The Infosys global supply chain management blog enables leaner supply chains through process and IT related interventions. Discuss the latest trends and solutions across the supply chain management landscape.

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September 24, 2012

How to get more from an EAM Implementation

Earlier I wrote on a very similar topic (Why my EAM implementation is not giving me as I expected) and that included many reasons those were leading to suboptimal output from EAM implementations.  One such reason was lack of quality data in the EAM systems. Also, very often, I see clients mentioning about data cleansing, data enrichment and master data management issues in their existing EAM applications. This made our EAM team to further explore possible reasons for these requirements and find out solutions.

We found that during package implementations, data collection and data modeling often gets less importance than other activities, which leads to data quality issues. Some of the reasons we found are:

• Unavailability of data - This typically happens in new implementations.  Though companies would have basic data like Assets, Items etc but the data elements which are used in KPIs and decision making like Failure Hierarchy and Asset Attributes are missing.
• Data Loaded from multiple sources - Data exists in different files, applications and databases. Each data source is having its own format, granularity and completeness. Once this data gets loaded and consolidated into one system, there are discrepancies.
• Lack of Control - In many companies there is no proper control mechanism while defining new Assets, Items and Specifications etc. This leads to individuals defining the data based on their own standards and formats.
• Continued Manual Usage -Due to volume, compliance and complexity etc, sometimes companies continue using manual systems. For Example, safety related procedure. Though it is possible to have this implemented in package, but companies hesitate to implement them in system. This leads to suboptimal usage of system due to combination of manual process and software application.

This is just an indicative list and there are many more reasons like too much data, too less data, data granularity etc which also result into suboptimal usage of system. Typically these reasons lead to Excess Inventory, Low Asset Reliability, Non-Compliance to safety, Non-Compliance to standard maintenance procedures, reduced decision making capabilities and incorrect reporting.

While these reasons are the implementation reasons and a good implementation methodology would prevent these issues to happen, but at the same time these issues can crop up even after the implementation is over, due to a continued usage of the system and lack of control. Hence data cleansing and master data management is always a continuous activity and should not be considered just onetime activity.

To provide a complete data enrichment offering, some of the solutions our EAM and BPO practices are working on are:

• Tool based data profiling solution
• Data classification as per standards
• Data Cleansing
• Domain expertise for various industries
• Best practices for master data management

I will write more about our offering in next blog, once we have got our solution in some shape.


September 18, 2012

Demand Planning - Practicing the Best Practices (contd.)

In the last blog on this topic, I mentioned that I will share some of the best practices in some of the sub-processes in the typical demand planning cycle. In this blog I will focus on first couple of sub process of demand planning cycle - 1) Setting up demand planning objectives and metrics for different business units/customers/key items/locations 2) Setting up the frequency of the forecasting process (create/review/publish) with the time horizons.

While setting up objective of Demand Planning process, Organization needs to be cognizant of the fact that objective should not be just limited to generation of the most accurate statistical forecast by using best suited statistical algorithms. Achieving 100% forecast accuracy is next to impossible. Hence organizations need to leverage internal collaboration (for demand shaping) and external collaboration (for demand sensing) as well in defining the holistic process in order to cater to the perennial demand uncertainty or variability. Demand planning process also needs to address specific business scenarios like New Product Introductions (NPI) and Product Lifecycle Management. In case of new products, even after applying concepts like 'Like Modeling,' achieving the goal of forecast accuracy is very much a challenging task. So, till the time new product is settled in the market, the goal in case of NPI can be a continual reduction of forecast error.

Another best practice in setting the objective is to strive for a single forecast across the organization for various products. Demand planning is a sub-process within sales and operations planning and is not a compartmentalized task. Each of the functional departments within company e.g. Sales & Marketing, Manufacturing, Finance would normally come up with their own forecast and there are high chances that some sort of biasness would creep in while generating different numbers. Hence Demand Planning process' main objective should be to derive a single operational consensus forecast by having a planned cross-functional meeting every month.

Now, let's look at different forecast measurement metrics e.g. Mean Forecast Error (MFE) or Bias, Mean absolute Deviation (MAD), Mean Absolute Percentage error (MAPE), Weighted Absolute Percentage error (WMAPE).

MFE measures the average deviation of forecast from actuals. However zero MFE does not imply that forecasts are perfect as positive and negative errors cancel out here. Hence it is not recommended as a single metric to be followed.

MAD measures the average 'absolute' deviation of forecast from actuals. Positive and negative errors thus do not cancel out here similar to MFE. However since this is a number, there is no way to know, if MAD error is large or small in relation to the actual Sales data. Therefore business planners will not be able to assess the real implications of higher or lower MAD in assessing the forecast value. Hence it is also not recommended as single metric to be followed.

As against MAD, MAPE measures absolute error as a percentage of the sales. Hence, as best practices, companies use both MAPE and Bias for measuring forecast error. Based on AMR Research, a 6% forecast improvement in MAPE could improve the perfect order compliance by 10% and deliver a 10-15% reduction in inventory.

Most of the companies normally choose to generate forecast at category or family as compared to the SKU, location and customer level. The greater the degree of aggregation, the more accurate is the forecast. However the best practice is to generate forecast at the SKU, location and customer planning level. In the past, this was not possible due to higher processing requirement of the servers. But this is pretty much possible today with any of the leading supply chain planning products available in the market.  

With regard to frequency of forecast generation, most of the organizations adopt monthly forecast generation for the horizon of 18-24 months which is allocated over weekly buckets by using allocation rules. As per the research conducted by Aberdeen group, almost 60% of companies generate forecast at a frequency of lesser than a month. However even a month is a pretty long duration for sensing the demand for CPG company, as on daily basis there are wide fluctuations  taking place in the consumer marketplace.  Hence CPG leaders like P&G, Kraft are performing short-term forecasting (by making use of Demand Sensing product) each day for the one-to-three/four week time period in addition to regular monthly forecasting for longer duration.

In next blog, I will keep on adding best practices in CPG industry for the next sub-processes of demand planning process. Till then, please enrich this series of blogs with your experiences as well...


September 13, 2012

Making 'Cash on Delivery' attractive to E-Retailers

It is still early days in India for the 'Online Retailing' industry. Right now the industry is fragmented as there are many players in this space. Hence there is a stiff competition among the players. One of the ways by which  E-Retailers are attracting people to buy on the net is by offering 'Cash on Delivery' (CoD) payment option. This is helping them overcome the reluctance of customers to shop on internet. This payment option tries to overcome the trust barrier which prevents customers to shop on internet.

CoD is a good option for customers who want to experience online shopping for the first time. It allows customers to pay only after receiving the goods. While CoD is an excellent option for the customer; it is the least attractive payment mode for E-Retailer. This is because CoD eats into their precious working capital. Moreover, CoD has other pain points when compared to online payment using credit/debit card.
The two main pain points in CoD are
1.      Courier Agent who delivers the goods collects the money from the customer. Once they accumulate cash from multiple deliveries they have to come back and deposit the cash to the E-Retailer's a/c. There is a time gap before the payment is realized by the E-Retailer.
2.   Collecting large amounts of cash also increases chances of theft.
Technology offers some solace in addressing the above pain points of CoD. E-Retailer can explore any one of the below mentioned options.
1.    Mobile payment options like Airtel Money (This is based on Infosys mobile commerce platform
WalletEdge) can be used to get the cash from the customer. As soon as the customer receives the goods, they can transfer the money using Airtel money on their phone to the vendor's a/c. This will ensure faster and safer mode of cash transaction. This solution has a limitation as all mobile customers may not be using Airtel services. The second solution detailed below can overcome this.
2.   Convert mobile phone to Point of Sale (PoS) terminal. Companies like Square Inc (USA), Ezetap (India) offer a simple credit/ debit card reader which can connect to a mobile phone/ device and enables the phone to read debit/credit card. Once a customer receives the goods which they have ordered online, they can swipe their card in the device carried by the courier person. The payment is made instantly. As the mobile phone and debit/credit card reach in India have grown tremendously in last few years, this option is one of the convenient options available for the E-Retailers.
Both the above solutions give the E-Retailers a faster and secure way to receive the money from the customer. As our Indian E-Retailers try out new and innovative ways to make customers shop on the net perhaps very soon we will have our own Amazon!!

September 5, 2012

Supply Chain Collaboration and Social Media

One of the key factors contributing to the success of a supply chain is the collaboration between the supply chain members.  Research has proved that the use of social media can vastly improve the sales, lower costs of procurement and bring about innovation in the supply chain

 In today's competitive environment, it's difficult to find new customers. Hence, it's getting increasingly important for companies to increase revenues with existing customers. The only way this can happen is to collaborate effectively with existing customers to turn them into big accounts. Social media can be a wonderful tool to bring about this change. A study has revealed that employees are able to interact more effectively with their suppliers and attain the latest information regarding the latest trends and technologies through the use of social media. Interaction through social media with suppliers can help bring about better service and on-time deliveries. This, in turn can help the vendors as well, since the customers start trusting these vendors and start giving them bigger business based on their reliability and sustenance. This eventually can turn into a symbiotic  relationship since longer vendor collaboration leads to lower costs.
Today, customers are influenced by reviews and comments of supplier/company posted on social media. Also, since the amount of people using social media today is so huge that the number of likes/dislikes on a product/company can greatly influence a customer's perception. If data from such social media can be integrated to supply chain, it can prove to be a great tool to attract customers.
Today, companies are trying to consolidate suppliers to service all points of their operation. Social media can again help in this front since the company's strategic vendor partner can refer its own partners to supply the company. This helps bring about strategic alliance between companies and their suppliers by which the supplier can help to serve its customer through its partners. This also results in optimal costs from customer's point of view. Social media can also be used to collaborate internally. For example, an internal social media application can help the purchasing team collaborate more effectively with the payables team. Today, tools are being developed to send notifications to all concerned users when the risk profile of a vendor changes. This helps better decision making by effective collaboration amongst internal team members.
Today 91 percent of working adults who are linked in any manner to any company's supply chain use social media regularly. Imagine how interesting the workplace would be if these people start using social media in workplace

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