Invigorating the topline of Hi Tech enterprises through a streamlined and efficient Demand to Deliver Value Chain
Most of the Hi Tech Manufacturing organization are reeling under tremendous margins pressure due to flat to modest growth in the topline and ever increasing costs. In this tough environment its absolutely imperative for organizations to capitalize on whatever sales opportunities are presented to them. At the same time they also need to be very aggressive in scouting of new avenues for revenue growth, so that their toplines are positively impacted.
A streamlined, efficiently run Demand to Delivery Value Chain is the key to this objective and this in turn calls for a best of breed, cutting edge, integrated, Enterprise wide solution/application to support the execution of this value chain.
Refer the blog by Abhishek Sabharwal to follow the full storyline. (http://www.infosysblogs.com/oracle/2010/02/demand_to_deliver_value_chain.html)
Infosys has developed an Oracle Applications based Demand to Deliver solution (D2D), which enables the following 3 Business Value Levers, which should positively the topline of the organizations : 
1. Increasing the Forecast Accuracy with the Strong simulation capabilities to model strategic business scenarios, leading to better demand capture and fulfillment
a) Ability to easily add external syndicated, POS and other data sources
b) Ability to Forecast at any level of time, product, and location aggregation
c) Ability to support different demand data for each customer and channel
2. Ensuring On Time Delivery leading to quicker Cash flow, enhanced customer satisfaction, stifling the competition
a) Ability to Plan the entire supply chain with a single holistic plan
b) Ability to Utilize sophisticated constraints and optimization algorithms
c) Ability to Include transportation/logistic partners and warehouse teams in the planning process
3. Ensuring lesser Stock Out situations leading to higher demand to sales conversion
a) Ability to do comprehensive inventory and POS management
b) Ability to generate supply chain visibility with accurate real-time POS data
c) Ability to do forward looking replenishment and allocation plans
Comments
This sounds like a very comprehensive solution indeed that I’d love to learn more about. Here are a few thoughts based on our experience in working with high tech manufacturing companies.
We’ve found that a key step in improving the quality of the forecast (as measured by Forecast Accuracy, your first point) is holding the forecasters accountable for providing a good forecast in the first place. This requires first measuring how good the forecasts have been in the past (how accurate was it? Which forecasters are most accurate? For which products are the forecast most accurate?). You can then use these measures to identify what needs work and how to improve forecast quality going forward.
With (2) and (3) (ensuring on-time delivery and lesser Stock Out situations) we’ve found that it’s critical to make sure that Operations is aligned to the sales forecast. One of our customers found themselves in a situation where Operations simply did not believe the sales forecast and resorted to their own devices for production planning. That customer was unable to meet demand in a particular quarter resulting in a $20M miss in revenue – sales had actually sold what they said they were going to sell, but Operations didn’t believe the forecast and so didn’t build to that sales forecast.
Posted by: Nipul Chokshi | March 5, 2010 9:05 PM
Yeah, you are absolutely bang on….Even my experience working with Hi tech manufacturing companies says so…
• On your first point regarding Forecast accuracy, here is my view point. Forecast accuracy can be attained by having good process and solutions to enable robust mechanisms around (a) Historical Data Capturing (forecasts vs. actuals) (b) Collaborative Demand Forecasting with inputs from Field Sales Force, Customers, Production and Planning Managers (c) Forecasting algorithms and tools, including ability to see various slices and dices and permutations/combinations (d) BOM’s for Forecast explosions for various SKU’s. These features can be achieved using Oracle Demantara. Oracle Demantara Demand Planning (DP) calculates Forecast Accuracy at lowest level of data which can be reviewed at higher levels like planner (forecaster), item level, product category and so on. This gives ability to analyze and improve in areas which are less accurate as compared to others. Infosys developed solutions have enabled customers achieve more than what is offered in Demantara out of box by archiving the forecast in monthly frequency and by running forecast accuracy calculations on top of that data in aggregated buckets of short term (1-2 months), medium term (2-4 months) and long term ( 4-6 months) buckets. We have also developed algorithms to automatically correct the sales forecast based on historical bias in the forecast. For example if a particular customer forecast or planner is biased by 10% based on history, our algorithm is able to detect that and apply a correction of -10% automatically to the forecast.
• On your second point regarding collaboration between Sales and Operations, here is my view point. Best way for any manufacturing organization to overcome this issue of mistrust between operations and sales is to adhere to a strict and disciplined sales and operations planning (S&OP) process, whereby senior management reviews the sales and operations plans at regular intervals (not more than quarterly, although recommended is monthly). Enabling the S&OP process which is signed off by senior management ensures that there is one plan for the whole organization to follow and there is no confusion about the plan which needs to be executed. Demantra Sales and Operations Planning (S&OP) is an ideal tool for this process as it not only provides pre-configured metrics and worksheets to review the plans and their performance, it is also fully integrated with Demantara Demand Plannning and Oracle Rapid Planning which enables the planners to run simulations on the fly as demanded by senior management and show the plan results enabling a speedy decision making by the executive. At a weekly, monthly, tactical level, there should be a robust SCP function which takes feed from Demand Planning, S&OP Plans and based on supply sources and lead times, churns out timely and accurate Work Orders /Purchase Orders as the case may be….The SCP engine needs to flexible, responsive as well as comprehensive.
Posted by: abhishek_goyal | March 16, 2010 11:31 AM