynjolfsson [1994] report positive effects of IT based on output and consumer surplus measure. On the other hand Landauer [1995] de-emphasizes the findings of recent studies and documents various cases of “the trouble with computers”.
More than a decade ago, one of the earliest surveys concluded that we still had much to learn about measuring the effects of computers on organizations [Attewell and Rule, 1984]. A more recent survey also reports a “sobering conclusion: our understanding of how information technology affects productivity either at the level of the firm or for the economy as a whole is extremely limited” [Wilson, 1995].
This study intends to explore what kind of IT investments could improve the productivity of the manufacturing industry.
Project Work
There has been a lot of work on Inventory Management or other areas of Productivity improvement, but when it comes to Productivity improvement through Labor Management a lot is yet to be done. Here when we speak of Productivity we mean labor productivity and would be discussing ways to monitor and improve labor productivity. Many mass production companies have either developed a tool of their own or else bought it from external clients, but when it comes to SME’s they are still at a nascent stage with its usage. The heart of the issue is always the establishment of a fair and objective means by which to measure work. It is complicated by the fact that neither the work itself, nor the individuals who perform it are the same from instance to instance. And, by the way, neither are the supervisors and managers who are charged with planning and acting on the data produced.
The single most effective process for establishing a reliable, objective metric for performance is the classical engineered standard developed through detailed time study of each task in context. Moreover, a fool-proof method is required with minimum error to record the performance and provide accurate results.
Best Practices. This approach begins with the identification and implementation of the optimal methods and procedures for performing various activities in a manufacturing process (Lifting a case-assembly to be attached the back panel, Tightening the screws or Attaching Label, etc.). There is no point in developing metrics for the inefficient or inconsistent methods – they will not have much value. Rather, you want to train people to do things in the best way and minimize the delay or efforts to the minimum so that efficiency can be improved. This improvement trend can be observed and monitored using the Information technology, use of RFID (Radio Frequency Identification) or Barcode scanning can capture the actual performance data and this data can be compared with Standard time to get the actual performance.
Building Standards. After implementing best practice improvements, Process Improvement team then studies the work, breaks each task into its constituent elements and develops an allocation of time for each occurrence of each element in that work. For instance, if an screwing task involves hand movement to pick the device, picking up the screw and tightening it at four places, the unit of work would contain elements and related time for picking up the screw driver, picking up each screw and tightening them individually and moving the object to outbound staging among other things. Some highly varied tasks may use 30 or 40 elements.
A precise calculation of the total “earned” time for this task presumes that the individual performing the work is using mutually agreed methods. Management’s expectation should be that a normal person with normal skills can sustain this pace for the duration of their shift (eight or ten hours) without undue fatigue.
For some tasks (loading, pass along picking or unloading a truck), an individual measurement may be especially difficult. In those cases, the most common solution is to create a standard time for the task and relate it to the total time for the entire team. While not as precise, this approach retains the fairness and objectivity that is essential to a sound measurement process. By studying many iterations of each work task and element, the engineer is able to arrive at time allocations which are highly reliable across a very wide sample of the work associates are asked to perform and can very accurately predict how long any given work assignment should take. However, before any standard is applied to a working situation it needs to be validated by the facility’s management to assure that everyone is satisfied that it is fair and accurate. Measurement then becomes the process of capturing actual time applied to the task and comparing it with the predicted or calculated “standard” time.
Capturing Actual Performance Data. This process need not be complex, but must be complete in order to be valid. Beginning and ending times for all tasks and all users are essential. Data can be captured automatically by radio frequency (RF) devices, or manually by bar code scanning or keyboard entry. The key is to minimize the time required to capture the information, while assuring that it is captured properly. This affords management an opportunity to see both performance and utilization at the individual or any other level.
Calculating Performance. Performance calculation simply relates standard time to the actual time applied by that individual to the accomplishment of that task. Data can be captured from the RFID device or from the Scanner and processed along with the Standard Time already entered in the computer to keep a check on Productivity.
By dividing standard time (360 minutes) by the actual time (400 minutes), the system returns a performance rating of 90% (or in a “0-based” form –10%) of the expectation or standard.
Calculating Utilization. While performance measures the employee’s work, utilization measures management. It is the application of available time (labor) to the open work and indicates how well managers have used the labor resource. For an individual, as an example, there are 450 minutes in an eight hour shift (two 15 minute breaks; lunch is not included). If 400 minutes of that time were applied to relevant work, that person’s utilization would be 89%
Calculating Total Productivity. In combination, these two calculations provide a powerful and otherwise unseen indication of how efficiently work is being accomplished. This metric is derived from relating the two results discussed above. By multiplying performance for a whole period (90%), by the utilization factor (89%) the total performance for the facility is actually 80%. When performance is less than 100% and labor is applied less than 100% of the time, the overall result is significantly lower.

Hardware and Software Design

The Barcode Scanner or the RFID device transmits the data to the Central Control Unit using standard RF-links or via standard Cable (UDB, RS-232 etc). The digital data extracted from the hardware is processed into useful information using the Application Software. The application software compares the Standard Time and the scheduled Takt Time with the current data as received from devices and presents a real-time picture of the Production trend. Production Rate, Yield, Defect rate etc can also be monitored in real time so that immediate corrective actions can be taken to improve the condition and bring it back to normal. Real time display screens present the processed data to the Floor manager and the entire production team to take immediate actions to increase the production flow.
Another implication of this method is to keep a check on the defect rate. The scanners can also be deployed at the repair stations to track, monitor and historically store the defect type and reasons for defect generation so that corrective actions can be taken. This method can also be beneficial in tracking the Market defect rate of the product by tracking the production history and similarly the production lot can be segregated. The question arises, with such huge pile of data it would be very difficult to extract the correct information and that too in a timely manner. The solution to this is Data Warehousing. You have accumulated years of archived data that would be valuable for historical analysis. The first important feature of a data warehouse is that it provides a comprehensive, historical, and homogenized view of the organization. Data warehousing can be used to support information access and to transform information into insights and action. A significant difference between an Operational system and a data warehouse is the granularity of the data stored. An operational system typically stores data at the lowest level of granularity: the maximum level of detail. However, because the data warehouse contains data representing a long period in time, simply storing all detail data from an operational system can result in an overworked system that takes too long to query.
A data warehouse typically stores data in different levels of granularity or summarization, depending on the data requirements of the business. If an enterprise needs data to assist strategic planning, then only highly summarized data is required. The lower the level of granularity of data required by the enterprise, the higher the number of resources (specifically data storage) required to build the data warehouse. The different levels of summarization in order of increasing granularity are:
• Current operational data
• Historical operational data
• Aggregated data
• Metadata
Your data warehouse will streamline the effort to acquire and digest information. This benefit is realized when users learn that they can save time by looking at summarized, graphically displayed information instead of plowing through the details to find the exceptions that need attention. We can measure productivity increases by measuring the amount of work that can be accomplished within a time interval
Results
In the final analysis, justifying a business intelligence system built on a data warehouse is about empowering your best people to gain insight from exploiting the information you’ve spent millions to control and years to develop. The data warehouse with business intelligence has the potential to enable users to continuously produce enormous, sustainable, measurable benefits that lead to competitive advantage and fulfill the promise of IT.
Benefits
Use of Information technology via the Business intelligence software organizes and presents information so that people can gain insight which eventually leads to action. A data warehouse monitors results after the action. That leads to more insight and eventually, knowledge. Business intelligence software leads you to the benefits in the following categories:
• Improving Productivity
• Improving Sales
• Improving Business relationships.
• Reducing expenses.

Limitations
The findings drawn from the present study cannot be considered as very conclusive. The limitations of this study are while dealing with small organizations with a turnover of few Millions who would not be able to spend huge capital involved in
• Setting up initial Hardware, viz Scanners, RFID, Servers etc.
• Limitation to develop Data Warehouse and reliability on external agencies for it.