At a Glance

Analytics to Unlock Operational ValueService Offerings

#1Opportunities at the intersections of functionsProduct plant - market logistics decisionsChoice of raw materialAmidst pressures of daily operations, it is a challenge to identify and act on value enhancement opportunities at the intersection of functionsIllustrativeSUPPLIERSCUSTOMERS

#If you look at any manufacturing operation at a very simplistic level, it essentially buys some inputs, converts them into something else and then sells the finished goods. As the complexity increases, either because of increased scale or geographic dispersion, we create functional specialization. And as each function tries to improve its own working, we move towards local optima at the cost of organizational performance. Some common examples we come across very often are to do with input material procurement. Often, the technical specs of input materials can vary within a range, with a corresponding impact on purchase price. These changes in technical specs often have an impact on the production throughputs, efficiencies, recoveries or yields and therefore costs. It is not feasible for procurement people to fully understand the impact of every change of technical spec in the input material and evaluate which material gives the business the highest profitability after taking into account both purchase price and changes in yields and processing costs. This is where a mathematical model can capture the economics in its entirety and enhance profitability.

Similarly, there are opportunities when multiple products can be made at multiple plants, then deciding how much of what to make at each plant presents an opportunity. I will give examples of each of these on subsequent slides as I describe actual projects that have been undertaken2Typical opportunity at interface between Sales - OperationsGeneric Linear Programming ModelMaximize Contribution across all product-market-plant combinationsSubject toYields or recoveries achievable, based on production technology in use at the plantCapacity constraints for each relevant production facility and transportation modeDemand constraints for each marketGeneric model would need to be customized and detailed for each businessTypical business challenge: What basket of products to make to maximize contribution in a fluctuating marketFeedstock CompositionOutput CapabilityOutput Sale-abilityContributionRecovery RatiosCapacity ConstraintsDemand ConstraintsMarket PriceConversion CostsCopper Example

#The first example here is of a Copper smelting unit, where besides copper, sulphur oxides in the ore yield sulphuric acid as a by product which can be either sold or used to produce fertilizer. Since the relative profitability of each product can change on a day to day basis, the optimization model helps the unit head decide what mix of products to produce to maximize profits

3Typical opportunity at interface between Sales - Operations Outbound Logistics PlanningGeneric Linear Programming ModelMaximize Contribution across all product-market-plant combinations (taking into account all direct costs, including transportation costs)Subject toDemand constraints for each marketOrder shipment sizes for each customer (if relevant) and / or transportation modeCapacity constraints for each relevant production facility and transportation modeMinimum and maximum run lengths, if relevantMinimum and maximum load sizes, if relevantYields or recoveries achievable, based on production technology in use at the plantCould have added complexity of servicing customers from warehouses / transshipment pointsGeneric model would need to be customized and detailed for each businessTypical business challenge: What products to make at which plant and to ship to which market by what mode of transportation to maximize contribution

#Taking the Copper example further, in a multi plant situation, we could build in the demands at different geographies and enhance the model to define how much of what to make where, as well as what distribution mode should be used to maximize profit

4Typical opportunity at interface between Sales - Operations Inter Plant Logistics PlanningGeneric linear programming modelMaximize (Earnings Cost across various stages of processing) for all products across all plantsSubject toDemand being metCapacity being availableOther considerations (scrap consumption, material balancing, customer line certification / preferences etc.)Typical business challenge: What products to process to what stage of processing at each plant to maximize contribution across the businessMarket Demand & Conversion PremiumPlant CapabilitiesPossible Paths (for an order to be processed)Plant Capacities and CostsOther Considerations *Optimal Loading & ContributionAluminium Products Example

#Going to another project that is under way at a Aluminium FRP business. Here multiple products could be made at any of 5 plants. The plants were all different, so the throughputs, yields, costs etc at each plant for each product differed. So the operational challenge facing us was to decide what volume of each product to load on each plant to maximize profits, or RoI on investment in capacity. Given that demand was slightly in excess of capacity, this model also helped us define what orders to short fill or refuse. Once the model was in place, the same model can be used to evaluate capacity enhancement proposals, by comparing the profits that are possible with and without the enhanced capacity and computing the likely payback period. On another level, the model could be used in annual sales target setting by evaluating the optimal product mix that would maximize the profit, given the capacities available and market dynamics

5Typical opportunity at interface between Sales - Production Planning Operations ProcurementTypical business challenge: What feedstock maximizes total contribution across all products and by productsMaximize Contribution across all product-market-plant combinationsMax Contribution from a Feedstock : Linear Programming Model

Plot for multiple feedstocks

#This is something that we undertook at the Copper unit again, where different ores, or concentrates have different chemical and metallurgical compositions. And these need to be mixed in various proportions to ensure various ratios remain within certain ranges for technical or economic or safety reasons. So we looked at optimizing the blend to maximize throughput through the smelter. This is what I was referring to on the opening slide when I spoke about the choice of input material determining the cost of material in the finished product

6Typical opportunity at interface between Sales and ProductionTypical business challenge: Optimal production run length in the face of demand fluctuations and capacity constraintsPhosphates Production Planning ExampleGeneric linear programming modelMinimize plant idle capacitySubject toDemand being metCapacity constraintsInventory build up being within normsBased onCapacity requirement of different shared resources (throughputs) of different products

#At the Chemicals plant in Thailand, we used Linear Programming to develop a production plan. Here we had multiple products all contending for common resources such as the kiln or spray drier. So the challenge was to determine what volume of each product to take up so as to maximize demand fulfillment and minimize plant idle capacity

7Typical opportunity at interface between Outbound Logistics Planning and SalesTypical business challenge: Optimal FG inventory to be held at each node of the distribution networkWhat-if Simulation Model(s) combining human judgment with mathematical modeling capabilities

#This is something I have not yet done in the field, but it would be entirely possible to develop a model to evaluate how much FG should be held at various stock points. The idea being to simulate changes in demand and use this simulation to determine optimal inventory holding patterns at various points in the distribution network

8Typical opportunity at interface between Operations and Technical Support ServicesGiven captive power plant capacity > demand, how to balance load to minimize cost of power consumedMinimize cost of power consumed across captive generation and gridSubject toSteam (at different pressures) + Power demand being metCapacity constraintsBased on Calorific value of different types of coalBoiler efficienciesTurbine productivityTypical business challenge: What mix of coal to use in which boilers to run which turbines for how long to generate required quantum of electricity at lowest cost

# At the Copper unit again, we had a captive power plant. We had different kinds of coal that could be used with each of 4 different boilers to generate steam, which would then be delivered to one of 5 turbines to generate electricity. So the optimization model sought to minimize the cost of electricity consumed by defining how much of which coal to use in each boiler, as well as for how many hours each boiler and turbine should run. The grid power aspect was also incorporated so that any excess or shortfall could be sent or drawn from the grid. That is why the objective was defined as minimizing the cost of power consumed rather than minimizing the cost of power generated

9Typical opportunity between Procurement and CommercialTypical business challenge: Whether to stock up or liquidate stocks of a highly price volatile input materialPhosphoric Acid Price Prediction ExamplePredicting the price of Phosphoric Acid (commodity common raw material) based on 2 different PA production technologiesRegression analysis to develop correlationPrediction model to forecast pricesUsed monthly to decide whether to stock up or liquidate stocks of PA, based on whether prices are forecast to move up or down

#All the models Ive shown till now have been linear programming optimization models. This one, which we did at the Chemicals plant at Thailand was a regression model used to predict the price of Phosphoric Acid. This is a key input material for them and the price fluctuates greatly. So by predicting an upward or downward trend, they can quickly decide whether to stock up or liquidate PA stocks. To so this, we studied the methods by which PA is produced and tracked the prices of those inputs materials to develop a price prediction model. If you look at the data, the price hovered around 700 USD at the time and our predicted prices were coming within about 10 $ of the actual. More importantly, the model is forecasting the trends correctly

10Typical opportunity at interface between Spares Procurement and Maintenance / RM Procurement and Production Planning Typical business challenge: Optimum inventory holding, without exposing plant to stock outsDetermine inventory holding and procurement policies for each combinationDetermine reorder levels and reorder quantities by analyzing consumption patterns, economic order quantities, lead time for delivery by suppliers, buffer stock requirements to offset supply uncertainties

#This is not a mathematical model, but an approach to help us define inventory holding norms. It is more useful for spares and consumables inventory policy making than RM-PM. Typically, when inventory categorization is done for accounting and valuation purposes, an ABC or XYZ basis is used. Whereas from a plant operations point of view, the VEA or vital, essential, auxiliary categorization is more relevant. So to decide spares and consumables inventory holding policies, we need to categorize inventory by multiple methods and use a combination to define the holding policy. So for example, if there is a high value engine that is required to be held, it would be a low consumption but high value and vital item. In this situation, it would be best to hold a single piece in a single location and airlift it to wherever it is required when there is a need. Whereas a vital but C and Z category item can be held at each plant. Policies relating to usability verification would also need to be put in place theres no point carrying a spare gasket and discovering it is not usable because it has been lying around forever

E.g. All A category items would be procured centrally, held locally; CXV / CYV category items (e.g. a vital spare part which may be of occasional requirement for breakdown maintenance and of high or medium value) : May be held in small quantity (not availing of EOQ advantages) at one of multiple plants which could require it, from where it may be express couriered to another plant, if and when it is required, and replenished when consumed; CZV would be procured and held locally, unless it deteriorates with age, in which case it would be held centrally

11Typical opportunity across Procurement-Production-Sales-DistributionTypical business challenge: How to minimize the cash to cash cycleStudy of supply chain process for issue identification and redesign opportunities (e.g. functional overlays highlight conflicting functional objectives, rework loops highlight process deficiencies etc.) to reduce total cycle time

SUPPLIERPay for RM, PMPurchase RM, PMReceive RM, PMStore RM, PMIssue RM, PMProducePPCManage WIPDeliver to W/HPrep del. doc.ShipInvoiceCUSTOMERW/HCollect ordersTest qualityIllustrative Supply Chain Process MapNote: Flow depiction, resources, controls removed for simplificationProcurement FunctionStores FunctionProduction FunctionStores and Logistics FunctionAccountsSalesCollect outstandings

#End to end supply chain alignment is a core offering. I have been working in this area for over 15 years now. Essentially the focus of work here is to reduce the cash to cash cycle, enhance OTIF levels, reduce inventories etc by analyzing the supply chain in its entirety rather than viewing it through any functional lens. This is typical process oriented work

12Typical opportunity across Procurement-Production-Sales-DistributionTypical business challenge: How to minimize the cash to cash cycle

Spinning & Weaving Unit Example

Identification of critical supply chain processes and their business imperativesAnalysis of current state and redesign to address business imperativesIdentification of projects, responsibility allocation and program implementation and governance

#And this is what I am working on at a spinning and weaving mill. Here, various improvement initiatives have been identified and are being worked on. The enhanced targets are being incorporated into their 2013-14 budgets by the implementing team itself, so there is a certain confidence that the benefits will be realized

13Summary of Potential Service OfferingsOperational decision support systemProduct mix decisionsProduction planning for single plant, multi-plantTransportation logistics planning (mode and route)Procurement timing (stock up or liquidate based on price forecasts)Feedstock blend composition / procurementCaptive power generation modalityStores and spares inventory holding policiesCash to cash process designStrategic decision support systemCapacity planning given a product mix and demand estimateProduct mix planning given a capacity and pricing-costing estimateDistribution network planning

#14Illustrative Tools Used in Such Model BuildingOperations Research ModelsLinear Programming ModelsGoal Programming ModelsAnalytical ToolsRegression AnalysisTrend Analysis / Curve FitmentProcess mapping and analysis

#Thank you

Contact details : Ajita Kini : Cellphone +91 98195 99412; ajita.kini@ajitakini.net

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