Data Analytics for the Supply Chain

Data Analytics for the Supply Chain

Data Analytics for the Supply Chain, More and more organizations are adopting data analytics to respond to supply chain disruptions and to enhance supply chain management (SCM), with several major disruptions currently impacting the supply chain. The following shares data analytics for supply chain, take a look.

Data analysis of the supply chain1

Comprehensive analysis of the benefits of big data to the supply chain

Nowadays, big data has completely crossed the concept of hype, and become an important weapon in the development of business in many industries in the real application, but in the field of supply chain management, the application of big data technology industry development is in the infancy stage, but I believe that along with the rapid development of big data in other industries, big data in supply chain management will also quickly follow up, then people will inevitably ask what benefits big data can bring to the supply chain, the following please follow Qian Yuan Kun and I understand the benefits of big data to the supply chain.

Big Data and Supply Chain

1, inventory optimization. For example, SAS's unique and powerful Inventory Optimization model can be used to minimize supply costs and improve supply chain responsiveness while maintaining high levels of customer satisfaction.

Its inventory costs can drop by 15% to 30% in the first year, and its accuracy in predicting the future rises by 20%, resulting in a 7% to 10% rise in its overall revenue. Of course there are other potential benefits such as increased market share. In addition, with SAS, product quality is significantly improved, and the defect rate is reduced by 10% to 20% as a result.

2, creating operational benefits, from the supply chain channel, and the production site of the instrument or sensor network collected a large amount of data. Using big data to more closely integrate and analyze these databases can help improve inventory management, the efficiency of sales and distribution processes, and continuous monitoring of equipment. For manufacturing to thrive, companies must understand the cost benefits that big data can produce. Predictive maintenance of equipment is in a position to adopt big data technology now. Manufacturing will be a major source of Big Data operating revenue.

3, B2B e-commerce supply chain integration. Strong e-commerce will lead the upstream downstream production plan - downstream sales docking, this docking trend is the upstream manufacturing outsourcing supply chain management Supply-Chain, focusing only on production Manufacturing, ProductionChain (R& D).

Logistics outsourcing up to supply chain outsourcing is a huge leap, reflecting the strong competitiveness of e-commerce and integration capabilities, massive data support and cross-platform, cross-company docking has become possible. B-B supply chain integration has a strong market space, can improve China's industrial layout, industry chain optimization, optimize the distribution of production capacity, reduce inventory, reduce supply chain costs, improve supply chain efficiency.

4, logistics platform scale development, B-C business model integration has become a reality, but the construction of the logistics execution platform is dragging the bottleneck. The integration of the sales supply chain of multiple products has great technical difficulties, such as supply cycle, inventory cycle, distribution time, logistics operation requirements, such a logistics center is very difficult.

Big data platform construction will drive the overall sales supply chain integration; China's also the reality of the problem of cross-regional logistics and distribution, urban-rural differences, etc., the government's control is a major difficulty/troubleshooting, big data platform to help the government to adjust its functions in place.

5, product co-design, in the past we are most concerned about product design. But now, in the product design and development process, the relevant personnel collaborate with each other, the factory and manufacturing capabilities are also synchronized in the design and development. The current pressure is to deliver more competitive, higher configuration, lower price, higher quality products to the market, and to meet all these requirements at the same time, is the next big value for manufacturing and engineering companies. That's where big data comes in.

How are organizations deploying big data?

Getting value out of data starts with handling big data, with the ability to ****enjoy, integrate, store, and search huge amounts of data from many sources. And in the case of the supply chain, that means being able to accept data from third-party systems and speed up feedback.

The overall impact is increased collaboration, faster decision-making, and greater transparency for all involved. Traditional supply chains are already using large amounts of structured data, and organizations are deploying advanced supply chain management systems that store resource data, transaction data, supplier data, quality data, and more to track supply chain execution efficiency, costs, and control product quality.

Benefits of Big Data for the Supply Chain

The current concept of Big Data goes beyond the traditional concepts of generating, acquiring, transforming, analyzing, and storing data, with the emergence of unstructured data and a diversity of data content, and the deployment of Big Data will face new challenges.

The challenge of simply processing the massive amounts of information generated, transmitted, and stored today. The volume of data is exploding, and with the adoption of M2M (machine-to-machine communication), this trend is set to continue.

But if these challenges can be addressed, a whole new world can be opened up? The core is in two areas:

1. Solve the problem of data generation, that is, how to use the Internet of Things technology M2M to obtain real-time process data to virtualize the supply chain process. By tapping into the potential of these new datasets and combining them with information from a wide range of sources, it is possible to gain entirely new insights. In this way, companies can develop entirely new processes that are directly linked to all aspects of the full product lifecycle. Integrated with this are reporting and analytics capabilities that provide feedback on the process, creating a virtuous cycle of reinforcement.

2. Solving the problem of data application, how to make the data generated by the various value conversion processes in the supply chain to generate business value, is fundamental to the revolutionary productivity of data deployment. The application of big data in the supply chain is no longer a simple ` transaction status visualization, support decision-making inventory level, the traditional ERP structure can not afford. Therefore, enterprises must re-do the top-level design of data application, and establish a powerful and comprehensive big data application analysis model, in order to cope with the challenge of how to play the value of complex and massive data.

The application of big data in the supply chain field has just begun, with the rapid development of the supply chain, big data analysis, data management, big data applications, big data storage in the supply chain field contains a huge potential for development, the investment in big data is also only with the supply chain combination, in order to produce sustainable, large-scale development of the industry

Data analysis of the supply chain 2

Analyzing the value of various types of data for supply chain management

In the process of supply chain management, we need to define various metrics, collect and analyze various types of data, analyze and assess the current state of management, identify gaps, and then designate an action plan.

We are talking about analyzing data in order to improve operations and create more value for customers, shareholders, and employees.

Before we discuss value analysis, let's talk about what is value?

In the eyes of the customer

It's mainly about adding value.

Let's say a supplier's rework process, if it appears on a detailed quotation for the customer to pay for it, the customer might not be too happy about it because he doesn't see these action steps as adding any value to him.

Simply put, judging value-added from the customer's point of view is mainly about whether the customer is willing to pay, and whether it was done right the first time, and for the production process, it's mainly about whether the activity changes the physical form of the material.

In the eyes of the boss

First, the main thing to look at is whether it is necessary.

Let's say employee training, preparation of various reports, compliance checking, and risk management are not value-added in the eyes of the customer, but they are very necessary for the boss, or there is no way to eliminate them at the current stage.

Secondly, the main thing to look at is whether it is efficient.

Under the current business model, whether it is the most efficient, whether it is the lowest cost, whether it is the fastest turnover, that is, whether it can bring more return on investment.

In the eyes of the employee

It's mostly about what's their main appeal in doing this?

Can the enterprise help employees earn money, the enterprise must first meet the needs of employees to meet the needs of customers, to meet the needs of customers after the need to meet the needs of the boss.

Imagine, if the needs of employees can not be effectively met, they will not be attentive to try to meet the needs of customers, the needs of customers if they are not met, they will not continue to buy or even cancel existing orders, so the boss will not make money.

In the process of supply chain management, data*** can be categorized into the following types:

I. Business data

For example, the supplier's offer, the employee's salary, and the different prices like the customer's price, which are all business data. Business data is mainly determined by supply and demand as well as competition. These data are mainly used to adjust supply and demand to meet your needs through side-by-side comparisons.

Transaction/process data

Nowadays, most companies have e-business processes, and one of the main benefits of e-business is that all the steps of the transaction process are digitally stored, which makes further data analysis possible.

For example, if we want to improve our delivery performance to customers and shorten the lead time, the original Lean survey may be to interview and find out where the waste is, and then make targeted improvements. But doing interviews, all the content, as long as it is a person to express a point of view, behind the point of view will be position. The position is not so easy to let the point of view and then keep objective.

We may wish to dig directly from the ERP, OA, SRM, CRM and other systems of real-time information, really let the reality speak, look at the past in the process of meeting the delivery of time are spent where to go? Not only look at the average time spent in each link, but also look at the fluctuation of their time, and their cycle of dealing with a specific business, drawing a real-time value stream map, finding the problem points, and confirming the improvement points, will become very easy.

Three, operating data

Operating data is mainly to see the degree of correlation with the strategic objectives of the enterprise, as well as systematic data or that can be corrected, and the significance of the data from the next step in the guidance of the action to be taken to think in all directions, the development of the applicable operating data assessment indicators.

Supply chain data analysis3

On the supply chain management how to do a good job of data analysis:

First, the supplier access data analysis:

The supplier can independently view the modification of their own business information: business license, product qualification certificates, suppliers of product information, etc.. At the same time, the purchaser can also provide data information on the various suppliers to compare and select.

Second, the procurement demand data analysis

A unified portal to manage the company's internal procurement needs, without having to a department by department with Excel statistics and then statistics, can be directly uploaded to the system procurement needs, and then centralized collection and aggregation of purchasing information, greatly improving the efficiency of the application. This allows the system to upload purchasing requirements directly into the system and then centrally collect and summarize the purchasing information, greatly improving the efficiency of the application.

Third, procurement quotes, price comparison data analysis

The use of supplier management system, according to the needs of the company's procurement business to develop a quotation ? template, select the suppliers who need to initiate the quotation, the system generates a quotation in one click, the system batch group quotation and automatic notification to the corresponding supplier, the supplier only need to enter the price of the convenient quotation, the purchaser according to the quotation automatically summarized to generate the comparison of the single, than the results of the price data is open and transparent.

Fourth, procurement shipping, warehousing, returns data management

The supplier of a key shipment, the automatic print out of the delivery note, while the system will save the electronic delivery note, the procurement side of the receipt of the data can be automatically passed over, no need to manually enter the number of checking can be confirmed. Both sides of the shipment, warehousing, return data synchronization in real time to avoid information lag and omission.

V. Purchase Reconciliation, Invoice and Payment Data Management

Shipment, warehousing and other data in real-time to automatically generate a summary of the board, the supply and demand sides of the real-time online reconciliation. The process of reconciliation, invoicing, and payment management can be audited and reviewed at any time.

Supply arrival data analysis

The system automatically counts the data of the supplier's collaboration process, and automatically generates multi-dimensional assessment panels to statistically analyze the number of suppliers arriving at the warehouse, the arrival of each supplier in a timely manner, as well as the qualification rate, and to see if it is consistent with the contractual agreement, so as to determine whether it is possible to continue to collaborate with the supplier, and also to determine whether it is possible to "win the best". The company is also able to "eliminate the winners and eliminate the losers" to help enterprises to precipitate and select outstanding supplier resources.