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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February 11, 2013

Is Agile methodology a good solution for ERP implementations

In my capacity as a consultant and having worked in multiple implementations, I have always thought about processes that could have reduced the cost, time and rework and hence would have brought about a high degree of customer satisfaction. Today, companies in the west have identified IT as an enabler of their business processes. However, after the recent economic recession, companies have started to realize the importance of curtailing costs of IT implementations through reduction of project implementation timelines, rework effort and fitness to requirements. Today, companies realize that their SME's cannot exactly articulate the complete set of requirements to the IT vendor at the beginning of the project. Moreover, since the SME's are experts in their areas of expertise only (eg: SME's for inventory, purchasing, shipping, financials etc..), they find it difficult to integrate the requirements and present a holistic picture to the IT vendor. This generally results in high costs of change in terms of both time and effort. This limitation can primarily be attributed to the fact that the end users and the SME's get to see the end to end solution in the implemented system at the very end stages of the project. Calling for a change in functional requirements in any of the modules at this stage have ripple effects in the entire end to end configuration and hence involves more costs. The need of the hour is to present the end state of the system to the customer as early as possible through rapid iterative process so that the changes identified in the system by the customer can be incorporated at the early stages of the project itself. Each phase of the waterfall model used in the implementations today has dependency on previous phases.. For eg: the design cannot start until the requirements gathering phase has completed or the System Integration Testing cannot start unless the design and development phase has completed or the User acceptance testing cannot start unless the system integration testing has completed. Agile methodology offers a solution to the above issues through having better collaboration amongst small teams by having faster code/solution reviews and shorter release schedules. This enables a process of continuous and constant testing which constantly removes bugs in the system thus ensuring that the solution meets the customer requirement. Agile project planning methodology has a very important characteristic of having a backward step. Traditional waterfall models provide a project plan that is very rigid and avert to changes. Since there can also be situations that call for a delay in decision or delay in a phase, continuous changing and adaptive project plan is the need of the hour.
However, Agile methodologies do have some inherent disadvantages like they need active SME and end user involvement throughout the cycle. This can sometimes be very demanding for the client. Also since there is a constant change in the requirements, it takes a lot of time for the requirements to freeze and hence the scope of the project is not ascertained till a very late stage. Typically in a waterfall model, the development and design work starts only after the business requirements are signed off by the client. In Agile methodology the developers and other team members of the implementation team tend to work so closely with their SME counterparts that sometimes there can be a gap as far as integrations with other modules/systems are concerned. Also since the agile methodology calls for frequent deliveries, the need of testers remain throughout the tenure of the project and not only for unit, system and integration testing like is the case in waterfall model.

February 5, 2013

Adding intelligence in the Forecasting process to support Supply Chain Segmentation

There has been a growing momentum across multiple industries to move from a "one-size-fits-all" approach to a portfolio of different supply chain strategies. Companies segment Supply Chains using different attributes across multiple dimensions of product [product volume or demand variability], customer [Customer/Channel value] to design multiple efficient or responsive Supply Chains. Mr. Thomas further recommends in his blog approaching Supply Chain segmentation as an end-to-end strategy that encompasses business processes starting all the way from the Customers over to Suppliers. This also includes Demand Management process, which will be the focus of my discussion in this blog. The Demand Planning Organization which is responsible to sense, plan, manage and communicate demand needs to factor in the different Supply chain strategies that are to be employed for different segments.
Most advanced planning systems provide a suite of statistical forecast models and different parameters for the Forecaster to further fine-tune the models and in addition they also contain Best-Fit models that perform multiple iterations to choose the forecast model and its parameters that provide the lowest Forecast Error specified. There is however no easy way for the forecaster to assign these models to different segments that is available out-of-the-box. As a result, the Forecaster ends up reviewing the forecast models for every part every forecast cycle and then changes the assignment, given a change in the strategy for a particular segment.

I had discussed in my earlier blog the importance of evaluating the value added by the Forecasters by comparing the accuracy of their forecasts with that of a Naïve Forecast or with that of the results from a Best-Fit forecast algorithm that are available in most Advanced Planning systems. We had also looked in my other blog a key assumption that underlies most statistical forecast models - The pattern identified in the historical data will repeat in the future and hence can be extrapolated. We have seen that this however is always not the case as a result of both external activities [driven by Market, Competitor and regulatory and social environment] and internal activities [driven by Demand Shaping, Product Lifecycle]. There are means to identify structural shifts in demand patterns and the alert the forecaster accordingly, that we explored in the blog, where we had discussed the usage of one of the mechanisms - Tracking Signal. However, these are more reactive in nature and hence we see a need to incorporate the impact of these activities in the forecasting process, where the Forecaster can add immense value by providing the additional intelligence in line with the Supply Chain Segmentation Strategy.

We have seen the following approach as an effective means of incorporating the Supply Chain segmentation information in the Forecasting process:
1. As part of the initial implementation, Forecaster can develop a complete suite of different forecast profiles that are differentiated by the assigned Forecast Models [like Moving Average, Linear Regression, Trend and Seasonal, Croston, Shape Modeling] and the forecast parameters assigned [like history horizon, smoothing constants, periods for seasonality, trend dampening]. The forecaster creates these forecast profiles in close coordination with the entire Supply Chain team to achieve the different strategies set for different segments [for e.g. Products with responsive strategies need to react quickly to demand changes and hence will warrant a shorter historical horizon; Products that are in a particular lifecycle and have another lifecycle change in the forecast horizon will need to be forecasted using Shape Modeling algorithms; Products for the educational channel will need to factor in seasonality and this list can go on and on]
2. Once the Forecaster creates all the required Forecast Profile, the next task for him is to assign these Forecast Profiles to the respective SKUs and create a Forecast Strategy Matrix for them to be forecasted accordingly. These rules can also include an effectivity date, wherein post the effectivity date the default forecast profile assignment would kick in. These rules needs to be maintained centrally and should lend themselves for further automation within the Advanced planning systems to generate the statistical forecasts
3. Forecasters take note of the changes to the Supply Chain Segmentation strategies in the monthly S&OP meeting and then revisit the above table to assesses if they need to develop any new forecast profiles or if they can extend one of the existing forecast profiles to a different segment
4. As part of the Forecast review, the forecaster would also want the feedback from the Demand Planner [a different model chosen than the one the Forecaster had assigned] to be incorporated into this matrix. By introducing this closed-loop feedback cycle, the additional intelligence from the Demand Planners can be incorporated in the forecasts and thus help improve the overall forecast accuracy

While we are all aware of the basic principle of forecasting that the forecasts will always be wrong, there is merit in adding the business and market knowledge to the statistical forecast models and hence improve the overall effectiveness of the demand planning process. While most advanced planning systems offer a wide variety of statistical forecast models, they do not provide out-of-box functionalities to support this critical process of incorporating intelligence into the statistical models. We have helped some of our clients by developing custom solutions that automate this process and seamlessly connect with the backend processing in the Advanced Planning systems and thus help the forecasters to be more effective and productive in achieving higher Forecast Accuracy. I would like to hear from you on your experience and challenges that you face in this particular area.

Use of Predictive Analytics in Supply Chain

Predictive analytics uses several techniques to analyze past events to make predictions about future. One of the areas where this technology can be applied is forecasting in supply chain management. The biggest challenge that companies today face is accurate demand forecasting. This can be determined to a certain extent if technology and analysis can help determine customer's usage, spending and behavior. For example, predictive technology can help determine the number of customers that would come to a coffee store based on the current temperature. This can be arrived at through data collected in the past where similar temperatures were recorded. Based on this analysis the number of staff can also be increased/decreased .  Apart from plain predictive models, predictive technology can be used to prepare decision models to take logical decisions based on a number of factors involving many variables. For example, in the case above, the other factors/variables that can contribute to the computing the number of customers arriving at the store could be the day of the week (weekday or weekend), the time (morning, afternoon, evening), current brand value of the products sold, type of customers etc. One of the other challenges that companies face today is the customer retention. Today companies respond to customer attrition on a reactive basis and not on a proactive basis. With the proper use of predictive analytics, patterns can be obtained on reduced customer spending, usage and behavior that can help determine the probability of attrition of a particular customer. Such customers should be attended to and their grievances should be addressed. Additionally such customers could be offered some discounts and lucrative offers to retain them. Predictive technology can also help to increase in cross sales or sell additional products to customers. For example, notifications can be sent to customers to ask them if they want to repeat their orders close to festive occasions like Christmas, Valentine 's Day etc. based on their past orders.  Another challenge that companies are facing today is the increased competition with competing companies. Predictive analytics can help determine the correct product version and the timing of the product to be launched to have a competitive advantage over competitors. This can be done by analyzing the impact of a previous version upgrade and the new product version should be targeted at customers based on the customer responses to a competing brand upgrade/model.  I would further discuss on this technology and its application to supply chain in my subsequent blogs.

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