Disruptions, including storms, port congestion, geopolitical shocks, demand spikes, and supplier failures, characterise the new reality. Strong supply chains do not make speculations; they feel, make decisions, and move swiftly. In the short course by Welingkar, we transform raw operational data into informed decisions, and thus, your network remains flexible without breaking and heals more quickly at a lower cost. The tone is pragmatic: fewer buzzwords, more actionable playbooks that you can bring home to your team.
We shift work from “chase the problem” to see it coming. You train to observe a few leading indicators, such as short-horizon forecast error, lane reliability, dwell time, supplier health, and activate playbooks at a young age. Your planners work days in advance, correcting purchase orders, slots, and routes to maintain service at the same level, rather than scrambling after stockouts and missed ETAs.
Both the demand and supply have signals: POS lifts, web traffic, promo calendars, weather notices, port queues, and strike notices. We demonstrate how to combine these streams into valuable nowcasts, such that procurement, production, and logistics change in time, before a blip turns into a break. The outcome is that there are fewer surprises, hand-offs are smoother, and fewer exception costs.
Not all safety stock is resilience. The ability to retain promises despite being hit, come back quickly, and learn to be hit with less force is what is called the ability to survive the shock effect. Each of the modules is one loop: Sense (get the truth), Decide (pick the lever), Execute (assign owners and actions), Learn (measure and improve). Making resilience a habit every week, this rhythm transforms it into a project.
Analytics exposes trade-offs: speed vs. cost, single-source quality vs. dual-source risk, service level vs. cash tied in stock. You will measure options on one page, so leaders do not argue about feelings; they give the go-ahead on options with well-defined impact on OTIF, lead time, and margin.
Traditional predictions are slow in a turbulent marketplace. You will combine history and quick signals: orders, point of sale, web traffic, offers, events, and even weather to make short-horizon nowcasts. Labs demonstrate how to smooth noises, find structural discontinuities, and re-weight inputs on a per-region or per-channel basis. The reward: faster production shifts, intelligent PO timing, reduced quantities of stockouts, and decreased outdated inventory.
Dashboards are important to the extent that they lead to action. You will construct a control-tower perspective around the critical few metrics: OTIF, ETA risk bands, dwell-time-by-node, lane reliability, and exception aging. We send alerts to owners, we establish escalation rules, and we create closed-loop root-cause notes rather than stacking them. Meetings are transformed into chart-browsing instead of exception-clearing.
Truck, container, and cold-chain sensor locations are streamed with location, temperature, shock, and door. This feed will be converted into early warning (such as temperature drift), realistic ETAs, and SLA compliance views. We also discuss limits, where additional sensors pay off, and where superior process on better data pays off, so you invest where the payoff is a fact.
A network sandbox is called a digital twin. You will plot important nodes (suppliers, plants, DCs, lanes), and run-what-if shocks: a port blockage, a supplier fire, a strike, or a sudden spike. Scenario runs assist in the selection among the rerouts, surge capacity, near-shoring, or temporary policy changes. You will determine the cost, lead time, and service of each choice and then create a one-page case for the move that you recommend.
The amount of inventory is not necessarily safe; the appropriate inventory at the appropriate place is. You will establish service goals by SKU-location by ABC-XYZ logic and compute safety stock based on demand and variation in lead-time, and redistribute buffers across plants, DCs, and stores. We discuss postponement (complete later, nearer to demand) and pooling (share buffers) to reduce total stock, although retaining promise.
Monthly S&OP tends to be inconsistent and opinionated. We base it on a small scorecard, accuracy of forecasts by horizon, bias, compliance with plans, capacity limits, and a few risk items. You are going to simulate: lock an actual demand plan, commit a practical supply plan, and grab the executive decision. Trade-offs are captured, owners and dates are identified, and the plan is made up and running.
The suppliers fail due to either financial or ESG, quality risks, or local risks. You will create a lightweight supplier scorecard (on-time, quality, financial health, ESG flags) and a tier-2 visibility map. We established dual sourcing limits, early-warning signals, and bridged the decisions to Scope 3 emissions. The higher the risk and the carbon are seen in the same light, the higher the resilience and sustainability.
All SKUs, customers, and lanes should not be promised equally. You will chart out cost-to-serve, design service levels, and matching inventory, transport, and cut-off policies to the levels. The high-value segments receive more reliability, and the system’s cost in general is lower due to policies that align with value.
You will create one-page visuals to be used by planners and leaders, utilising Power BI or Tableau (and Excel, where applicable). Both views contain their owners, targets, and next steps. We educate about labeling, filters, and pacing, which make meetings a decision and not an argument. The metric tree displayed behind the page is predictable, and assumptions are clearly made.
You will construct miniature models of safety stock, transport variability, surge capacity, and reroute cost. It is not fancy math, but an easy answer to the question: what happens when and what do we need to do differently? Models remain explainable to enable finance and operations to have confidence in and utilise them.
Insufficient data kills resilience. We discuss golden sources, standards of naming, late/dirty data processing, change logs, and Role-based access. You will exercise a light operating practice – who flags what, when, and how exceptions – that the single source of truth becomes really single.
The studios are practical building sessions in which the teams swapped sticky notes with prototypes that were working within the same room. Sprints conclude with a demonstration and a single measure relocated. Simulations enable you to experience the bullwhip effect and achieve control using cadence, buffers, and improved signals. You practice escalations, but not only models–who you call, what you change, and when.
You come away with living files, not screenshots: a control-tower dashboard, a MEIO worksheet with targets, a digital-twin scenario brief, a supplier-risk scorecard, and an S&OP scorecard template. These are plug-and-play assets that you can tailor to your situation next week.
The sessions are conducted either on the weekend or in a hybrid format. Labs are provided with sample data, which can be replaced with your own in the future. Faculty coaches help you refine the ‘so what’ to the point of shippable change, and maintain momentum without overloading calendars.
The details constitute resilience: we had already seen signs in the past, we had made the trade-offs more apparent, and we had found the cadences —the little policies that have stood the test of time. These concepts are brought to practice in the short course offered by Welingkar, utilising concrete dashboards, scenario models, MEIO targets, and digital twin briefs that you can apply on Monday. With a network that can perceive earlier and make decisions more quickly, promises are kept and expenses remain reasonable even in a noisy world.
No. We are concerned with decision-first analytics. You will gain access to useful dashboards, concise policy models, and straightforward governance procedures. Tools are maintained in an understandable way (Excel/Sheets, Power BI/Tableau, and basic SQL, when applicable) to enable planners, managers, and analysts to utilise them without requiring data science expertise.
Yes. You walk away with the working files- control tower page, MEIO worksheet, scenario brief, supplier threat scorecard, and S&OP scorecard that can be customized to your data and cadence in a few days. The course is structured in such a way that every artifact is a motivator of the real meeting and the real decision.