Enhancing the Supply Chain through Business Analytics

Posted in Operations & IT Articles, Total Reads: 1391 , Published on 06 October 2014
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Relying on the traditional method of management is becoming difficult with respect to consumer expectations, global operations, rising fuel costs and price wars from low cost outsourcers. The traditional supply management practices lack synchronization between planning and execution, do not have access to real time data visibility, causing stock-outs or excess inventory. There is a lack of flexibility in the network and distribution, hence making the decision makers confused between profits and customer satisfaction resulting in less profitability.

Image Courtesy: freedigitalphotos.net, cooldesign


The basic facts that the companies want to investigate needs to be correct, and not many companies can say that about their existing data. With the amount of data that is being generated everywhere, how can the companies find and implement ways to manage and do something meaningful with all these inputs? This demand gap analysis demands the entry of data analytics in the supply chain field, called as “supply chain analytics”. When a company’s supply-chain management is accelerated with data-driven insights, it is more effective at controlling costs, thereby making profits. SAS has designed “8 levels of Analytics Model”. The first four rely on past data and hence are reactive decision makers. The last four are pro active decision makers and are instrumental in shaping the trends. The levels of analytics will help a company easily visualise the information and perform decision making at each step, have a clear distinction between local and global performance, do the root cause analysis and help spot new market opportunities.


Level 1

Level 2

Level 3

Level 4

Level 5

Level 6

Level 7

Level 8

Standard reports

Ad hoc reports

OLAP

Alerts

Statistical Analysis

Forecasting

Predictive Modelling

Optimisation

What happened? When it happen?

How many? How often? Where?

Where is the problem? How to find the answers?

When to react? What actions required now?

Why is this happening?

What am I missing?

What if the trend continues? How much is needed? When it will be needed?

What will happen next? Will that affect my business?

How do we do things better? What is the best decision for a complex problem?

Contains financial statistics reports.

Give custom reports (e.g.

see sales in a region at a given time).

OLAP allows you to play with the data.

Alerts can be given through email/ RSS/ red dials/ dash board.

Use of complex analytics starts. Use of regression analysis / Frequency- models.

Decide on the individual products under stocking or over stocking.

E.g.: How a promotion will affect your sales, which are the products you will promote?

E.g.: Price optimisation, size optimisation, considering supply constraints and available inventory.

Useful, but not for long term decisions.


The organisation not only sees the report but wants to understand what it means.

E.g : receive alert if the production is behind the schedule

E.g.: you try to understand why is the yield not increasing





CHALLENGES FACED TODAY IN SUPPLY CHAIN MANAGEMENT (SCM) - BUSINESS ANALYTICS NEED OF THE HOUR

• Current business applications are functionally rich and transaction intensive. The components of SCM like planning, execution and collaboration are now integrated more like into a black box which makes it cumbersome to draw any sort of business insight

• In this present interconnected world, a lot of data and information are associated with every transaction. It’s becoming increasingly difficult to analyze these data and feed back into business processes

• So our objective in this scenario must be to take the “black-box” out of SCM business applications. This is only possible through visualization and interaction, interrogation and sharing of insights through analytics.

For e.g.- During any business transaction ,we have to consider multiple dimensions like time (real time, shift , daily, etc.), product(raw material, components, sub assembly, final product), role(operator, supervisor, manager and executive) and geo-spatial (line, store, region, global) simultaneously. Analysis of data of such magnitude is extremely difficult with traditional methods.


TRADITIONAL SUPPLY CHAIN VERSUS SUPPLY CHAIN ANALYTICS

There has been ongoing debate whether supply chain analytics with its tools and techniques really adds value to the system and delivers better ROI when compared to the traditional metrics. Penton research conducted a survey in 2010, in which 210 respondents involved in the company’s supply chain operations responded. The metrics below shows the summary of the findings:


Figure: Does the metrics stay the same or improve or gets worse in the last two years.


This reveals that companies who have higher revenues and have “very confident in data” for decision making seem to be more efficient. They get more value from their assets. Their accurate forecasts and higher inventory turns keep their inventory at efficient levels. The companies who have “less confidence in data” are seen to be not improving on the surveyed metrics.


Figure: Does the metrics stay the same or improve or gets worse in the last two years


The above figure shows that companies with advanced analytical abilities in their supply chain are performing at a higher level than the companies without analytical capabilities of supply chain data. This was found to be true irrespective of the company size.


Given below is a table which puts what the companies surveyed by Deloitte have to say in favor or against the supply chain analytics practice.



ANALYTICS AND SCM- A FLEXIBLE COMBINATION

The combination of analytics and SCM are quite flexible and are not like one size fitting all. There are broadly three different types of analytics which work in tandem with SCM.



BASIS OF CLASSIFICATION

INLINE

PURPOSE - BUILT

PERFORMANCE MANAGEMENT

Embedded Supply Chain Analytics

Advanced Supply Chain Analytics

Mobile Supply Chain Analytics

Supply Chain Business Network Analytics

Strategic Supply Chain Analytics

TIME FRAME

Executional and Operational

Operational and Tactical

Operational and tactical

Tactical and strategic

Strategic

APPLICATION POINT

Analytics specific to a business process seamlessly integrated at the point of decision

Purpose built analytics and analytics workflows spanning a group of functional areas

Analytics targeted and personalized with un-tethered access anytime and anywhere

Analytics to identify and monitor B2B and B2C trends and performance

Analytics  to monitor and align performance against strategy across functional areas by correlating standardized metrics and KPI’s

USERS

Planners, power users

Functional owners, business analysts

Functional owners, business analysts, executive management

Functional owners, business analysts

Functional owners, executive management


REAL LIFE CASE STUDIES DETECTING COMPETITIVE ADVANTAGES OF THE SUPPLY CHAIN ANALYTICS

Proctor And Gamble: The company is consistently been regarded as the leader in analytics. It had started building operation research for its supply chain around 1968. They have transformed all their reporting into visual representation. Termed as decision cockpits, they contain on the fly ashes, control charts, automatic alerts, drill down capabilities. Then came the development of “business sphere”, which uses a collection of business intelligence systems that integrate real time global data, analytic models and improved visualisations. This has given them an edge to leverage more than 500 million data points in a month. The company states that it moved from 18 % to 90 % distribution in their global business units. It moved from 2,000 users to 58,000 users accessing Cockpits weekly. They saw a reduction in the number of emails generated and reduction in multiple requests on information. For them, Business intelligence in supply chain is a cant miss.


CONCLUSION

Business analytics(BA) are the technologies and skill used for iterative investigation of past business performance so as to gain insight and drive business planning. BA in critical process areas can easily affect a supply chain’s performance. We have seen how proper application of analytics in SCM can have a positive bearing on the organization. In addition, companies that can support their analytical capabilities with good IS and business intelligence (BI) system are likely to perform better. The application of an ideal BI system in the supply chain analytics gives an organization’s employees, partners, and suppliers easier access to the information they need to effectively do their jobs, and the ability to analyze and easily share this information with others. BI provides critical insight that helps organizations make informed decisions. The way forward should be to integrate real time BI systems in the supply chain analytics system of an organization. Crucial aspects like business transactions, customer demographics, seasonal flows, supplier data and inventory levels need to be carefully monitored and coordinated for successful implementation of real time BI enabled supply chain solutions. Current trends suggest that application of real time BI enabled supply chain solutions in organizations will lead to better operational efficiency and KPI’s for any organization in SCM.


This article has been authored by Sudipt Mishra & Sibani Mahapatra from XIMB


REFERENCES

www.forbes.com

http://profitpt.com/pdf/Business-analytics-supply-chain-performance.pdf

http://cjou.im.tku.edu.tw/bi2009/4.pdf

http://www.gartner.com/technology/topics/big-data.jsp



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