Predictive Analytics: The frontier for Inventory Management

To compete in today's global economy every company needs to rise against tougher competition, product devolution and changing consumer demands and behavior — all of which leads to a greater need for the organizations to closely monitor their inventories. Failing to do so can create a big hole in a company's pocket, magnitude of which can extend up to billions per quarter. But getting a better grip on inventories is easier said than done. 

Many companies still fail to keep a track of their inventories by product type, consumer sentiment and cost due to lack of necessary tools. This leads to their inability to forecast demand which results in unwanted expense in managing the out of sync inventories. It would not be surprising to know the result of a survey conducted in 2011 that 57% of 500 companies wanted to reduce their inventory carrying costs. ^[2] This creates a strong need for every company to manage their inventory effectively.

What is Inventory Management?

"Inventory management is the process of efficiently overseeing the constant flow of units into and out of an existing inventory. This process usually involves controlling the transfer in of units in order to prevent the inventory from becoming too high, or dwindling to levels that could put the operation of the company into jeopardy." ^[7]

It is one of the most important dimensions of any company's supply chain. It is strong enough to determine the success or failure of any business. A business ranging from as small as a bakery to as large as multinational FMCG company, face the same basic issues. Keeping optimum levels of inventory is an intriguing process, and one that requires invariant level of constant monitoring. Maintaining a correct level of inventory can have benefits like increased sales and happy customers. On the other hand, failing to do so can create logistic issues, lower sales and lower consumer satisfaction and plunging profits. 

What is Predictive Analytics?

"Predictive Analytics encompasses a variety of techniques ranging from statistics, 

modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events." ^[5]

Predictive modeling makes use of the patterns patterns found in historical and transactional data to categorize strengths and weaknesses. ^[6]

How predictive analytics can improvise inventory management?

Applying predictive analytics can result in significant improvement in the cost structure  of inventory management. This works irrespective of where the inventory lies in the supply chain. 

Predictive analysis makes use of historical data and applies various algorithms on that data to forecast demand. This can help in proper prediction of inventory. These algorithms provide prior intimation of possible rise or plunge in demand. This information can be used to trigger management of logistics and warehousing, supply and order collaboration and risk mitigation.

Predictive inventory management has applications not only in forecasting but also in assortment optimization, placement and design optimization and price optimization. 

Using predictive analysis, organizations can leverage upon ^[1]: 

1. Align demand and inventory to reduce overhead cost. 

2. Reduce working capital and increase cash flow.

3. Increase the availability of merchandise based on its popularity and hence achieve better consumer satisfaction.

4. Achieve increased inventory turn over rate. 

5. Improve overall profit margins by limiting discounting of redundant stock

According to a survey ^[4], approximately 73 percent of the executives who participated in the survey indicated that supply chain analytics tools were helping them meet their company goals. Around 71 percent of them believed that the there is more potential in analytics tools. They can go beyond providing information about prior performance and give more predictive features.

Supply chain analytics can be a big leap forward in various dimensions of supply chain, inventory management being one. But for every company plunging into the analytics, the questions to ask here is - "Is it worth it?", "Are we ready to take the risk?".

REFERENCES

[1] http://public.dhe.ibm.com/common/ssi/ecm/en/ytw03260usen/YTW03260USEN.PDF

[2] http://www.genpact.com/docs/resource-/inventory-optimization-the-benefits-of-building-a-smarter-supply-chain.pdf?sfvrsn=2

[3] http://www.supplychaindigital.com/warehousing_storage/reduce-risks-with-technology

[4] http://www.industryweek.com/blog/supply-chain-analytics-what-it-and-why-it-so-important

[4] http://www.ebnonline.com/author.asp?section_id=1061&doc_id=262988&itc=velocity_ticker

[5] http://www.theinstitutes.org/doc/predictivemodelingwhitepaper.pdf

[6] http://en.wikipedia.org/wiki/Predictive_analytics
[7] http://www.barcodesinc.com/articles/what-is-inventory-management.htm