Overview of the One-Click Supply Chain Model
Kunal Jetta, a demand planner and inventory controller, presents an integrated supply chain model that synchronizes demand planning, sales and operations planning (S&OP), and inventory control. This model is designed for manufacturers of perishable products such as juices, shakes, and yogurts distributed across multiple global regions including Africa, Middle East, North America, Asia Pacific, Australia, New Zealand, Europe, Indian subcontinent, and Latin America.
Demand Planning and Forecasting
- Demand-Driven Forecast: The forecast is based on actual orders sold during a specific period combined with end-of-month back orders that were unfulfilled, ensuring a realistic demand-driven approach.
- Forecasting Method: Utilizes exponential smoothing with an alpha value of 0.3 to generate statistically sound forecasts. For a deeper understanding of this technique, check out Mastering Linear Programming: A Step-by-Step Guide to Graphical Solutions.
- Multi-Level Analysis: Forecasts can be toggled between product level, region level, and product-location combinations for granular insights.
- Error Metrics: Absolute and squared errors are calculated to measure forecast accuracy.
Sales and Operations Planning (S&OP)
- Consumption Forecasts: Yearly consumption data, last month’s closing figures, and forecasted values are presented for each product-region combination.
- Forecast Adjustments: Sales and marketing teams can adjust forecasts based on market intelligence, upcoming orders, or promotional activities, with options to specify reasons such as regular targets, bulk orders, or institutional sales. For more on how these adjustments can impact overall strategy, see Understanding Linear Programming Problems in Decision Making.
- Impact Tracking: Changes in forecast quantities are tracked and updated in real-time to maintain transparency.
Inventory Control Integration
- Stock Levels: Inventory is monitored at SKU, regional, and supply point levels, including stock on hand and in transit.
- Stock Thresholds: Minimum (7 days), reorder (15 days), base/optimum (30 days), and maximum (45 days) stock levels are defined to balance availability and minimize expiry risks.
- Perishability Considerations: Given the products’ shelf life, the model prevents overstocking to reduce losses from expired goods. For insights on managing perishability in inventory, refer to A Quick Guide to Supply Chain Optimization.
- Replenishment Logic: Replenishment orders are triggered only when stock falls to or below the reorder level, ensuring efficient inventory turnover.
Additional Features
- Sales Performance Analysis: Identifies highest and lowest selling products and regions, along with their contribution to overall consumption.
- Supply Point Management: Tracks inventory across distribution centers, warehouses, clearing agents, and distributors.
- Hypothetical Data Support: The model is supported by sample sales and consumption data to validate the forecasting and inventory algorithms.
Summary
This integrated supply chain model leverages exponential smoothing for demand forecasting, incorporates real-time sales and operations adjustments, and tightly controls inventory levels to optimize stock for perishable products. It provides a robust, one-click dashboard for manufacturers to reduce stockouts, minimize expiry losses, and align supply with actual market demand across diverse global regions.
good evening I'm Kunal Jetta and by profession I am a demand planner for castor inventory controller and Sales
and Operations planning today I'm going to talk about a model which I have made that has synced demand planning sales
and operations and inventory controlling all together I call it the beginning of one-click supply chain here we are going
to discuss about a demand plan wherein the example used is of a manufacturer who sells juices shakes and yogurts
across Africa Middle East North America Asia Pacific Australia New Zealand Europe India Indian subcontinent South
America or Latin America these are also bifurcated and region level and finally we have a combination of the product at
the location so this forecast is basically derived from the demand that is the orders sold in the specific
period along with the end of the month back orders which were actually orders from customers but we were not able to
fulfill and execute them so we call it a demand driven forecast the total demand for the month of jan was 1015 and so on
and so forth the forecast is used derived through the exponential smoothing method where the alpha is 0.3
and the error the absolutes and the squared errors are derived mathematically
and I'm sure everyone is aware about this model this very well-known method we can toggle between the demand at all
the levels and even at the combination levels and this is the best demand derived out of the statistics for the
product at the region and at the combination of both and parallely we have also drawn the sales and operations
plan of the unique factors of product and the region showing the consumption for the entire year
the last closing month and its forecast and we have also derived the error the variance in percentage and the best
forecast of the beginning of the new month and this is a specific indicator provided or the specific option provided
for the sales and marketing team during the snop made they can using their expertise or you know schemes or any
orders which are in pipeline or any projects that they are going through they can increase or decrease the
quantity and the reason can be selected here appropriately to have a robust figure and this is how we are going to
do it for 370 there is no update so we can select the option of no change in forecast we have changed let's change it
here to 790 so there is an increase of 35 quantities where it updates that the forecast is changed and here you can
have a selection of why it is change regular target and then we have some exponential moment in this product for
example we make it directly almost twice of this number and says updated near we can have the remark
or whether it is a distributor bulk order or an institutional sale expected from a big tender so on and so forth
this is how we can update and have you know a one-click supply chain dashboard we also have the provision of finding
out who has been the highest seller across the period and who has been the lowest seller and what has been their
what has been their contribution in the consumption okay so product wise region wise and the
consumptions finally we also have synched the inventory controlling part along with the demand planning
forecasting and sales and operations where we have the unique combinations we have SQ level we have regional level and
we have supply point level stock and in transit and they are stock levels minimum carrying seven days talk reorder
carrying 15 days talk base or optimum level carrying the 30-day stock that is one month's coverage and maximum is one
and half month because the products that we are dealing with are perishable products they are expiry controlled
products and they do have shelf life so we should not be keeping too much of stock or as we know there are chances of
the stock getting close to expiry or expired and we will bear you know losses and consequences further that this
indicates the level of stock the monthly average the daily consumption of apple juice at Africa Middle East and the open
orders of apple juice at Africa Middle East and weather we actually need to replenish the quantity now this is a
very interesting part replenishment quantity can only be given if the stock goes below or equals to the reorder
level that's when I read or and I replenish the stock so here we have 69 which is then being reduced by 27 more
so whatever we have is less than 45 and that's why it is taking the stock back to base level and keeping the stock
level at base level it has to replenish the quantities by 48 numbers so that's how we derive on the inventory
controlling part at SK and regional level and even the supply points these are nothing but the distribution centers
or the the agencies or the supply points of this particular manufacturer maybe a warehouse or a clearing and forwarding
agent or cross consignment agent or a distributor and finally just to touch base a hypothetical sales data or
consumption data has been prepared to support this algorithm and also the end of the month back orders which is
basically a support to have the demand driven forecast so that's all today just to you know close this I would like to
specifically have a small synopsis on what the file talks about is a demand planning using the method of the
explanation of smoothing the forecast is demand driven @sq and regional level and the Alpha considered is 0.3 to begin the
forecast width and can be modified using the market trend the sales and operations where we have consumption
forecasts fir'aun's gap highest lowest contributors provision for updating new forecasts with selective reasoning is
available we have inventory control management of talk on hand plus in transit and this is basically the view
is provided at supply points with levels of inventory statistically calculated and having optimum coverage to reduce
sales risk and stock out situations the assumed product categories will be juices shakes yogurts
the regions will be Africa Middle East and the rest of ones are updated here and that's it from my end I hope you
like this project and thank you and have a good evening
Heads up!
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