Question

Just-in-time (JIT) strategy reduces inventory (storage/waste) costs. This method requires producers to forecast demand accurately. Explain (briefly) how you would use “Big Data” to help implementing JIT?

Answer 1

The British Motor Corporation plant in Australia originally developed the just-in-time production system

in the 1950s, but it was largely adopted by Japan in the 1960s and 1970s. Post-World War II.

Just-in-time manufacturing operates on a philosophy of complete elimination of waste.

Rather than working on a production-push basis, JIT manufacturing works on a demand-pull basis.

Essentially, actual orders dictate the exact quantities organizations manufacture, they make only what is needed, when it is needed and in the amount it is needed.

Advantages of Just-In-Time strategy

· This method eliminates waste, removing out-of-date or expired products and overproduction from the equation.

· Stock holding costs are kept to a bare minimum. Storage space is freed up and better utilized, eliminating unnecessary rent and insurance costs.

· Because only essential stocks are acquired, less working capital is necessary to finance procurement. A minimum re-order level is set, and new stocks are ordered only when that mark has been reached, streamlining inventory management.

· Return on investment (ROI) is generally high because of the low level of stocks held.

· Just-in-time production can easily incorporate changes in demand. Because the system works on a demand-pull basis, all products manufactured are sold. JIT production is adaptable to today’s somewhat volatile markets.

· This system necessarily emphasizes the “right first time” concept, minimizing costs of inspection and rework.

· JIT production encourages high quality products and continually improving efficiency.

· This method emphasizes constant communication with the customer to improve processes, meet changing needs and promote higher customer satisfaction.

To do JIT successfully, organizations must implement incredibly detailed plans concerning procurement policies and manufacturing processes. They must employ technological systems, such as production scheduling software and electronic data interchange, to provide necessary support.

And finally, organizations must be willing to continually reevaluate systems and implement new methods to meet their customers’ needs and improve efficiency.

Here comes the importance of Big Data.

                The term big data refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around a long time.

It involves,

Volume: Organizations collect data from a variety of sources, including business transactions, smart (IoT) devices, industrial equipment, videos, social media and more..

Velocity: With the growth in the Internet of Things, data streams in to businesses at an unprecedented speed and must be handled in a timely manner.

Variety: Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.

The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable

1) cost reductions,

2) time reductions,

3) new product development and optimized offerings, and

4) smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as, Determining root causes of failures, issues and defects in near-real time.

Generating coupons at the point of sale based on the customer’s buying habits.

Recalculating entire risk portfolios in minutes.

Detecting fraudulent behavior before it affects your organization.

Uses of “Big Data” in JIT approach includes,

1. Risk management : There are several many different areas of supply chain management where big data can be of significant help.

Suppliers now have the choice to share their production data with their partners and customers which creates complete transparency and a highly effective communication channel for both parties. This way the manufacturer can see exactly whether the supplier is delayed with production or just in time, to then adjust all the related processes and avoid waiting times.

2. Build to order configurations : Manufacturing ‘products to order’ became a trend and not just in the automotive industry but in aviation, computer services, and even consumer goods. The build to order (BTO) production approach is a very efficient and profitable business model. But in order to see real growth from it, a well-defined data platform needs to be in place to analyze customer behavior and sales data.

3. Improve product quality : Product quality maintenance is of top priority for manufacturers. Most of them already have the data needed to significantly improve quality levels and reduce quality-related costs, but just very few of them can connect their data sources in a way that it would provide actionable insights.

4. After-sales: The costs of warranties and recalls can easily go out of control even due to the smallest mistakes in the production process. With the help of big data, it is possible to either avoid or to foresee warranty or recall issues, potentially saving significant amounts of money.

5. Track daily production : In order to optimize production quality and yield, manufacturers need to have daily data flow from their production lines in order to see discrepancies and opportunities in real-time. This includes sensor data coming from the production machinery and also financial information that is properly linked together with operational data for analysis purposes.

6. Data-driven enterprise growth : By using big data, it became possible to quickly compare the performance of different sites and also to pinpoint the reasons for the differences. In addition to internal production and sales data, it is also possible to analyze entire markets, build what-if scenarios and to use predictive models.

7. Predictive and preventive maintenance : With the sophisticated sensor technology, operational data can be collected and analyzed in real-time from almost any kind of machinery or consumer product.

When operational data is analyzed with a pattern recognition method, upcoming failures and need for maintenance can be predicted well in advance. That allows preventing downtimes and costs related to maintenance. In the same time, preventive maintenance will drastically prolong the lifespan of machines by preventing irreversible failures.

8 Overhead tracking : The overhead costs are determining the profitability of each manufacturer. To have real control and visibility over these costs, big data environments are needed with connected data sources and advanced analytics capabilities.

Part standardization is one of the big areas that can hugely contribute to reducing supplier-related costs. It can reach a significant reduction in the part and supplier proliferation. This saves not only costs but time on managing parts data.

9. Testing and simulation of new manufacturing processes : The day has come in manufacturing when no risk has to be taken when implementing a new product or process. Both manufacturing processes and the products can be tested before production/implementation. This is possible thanks to digital twins, virtual reality environments and manufacturing process simulations.

In short, this strategy encourages continual improvements, as well as planning and innovation to overcome any obstacles and inefficiencies. This just-in-time manufacturing method has been successfully implemented in many manufacturing organizations. It is a philosophy that focuses on optimization: reducing waste, increasing productivity and constantly responding to customer needs.